The role of the surgeon in quality cancer care

The Role of the Surgeon in Quality Cancer Care Introduction Cancer remains 1 of the major causes of premature death in North America. There have been dramatic advances in cancer treatment in recent decades, and it is expected that the coming years will yield further improvements in cancer outcomes. The Unites States of America and Canada are at the forefront of this revolution in oncology; as a result, North Americans have available to them the finest and best tools for the screening, diagnosis, and treatment of cancer. Many of the major advances in the science and treatment of cancer have resulted from the work of surgeons. Giants in our field from Halstead and Billroth to current figures such as Fisher, Rosenberg, Folkman, and Wells have pioneered modern cancer science through the application of the scientific method directly to the problems faced by their personal patients. Surgeons also play a leading role in providing cancer care at critical junctures throughout the treatment process. Surgeons are generally the first contact in the medical system for patients with cancer. They establish and coordinate the multi-disciplinary cancer care team and provide what, in many cases, is the most important component of cancer care. It was surgeons in America who first recognized the need to coordinate and evaluate the quality of cancer care. In the 1920s, 50 years before the National Cancer Institute (NCI) instituted its cancer care evaluation program, the American College of Surgeons (ACoS) established the program that matured into today’s Commission on Cancer (CoC). This is the first and only national program that sets standards for cancer programs at community hospitals. This program included the first community-based cancer registries to collect and analyze information on the demographics of cancer patients and patterns of cancer care. Although originally maintained in paper form for internal hospital use, more recently, the cancer registry data were computerized, and in the late 1980s, the registry data from the CoC-approved programs were aggregated into the National Curr Probl Surg 2003;40:511-90. 0011-3840/2003/$30.00 ⫹ 0 doi:10.1016/S0011-3840(03)00051-0

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Cancer Data Base (NCDB).1 The NCDB now contains staging, treatment, and follow-up data on more than 12 million cancer patients and includes approximately 70% of all new cancer cases diagnosed in the United States each year.2 Despite these efforts and successes, there is clear evidence that many North Americans do not receive high-quality cancer care.3 Well-documented variation in cancer care affects outcomes ranging from quality of life, satisfaction with care, and cost to cancer recurrence and survival rates. Although surgeons have a heritage of leadership in defining and providing quality cancer care, there is concern that surgeons are among the culprits who contribute to the compromise of those very standards. There is also increasing frustration with the failure of physicians to tackle the difficult problems and enact meaningful changes that improve quality.4,5 This frustration was stated clearly in a recent editorial in the New York Times: “With the evidence growing ever stronger that medical errors are a danger to many patients, it is disturbing to find such retrograde attitudes among physicians. Reform can succeed only if the medical profession gets behind changes that expert groups and plain common sense suggest could significantly reduce the harm caused by medical errors.”6

There are quality problems associated with all aspects of treatment, including diagnosis, operation, use of adjuvant therapy, follow-up surveillance, and end-of-life care. Factors known to be associated with variations in cancer care and outcomes include characteristics of the surgeon and other providers, the patient, the institution, and the health system, including mechanisms of payment. The most compelling data relate surgeon and hospital case volume with patient survival rates in high-risk cancer operations. There are also data that suggest an association between surgical case volume and long-term outcome for patients with low-risk, high-incidence cancers, such as breast and colon cancer. In both situations, a large fraction of such operations in the United States are performed at hospitals and by surgeons with very low case volumes. There are also substantial data that link surgeon training and specialization to patient outcome. For several reasons, surgery is the phase of cancer care most extensively examined for quality variation. This is due, in part, to the importance of surgical care and the finding of outcomes associated with surgeon and hospital factors. The intensive scrutiny of surgical cases may also be due to the relative ease of collecting data on hospital-based cases. Evaluating other phases of cancer care is hampered by logistical and legal barriers to the collection of data in the ambulatory setting, often administered by 512

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independent providers who have no formal affiliation with a cancer registry. Recognition of these problems led the Institute of Medicine (IOM) to undertake a formal review of the status of quality of cancer care in America. This culminated in the publication of 3 reports that called for major changes in the structure of the cancer care delivery system.3,7,8 Major changes were suggested in the organization, structure, financing, and legal underpinnings of the healthcare systems.8 Potential solutions at the provider and provider group level to impact on quality include implementation of practice guidelines, feedback of treatment data to providers and institutions for internal improvement, academic detailing to outlier providers, direct feedback of data to the public, legislated changes in practice, and accountability (including punitive measures). For certain high-risk procedures, regionalization to institutions and surgeons who can maintain and document proficiency has been advocated. Public reporting of data and regionalization are not new to the surgical community, having already occurred with trauma and cardiac surgical care in many states. This includes reporting of institution- and physician-specific risk-adjusted treatment mortality rates. For cancer care, a large employer consortium, the Leapfrog Group, recently established a program to regionalize treatment for their employees for certain high-risk vascular and oncologic procedures, which bypass further research into these issues.9 Undoubtedly, new and innovative solutions are essential to improve cancer care. For some situations, administrative changes that include regionalization to high-volume centers may be meritorious. For other cancers, different solutions may be more appropriate. Quality evaluation and enhancement are complex and evolving concepts, which require the input of experts from an array of fields. In addition, because of their impact on society, quality initiatives must include all affected and interested parties, ranging from health services researchers, health policy experts, and healthcare institutions, to employers, payers, government, and the public. However, it is physicians and surgeons, not corporate executives, who are most qualified to identify and implement comprehensive programs for quality enhancement.10 Unfortunately, physicians have become cynical and frustrated with quality improvement efforts that are perceived as punitive or a guise for cost containment.11 Furthermore, as quality improvement matures into a scientific field of study unto itself, physicians are often lost in the language and methodologies of population scientists and health services researchers. However, “. . . such reactions are a luxury that physicians can no longer afford.”11 It is therefore incumbent on surgeons to demonstrate Curr Probl Surg, September 2003

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leadership once again by engaging in collaborative efforts to identify meaningful and workable solutions to reduce variation and enhance the quality of cancer care for all Americans. Quality improvement will require a culture change for all involved participants of the healthcare system in an environment open to change and innovation. Physicians must recognize that the status quo is not acceptable and employ foresight and imagination to operationalize new paradigms for care. They should welcome new sources of information and fresh strategies that will help them improve the care they provide. Institutions, payers, government, and the public must work with providers constructively and resist the temptation to use quality assessment to assign blame for past problems. Society must recognize and address financial and legal barriers to change, including meaningful protections against liability for providers who participate in quality enhancement initiatives.8 Based on the IOM reports and Congressional mandates, major organizations are identifying national goals for quality improvement and defining core quality measures.12,13 Membership in these groups is often weighted toward regulatory bodies and health services researchers, not clinicians. They will need the expertise of practicing surgeons and other clinicians to define quality issues and implement change. Efforts currently underway will shape quality enhancement initiatives for the next decade. If surgeons are to have an impact on the quality improvement movement, they must act now. This monograph provides an overview of the complex issues of quality evaluation and quality improvement for the practicing clinician. The first section defines quality and examines the means available to measure quality. This section includes a discussion of existing and proposed measures of quality and how such measures are chosen and validated. In addition, this section reviews the mechanisms by which cancer care data are collected in the United States. The next section summarizes the evidence that suggests a variable quality of care for patients with cancers associated with high-risk surgical procedures and for those with common cancers with low surgical-related morbidity. Factors that correlate with outcome, such as characteristics of the healthcare system, healthcare delivery, patients and providers, are addressed in detail. Specific attention is directed to the literature that examines the impact of surgeon and hospital case volume and surgeon training as factors that affect quality. The next section reviews the types of interventions that may address quality problems. Finally, we review recommendations for quality enhancement and the crucial roles surgeons may play in affecting change. 514

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Defining and Measuring Quality Defining Quality Cancer affects more than 1 million Americans, results in the deaths of nearly 500,000 individuals, and costs in excess of $50 billion a year. There is increasing recognition that the quality of cancer care varies widely. The cost in terms of preventable or untimely death, suffering from unnecessary morbidity, and the loss of organ function is difficult to quantify. However, variation in cancer treatment, including delay in diagnosis, inappropriate treatment, or poorly applied treatment, certainly results in the deaths of thousands annually in the United States.14,15 Estimates of the extent of inappropriate use of medical services suggest that as many as 30% of patients receive care that is contraindicated.16 Although there have been attempts to address the quality of cancer care (eg, the efforts of the American College of Surgeons), variation in the quality of care persists. There is an increasing awareness of this problem among those outside the medical field, and calls for major changes in the healthcare delivery system are on the rise. These concerns resulted in the publication of a 1999 monograph by the IOM National Cancer Policy Board entitled (NCPB) “Ensuring Quality Cancer Care.”3 This report contained an exhaustive review of the factors that surround the quality of cancer care. Ultimately, it was highly critical of the American cancer care system, making the statement “The NCPB has concluded that for many Americans with cancer, there is a wide gulf between what could be construed as the ideal and the reality of their experience with cancer care.” The NCPB made 10 broad recommendations to address the quality of cancer care. Among these is a call to regionalize to high-volume centers those procedures that require high-level technical expertise and that are associated with high treatment-related morbidity and mortality rates. Furthermore, the NCPB addressed the need for ongoing clinical research and full access to care for those who are uninsured or underinsured. Most importantly, the NCPB called for the establishment of quality standards, quality measures, and a national data system to support this measurement. The NCPB report alerted much of the cancer community and the public to these issues, which led to Congressional hearings and proposals for federal legislation (eg, the Senate Kennedy-Frist bill of 2002, S.2965). This report was followed by 3 additional monographs from the IOM that outlined the need for data systems to evaluate the quality of cancer care, that outlined a framework for enhancing quality, and that defined structural changes in the healthcare system necessary to assure quality Curr Probl Surg, September 2003

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improvement.7,8,17 Others in the cancer care community responded by establishing major quality initiatives to determine the best methods to define and enhance cancer treatment services. These initiatives were organized by the NCI, the ACoS CoC, the American Society of Clinical Oncology (ASCO), the National Comprehensive Cancer Network (NCCN), the American Cancer Society (ACS), the Agency for Healthcare Research and Quality (AHRQ), the RAND Corporation, the National Quality Forum (NQF), the National Committee on Quality Assurance (NCQA), the Robert Wood Johnson Foundation (RWJ), and others. In addition to establishing independent programs, heightened awareness of the quality gap has motivated many of these agencies to collaborate, with the goal of developing unified approaches. For example, the CoC is collaborating with investigators at ASCO, Harvard University, and the RAND Corporation in a study that examines the quality of care for patients with breast and colon cancer in 5 metropolitan areas in the United States. The study is evaluating the quality of care in a single timeframe from both the medical and the patient’s perspectives by using cases identified from the NCDB. The NCI is collaborating with the NQF and RAND to develop a core set of quality measures for cancer care. Cancer surveillance organizations have established ongoing collaborative groups to ensure coordination of data collection for cancer surveillance and quality assurance.18 One desired goal in this effort is to standardize the data collection elements and establish a coordinated system for documenting cancer staging information. This approach would eliminate the duplication or triplication of data collection and improve the overall quality of data.18,19 Quality problems in care occur in the choice of appropriate treatment and in the technical application of that treatment. Quality problems in health care, and cancer care in particular, result from underuse, overuse, or misuse of accepted treatments.20 Although physicians recognize quality cancer care when they see it, defining quality in practice is difficult. Improving quality requires a functional definition, measurement tools, and mechanisms to collect data to apply these tools.

What Is Quality? The most widely accepted definition of quality comes from the IOM: “Quality of care is the degree to which health services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge.”21 This definition implicitly acknowledges the complex interaction of factors that lead to quality. First, it recognizes that quality is important in a broad array of services, 516

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ranging from highly technical treatment to screening, preventive, and mental health services. Second, it promotes the concept of personal autonomy and differences in the needs and preferences of individuals, stating that care must achieve the outcomes desired by the individual and that care must be provided in a sensitive and informative manner.22 Third is the concept that defining quality medical care is not limited to the individual but must also address the impact of care and the healthcare needs of the entire population. Furthermore, this statement suggests that desired healthcare results include outcomes other than survival. Although ultimately the most important outcomes in cancer care are survival and cancer recurrence, serious considerations may also include organ preservation, quality of life, satisfaction with care and cost. Finally, this definition of quality acknowledges that the standard of treatment is continuously changing as our knowledge and experience with cancer care expands.20 Different constituencies have varying perspectives on what constitutes quality of care.11 Providers generally interpret quality in terms of technical success and appropriateness of outcomes for individuals. Patients or consumers of health services add to this concept the need for autonomy, satisfaction with care, and the way services are delivered. Payers and purchasers of care add the dimensions of population outcomes and cost. Although these views complement the perceptions of the providers and consumers, there is the potential for conflict between objectives and perceptions of quality. In cancer care, this is often manifested by an adversarial relationship between patients and payers, especially with respect to highly technical or investigational treatments. In the last 2 decades, there have been numerous examples of managed care organizations denying access to potentially expensive treatments on the premise that they have limited or unproven value. At the same time, it may be argued that many real quality improvements are contingent on the power of payers to affect change. In assessing quality, the validity of these differing perspectives and goals of stakeholders must be acknowledged openly.

Measures of Quality Measurements of care must address all dimensions of quality. To the pragmatic physician, the most important measure of quality is determining whether specific outcome goals have been achieved. However, attaining these goals is not simply the product of interaction between the provider and the individual patient. Factors including the structure of the Curr Probl Surg, September 2003

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health system affect these interactions and must be assessed when defining quality of care.22 Structure refers to how the characteristics of the healthcare system and providers, including the organization, comprehensiveness, financing, and accessibility, affect the quality of care.23 Differences are related to a system that provides a wide range of organization models of healthcare delivery, limits access to care for those without health insurance, and does not fully accommodate cultural differences between ethnic groups. In cancer care, this is exemplified by differences in the stage distribution at presentation and treatment of cancer for different ethnic and socioeconomic groups.24 The second dimension of quality assessment is outcome. The primary outcome measures in cancer care are death and cancer recurrence. However, it may be more useful to use other outcome or quality measures. The waiting time required to capture meaningful information on survival and recurrence makes these measures insensitive and impractical. During this time, the structure of the health system, the involved providers, and the standards of care may have undergone considerable transformation. In addition, survival and recurrence measures may be insensitive to quality because they are often affected by the natural history of the disease, patient health status, and other influences outside the control of physicians. Other outcomes that may be measured in cancer care include tumor response, absence of symptoms, and quality of life.25 However, even these outcome measures may be relatively insensitive to quality.22 Another approach is to measure quality in the context of processes of care. Processes of care relate to the subjective and objective experiences of patients.23 Examples of process measures include the proportion of patients who receive a given service, such as mammography, rectalsparing surgery, or adjuvant therapy. The strength of process measures is the ability to examine specific aspects of care in a functional timeframe. Process measures are likely to be used widely to assess quality of care. However, individual process measures may have limitations similar to those of outcome measures, in that they may be insensitive to quality; may be related to status of the patient, stage of cancer at presentation, or natural history of disease; or otherwise may not be controlled by the provider or health system. The use of process measures for quality assessment is based on the assumption that the measurable processes define the overall quality of care. The utility of process measures in predicting outcome is dependent on the strength of the evidence supporting the specific practice and on the availability of data to assess these 518

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measures, controlling for patient and disease factors. Unfortunately, this assumption, however logical, may not be true. For most process measures, there is little clear evidence of a link between adherence to a process measure and outcome. Process measures should address all aspects of cancer care, from screening and prevention through all phases of treatment (eg, diagnosis, surgery, radiation, and systemic therapy), to surveillance, treatment of recurrent disease, palliative care, and terminal care. A myriad of process measures that cover all aspects of care have been proposed. For example, in breast cancer, measures may include the proportion of women undergoing mammography, the use of breast-conserving surgery (BCS), the fraction receiving appropriate radiation and systemic therapy etc. It is obvious that defining and employing all these measures would be difficult. Problems with data collection, clinical consensus, and the need for comprehensiveness have stymied development and application of process measures. To date, the only process measures applied for the population at large are the Health Plan Employer Data and Information Set (HEDIS). First implemented in 1989, HEDIS standards are used for the evaluation and certification of managed care organizations. Application of cancer care measures is limited by the issues addressed above and, on a large scale, the inability to accurately identify individuals with cancer, the stage of cancer, and the treatment administered. For this reason, HEDIS measures only 1 cancer benchmark—the percentage of women older than 50 years who have mammograms at least every 2 years. However important the use of mammography, this standard is arguably a poor and insensitive assessment of the quality of breast cancer care.

Identifying and Validating Quality Measures The NCPB in its 1999 report advised that the nation develop and monitor a core set of quality measures.3 For each type of care, there may be dozens of potential measures of the process and outcome of care. However, because of the limitations of clinical evidence and consensus and in resources to measure and utilize such data, it will be necessary to establish a more limited set of measures. Several groups are working independently and in concert toward this goal. Most notable is the effort of the NCI to formulate core measures of cancer care in conjunction with the NQF and the RAND Corporation.12 There are no universally accepted standards for the assessment and validation of quality measures. Mandelblatt and colleagues summarized Curr Probl Surg, September 2003

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TABLE 1. National committee on quality assurance. Attributes of quality measures for cancer care. 1. Measure must capture a condition or aspect of care with a significant burden on the target population. 2. The condition or process must be sufficiently common to provide stable estimates and statistical power. 3. The indicator must be clinically relevant. 4. The process or condition should be amenable to change by providers, patients or health systems to provide real improvement in the quality of care. 5. There should be sufficient evidence supporting the relationship between the process/ indicator, the use of care, and outcome. 6. The indicator should be clearly defined and free of confounding and non-measurable elements. 7. The process of measurement must be acceptable to those involved (providers, patients, health systems) and the cost of measurement must be practical.

the attributes of useful quality measures as outlined by the NCQA (Table 1).23 Defining quality measures that meet these criteria requires a rigorous methodology for proposing and testing the putative measure. One formal mechanism for developing oncology quality measures was piloted by the RAND Corporation.12,13,26 The tenets of their methodology are that measures be evidence based, address meaningful aspects of quality, and in aggregate address all dimensions of quality. Measures should be developed with a clearly intended application, assist providers in improving care, and not place undue burden on providers who collect data. They should help patients, payers, and other consumers make informed choices. Quality measures must be revised over time, based on feedback from users and changes in practice, and must be selected by an evidence-based and formal consensus process. Using this methodology, the RAND Corporation recently developed a set of cancer care measures.26 From among 46 clinical areas, they selected 11 disease sites in cancer. For each cancer site they provided approximately 10 quality indicators encompassing screening, diagnosis, treatment, and follow-up surveillance. To identify quality measures, the RAND group convened expert panels for each disease site. After defining what were considered reasonable measures based on evidence, it was determined whether application was feasible in practice. Each measure was applied by panelists in their own practices, and the results were reviewed by the panel to select final measures. The quality measures defined by this process are limited in scope and are largely subjective. Applying these measures will require access to 520

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outpatient medical records and will likely suffer from failure to identify a substantial fraction of patients subject to each measure. For breast cancer, the task force identified 10 quality measures. Four address the evaluation of patients with breast masses, including the determination of whether a patient had a timely follow-up examination or biopsy. Another measure is whether a woman with stage I or II breast cancer was offered a choice between mastectomy and BCS. Three of the measures address the frequency of mammogram and physical examination in follow-up surveillance. Only 2 measures address key processes of breast cancer treatment: the use and timing of radiation after BCS and the use of adjuvant systemic therapy in postmenopausal women with node-positive breast cancer. Even these measures do not address the care provided to a large fraction of breast cancer patients, such as the use of adjuvant systemic therapy in node-negative or premenopausal patients. The utility of these measures will therefore depend in part on the assumption that good care in the population studied (eg, adjuvant therapy among postmenopausal women with positive nodes) indicates good care in other groups (eg, adjuvant therapy among premenopausal women and in women with negative nodes). The other notable project to identify core quality indicators for oncology is the CanQUAL project of the NCI. Working with the NQF and the RAND Corporation, this group has identified 8 major areas in oncology care for which they will develop measures. The areas include 4 disease sites (breast, colon, lung, and prostate) and aspects of care that cut across disease sites, such as pain management and palliative care. This work is in progress and will likely be reported in 2003.

Practice Guidelines for Quality Measurement Practice guidelines are tools to assist patients and providers in making informed decisions regarding the treatment of specific conditions.27,28 Practice guidelines are generally developed by teams of experts from several professional disciplines to reflect the best current standard of care. Whenever possible, guidelines are based on high-level, clinical trial evidence. However, in oncology and other areas of medicine, there is not sufficient high-level, clinical trial evidence for all phases of care. Therefore, most guidelines are based to some extent on expert consensus. Other key factors in practice guideline development are the need for periodic review, updating, and feedback from users for incorporating revisions.29 Unfortunately, many practice guidelines do not meet these standards.30 The most comprehensive practice guidelines in oncology are Curr Probl Surg, September 2003

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the Canadian cancer practice guidelines and the oncology practice guidelines of the NCCN (available at www.nccn.org).31,32 Although not originally intended for use in quality measurement, practice guidelines may provide functional and practical measures of quality. For each aspect of care for a specific cancer, a decision point in the guideline can be identified (eg, decision point of adjuvant chemotherapy for a patient with node-positive colon cancer or for radiation after mastectomy with 4 or more positive nodes). Concordance with the guideline recommendation at each decision point may be used as a quality measure. This would provide quality measures at each phase of care for each cancer patient (eg, staging evaluation, surgery, radiation, systemic therapy, and follow-up surveillance). There are limited data that demonstrate the utility of applying practice guidelines for quality benchmarking, however.33

The Cancer Data Collection System Overview. Evaluating quality requires 4 major elements: identifying patients with cancer, data sources, data collection strategies, and quality of care measures.34 In the ideal world, the choice of quality measures should dictate the type of information that is collected. However, in the pragmatic world, it may be necessary to tailor quality measures to the available data. This section summarizes the systems by which cancer surveillance and outcome data are collected in the United States and the strengths and limitations of these systems. Cancer care data collection should encompass all patients, providers, and practice settings in the community. Historically, cancer care data collection has been hospital based and centered at larger hospitals with the resources and commitment to collecting data. Therefore, the most complete data are available for hospital-based care, centered at large or high-volume hospitals. A steadily increasing fraction of care is administered in ambulatory settings, however. For such cancers as melanoma and breast, all care including surgery may be delivered outside the hospital setting. Therefore, a hospital-based cancer registry program may not capture these cases. There is clear evidence that the outcome of cancer care is sometimes related to the volume of cases treated at a hospital. Small hospitals (and practitioners affiliated with small hospitals) may be the situation where care is most likely to be compromised. These are also the least likely to have the resources to maintain a cancer registry and participate in quality evaluation. Focusing data collection on hospitalbased care and at higher-volume institutions or practices may specifically miss patients whose care is most at risk of compromised quality. 522

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To measure quality adequately, the ideal data collection system would contain information on the entire population, including specifics about co-morbidity, cancer staging, cancer diagnostic and treatment modalities, recurrence, quality of life, and survival.17 The system would operate in real-time, thereby providing support to patients undergoing cancer care and allowing close tracking of care to assure that treatment meets accepted standards. The system would provide mechanisms to use the information for constructive quality improvement, including feedback to providers, hospitals, healthcare organizations, healthcare consumers, and the public. The existing systems for collecting cancer treatment data in the United States are not designed to assess community-wide quality of care. There are 2 major systems: population and hospital-based registries. The primary objective of population registries is cancer incidence surveillance. These programs collect limited treatment information. Hospitalbased systems provide more detailed treatment information, but do not capture a large fraction of the care administered outside the hospital. In addition, the majority of hospitals in the United States do not maintain a cancer registry. The major population-based, hospital, and health system registry and data collection programs in the United States are outlined below and are summarized in Table 2. Population Registries. The primary aim of population-based registries is to define the incidence and stage at presentation of cancer for the study population. These registries capture at least 90% of cases in the population and provide critical information on trends in incidence, stage at presentation, and survival. However, they generally contain limited information on treatment. There are 2 major population-based cancer surveillance systems in the United States: the state cancer registry program, coordinated and funded in part through the National Program for Cancer Registries (NPCR) of the Center for Disease Control and Prevention (CDC), and the Surveillance, Epidemiology, and End Results Program (SEER) of the NCI. State Cancer Registries. Cancer is a reportable disease, and data are maintained in a central registry in each state. The major objective of most state cancer registries is to define the burden of cancer on the state’s population. Cancer cases are identified by collecting reports form hospitals, pathology laboratories, and physicians regarding individuals with cancer. Records from multiple reporting entities on the same person are combined into a single case record.35 This process is complex and is complicated by problems such as missing unique identifiers (eg, Social Security number), errors in identifiers (eg, birth date or address), and Curr Probl Surg, September 2003

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TABLE 2. National programs for cancer data collection Population registries NPCR/State cancer registries

NCI/SEER

Objective

Population cancer surveillance

Population cancer surveillance

Cases included

All cancers in U.S. via state cancer registries

All cases in SEER regions (25% of U.S. population)

Staging system

Varies; summary stage in some states, TNM in others Required reporting from institutions and providers 2-3 years after treatment

EOD, may be converted to TNM in most cases Required reporting and locally contracted cancer registrars 2-3 years after treatment

Available to public without identifiers

Aggregate data available to public; data linked to Medicare claims available to researchers ⬎95% of all cancers in region; first course treatment data; linkage to other data sets Only 25% of population; limited treatment data $27 million

Data source Time to data available Data availability

Strengths

⬎90% of all cancers in state

Weaknesses

Delayed data availability; limited treatment data

Cost

NPCR, National Program for Cancer Registries; SEER, Surveillance, Epidemiology, and End Results Program; EOD, extent of disease; NCI, National Cancer Institute; NCCN, National Comprehensive Cancer Network.

differences among reporting institutions for dates of diagnosis, dates of procedures, and diagnosis codes. The process of collecting and matching the data is time consuming. The current standard is for state registries to complete the process within 2 years of diagnosis. Final data are not available in most states until 3 years after diagnosis. State cancer registries attempt to identify at least 90% of incident cancer cases in a timely and accurate manner. The quality of data varies among registries, and many state registries do not meet standards for timeliness, accuracy, and completeness.17 Factors that affect the quality of state registry data include the resources available to the registry and reporting entities and the compliance with reporting requirements by providers, pathology laboratories, hospitals, and healthcare organizations. Although cancer is a reportable disease in all states, most do not have the resources to enforce compliance. Many physicians do not report cases on the assumption that other entities are 524

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TABLE 2. Continued Hospital-based NCCN

ACoS NCDB Hospital-based quality assurance; national practice patterns of care Patients treated at hospitals with ACoSapproved programs (25% of U.S. hospitals, 70% of cancers) TNM

System-wide quality of care; supports clinical and translational research Patients with breast cancer and nonHodgkin’s lymphoma at NCCN institutions

Hospital record review; physician staging, some ambulatory data At hospital: 6-8 months At NCDB: 2 years

Hospital record review, communications with outside providers 6 months after diagnosis

Hospital data used locally; NCDB data available on application to NCDB

Not available to public or researchers outside NCCN

Extent of treatment information at hospital; follow-up for outcome

Detailed treatment and co-morbidity data; potential for linkage to genomic information NCCN member institutions only; limited disease sites Not available

Not population based; variable data quality; ambulatory data limited $2 million at ACoS; $50 million plus inkind support at hospitals

TNM

reporting the same information. This may result in failure to capture some cases, particularly those treated solely in the ambulatory setting. The utility of state cancer registry data for the evaluation of quality is limited. In many state registries, the stage of cancer is codified using the Summary Stage system, in which the stage is coded as local, regional, or distant. Use of the simpler Summary Stage is necessary because reporting entities such as smaller hospitals lack the resources to employ a certified registrar to collect and interpret the data necessary to report TNM stage. In addition, collection of treatment information in state registries was not included in the legislated mandate, and treatment data are generally sparse. To enhance the scope and quality of state cancer registry data, Congress established the NPCR in 1992.18 The CDC administers this project and provides critical technical support, assisting with standardization of training, reporting, data collection, and storage. The need for populationbased systems to address the quality of cancer care has led many states to investigate novel mechanisms to collect information on cancer patients Curr Probl Surg, September 2003

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not previously considered for quality improvement initiatives. One strategy to leverage the case finding in the state registry is to match other sources of treatment information with registry staging data. For example, in 2000 the New York State Cancer Registry began collecting administrative claims information on all cancer patients from all managed care organizations in the state. Although the primary purpose of this effort is to enhance case finding, it will also be useful in quality assessment. Surveillance, Epidemiology, and End Results Program. The other population-based cancer registry program in the United States is the SEER program of the NCI. The SEER program, established by the National Cancer Act of 1971, aims to collect, analyze, and disseminate information for cancer control efforts. The SEER program strategy is to collect population-wide data in regions selected to represent the American population in ethnicity, race, and socioeconomic status. The initial SEER regions were the states of Hawaii, Utah, Connecticut, Iowa, and New Mexico, plus Puerto Rico, as well as the metropolitan areas of Detroit and San Francisco/Oakland. SEER regions currently include approximately 14% of the U.S. population and are expanding to 25% of the U.S. population. The program collects staging data on as many as 98% of cancer cases in SEER regions and follow-up data on 95% of these cases. Data collected by the SEER program include cancer type, stage at presentation, first course of treatment, and survival. Cancer stage in the SEER registry is codified using a SEER-specific system called the extent of disease (EOD) system. EOD incorporates information on tumor size and local extension, involvement of regional lymph nodes, and the presence of distant metastases. Although it might seem sensible for the SEER program to use TNM staging, the need for backward compatibility of data for analysis of time trends in cancer incidence and survival makes it necessary for the SEER program to continue collecting stage information using the EOD system. Treatment information includes data on the initial course of cancer treatment, such as surgery, radiation, and systemic therapy through 4 months after diagnosis. However, the data are often incomplete, most notably for adjuvant therapy and radiation administered outside the hospital setting. A study that examined the completeness of breast cancer treatment information in the California State Registry in the Los Angeles SEER regions demonstrated that although the registry included 95% of surgical treatment, it captured only 72% of radiation data and only 56% and 36% of data on chemotherapy and hormonal therapy, respectively.36 Therefore, SEER data have many of the same limitations as state registry data in evaluating the quality of cancer care. SEER data are also limited in their utility for ongoing quality management by the 526

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delay in availability of data and because SEER will only include 25% of the population. Matching Population Registries with Claims Data. One strategy to capitalize on the completeness of case finding in population registries is to match cases in the registry with other sources of treatment data. One such data source available on community-wide treatment is payer administrative claims. Claims data alone have limited value in the evaluation of care because they do not contain the information necessary to determine the stage of cancer at presentation, if the recorded treatments are for cancer, or if the recorded events represent a newly diagnosed cancer.37 However, claims include procedure and diagnosis codes that, when analyzed in conjunction with registry staging data for a specific patient, can identify all care regardless of the treatment venue or provider business relationships.38 One segment of the population for which there are complete claims data are older Americans through the Medicare program. Medicare maintains a national database of all claims on older Americans. The NCI and the Health Care Financing Administration (HCFA) link Medicare claims with individuals in the SEER registry in a publicly available database. This is widely utilized to study the treatment of older Americans.39,40 Although this is a powerful resource, its utility for ongoing quality improvement is limited because it only covers older persons living in SEER regions. In addition, SEER data do not identify individuals undergoing cancer screening, making it impractical for studying this important phase of care. Furthermore, SEER and Medicare data do not provide information on the use of outpatient prescription drugs (eg, use of tamoxifen, an important element of breast cancer care in the elderly population). Private payer claims have been used in conjunction with state cancer registry data to assess cancer care in several settings.41,42 Although the matching of private payer claims with state registry data may be useful for isolated studies in specific markets, there is no similar population-wide set of claims data for the general population younger than 65 years. Unlike hospital-based registries, SEER and state registries operate under legislative reporting mandates and are exempt from new regulations that restrict the reporting of identifiable health information. Despite their limitations, enhanced population-based registries are likely to take a lead role in the effort to define and improve the quality of cancer care.43 Hospital-based Cancer Registry Programs—American College of Surgeons Commission on Cancer. Hospital-based registries capture detailed treatment information that can be used to measure the quality of care for all patients treated at those hospitals. Although cancer care is Curr Probl Surg, September 2003

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shifting to the ambulatory arena, the treatment of most cancers still requires at least 1 hospitalization or at least the involvement of a pathology laboratory at the local hospital. Therefore, hospital registries identify most cases treated by their affiliated medical staff. The cancer registry collects information on all patients who receive some component of treatment for a newly diagnosed cancer at that institution, including demographics, cancer stage at presentation, type of treatment (ie, surgery, radiation, and systemic therapy), recurrence, and survival. Hospital registries have access to all information on care in the hospital, and in most communities they are able to collect data on pre- and posthospital care provided by physicians on the hospital medical staff. Comprehensive cancer registries operate mainly as part of cancer programs that are approved by the ACoS CoC. The ACoS founded the Cancer Campaign Committee in 1913 and the Joint Committee on Accreditation of Hospitals in 1918. In 1930, the Cancer Campaign Committee was renamed the Committee on the Treatment of Malignant Disease, with the goal of publishing standards, organizing cancer care, and forming cancer registries. In the 1950s the CoC published a “Manual for Cancer Programs” that outlined the structure currently used for program approval. Components of the ACoS approvals program are familiar to most surgeons and include an oversight cancer committee, a cancer registry that compiles treatment and annual follow-up data on cancer patients, and tumor boards for multidisciplinary case review. The CoC Approvals Program is a voluntary program for hospitals and is the only nationwide certification process for community-based cancer centers. In contrast, the NCI centers program only certifies approximately 60 comprehensive cancer centers, and the major focus of NCI certification is the cancer research program and not clinical care. Approximately two thirds of the NCI-designated comprehensive cancer programs also have CoC program approval. Overall, approximately 1,500 acute care hospitals in the United States maintain CoC approval. Although this represents only approximately 25% of all hospitals, these institutions collectively treat as many as 70% of cancer patients in the United States. The ACoS, in collaboration with the ACS, budgets less than $2 million annually for the CoC and NCDB. However, the voluntary commitment from participating hospitals to maintain an approved cancer program is probably in excess of $50 million. The exact added cost to hospitals of CoC program approval is impossible to determine, since at least some of the program expenses are incurred for internal quality control and mandatory reporting to outside agencies (eg, state cancer registries). The largest component of this 528

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expense is the cancer registry personnel. Cost limits the ability of many smaller hospitals to participate. The migration to computer-based data systems in the 1980s made it possible to combine the data from the registries at all CoC-approved programs. Beginning in 1989, the CoC began aggregating hospital cancer registry data into what is called the NCDB. The NCDB now contains information on approximately 12 million cancer cases and adds almost 1,000,000 cases per year. The strength of the NCDB is that it includes more complete treatment information than do either state or SEER registries, and these data are available in a more rapid time frame. This allows NCDB data to be used for detailed studies of patterns of treatment and outcome.44 However, the NCDB is not a population-based registry, and it collects only a convenience sample of cases treated at hospitals with approved programs. Therefore, it is not truly representative of the entire population of all hospitals that treat cancer patients. The data in the NCDB are of variable accuracy and quality. The most complete data in the NCDB are on hospital-based surgery. Information on posthospital care, including adjuvant systemic and radiation therapy, is often not identified and captured. A recent study highlights the extent of missing information on ambulatory treatment data in hospital registries.45 Three registries in the New York City area identified virtually all breast cancer surgery data but did not identify the use of radiation for almost one half of the women who received it and did not capture the use of systemic therapy among two thirds of women who received it. Examination of the cancer care system illustrates why collection of data on ambulatory care is difficult. The members of each patient’s team are chosen by referral practices and patient preference based on reputation, personal or family experience, and geography. This results in a chaotic web of providers who may have no affiliation with each other or with the hospital that maintains the cancer registry. The cancer registrar may have no knowledge of where patients are referred for treatment, or even if a patient was referred for care after surgery. These problems with data collection by hospital registries may be compounded by recent changes in privacy standards mandated by the Health Insurance Privacy and Portability Act (HIPPA). Concern over the heightened regulatory environment with privacy issues may make individual providers and hospitals reluctant to report data or to require patient consent for this reporting. In contrast to legislative requirements that mandate reporting to population registries, there is no such requirement to report to a hospital registry. However, HIPPA standards allow reporting from providers and other covered entities to business associates, including Curr Probl Surg, September 2003

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hospital registries, and for hospital registries to report data to business associates such as the NCDB. Data transmitted to the NCDB do not contain individual identifiers, and the data are used for quality control and research, uses approved by HIPPA. Clearly, the intent of HIPPA was not to impair the ability to evaluate and improve the quality of care. To meet the challenges faced by hospital registries and the NCDB and to enhance the utility of the NCDB for quality of care research, the CoC recently underwent a major restructuring.46 The core data set for cancer registries has been updated and streamlined, additional scientific staff have been hired to administer and direct programs, and enhanced input has been sought from the health services research community. The CoC itself has reorganized around quality evaluation, with a parent committee that oversees disease site teams in most cancer types. Each team includes leading specialists from all oncology disciplines who are focused on increasing the quality and utilization of the NCDB resource. Other National Programs. The limitations of existing data systems have led to calls for the establishment of a national system to collect cancer stage and treatment information. National Comprehensive Cancer Network. A major initiative in cancer care evaluation is the Outcomes Program of the NCCN. The NCCN is a consortium of 19 NCI-designated comprehensive cancer centers that aims to enhance the quality of cancer care and foster research. To address these goals, the NCCN developed the nation’s most complete collection of comprehensive, clinical practice guidelines (available without charge at www.nccn.org). NCCN guidelines are developed by multi-disciplinary expert teams from member institutions, are updated annually, and cover virtually all cancers and all phases of evaluation and treatment of these cancers.32 To explore the use of NCCN guidelines for quality benchmarking and to determine whether cancer care at member institutions adheres to guidelines, the NCCN established a multi-center outcomes database and analytic program.47 Detailed diagnostic and treatment information is collected and analyzed to determine whether care is concordant with the guidelines. The NCCN developed a new set of data elements aimed at measuring guideline compliance, because data in the hospital cancer registry were not sufficiently detailed to apply practice guideline benchmarks. The program began data collection in 1997 and focused initially on breast cancer. The program has expanded to include non-Hodgkin’s lymphoma and pain management. The program utilizes Web-based data submission and employs rigorous logic checks and data auditing, similar 530

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to the mechanisms used by cancer cooperative groups.48 The breast cancer database now has information on more than 12,000 patients. The data on cancer patients are used to evaluate the patterns and quality of care at each institution and to support ongoing quality improvement. The aggregate, institution-specific, and patient-specific data are provided annually to each institution for internal quality improvement. In addition, the data are a rich source of information on emerging practice patterns. The first report on practice patterns from this project was a detailed analysis of the use of sentinel node biopsy (SNB), demonstrating that expert breast cancer teams accept SNB as standard care.49 The NCCN plans to extend the outcomes program to include most major cancer sites and to link clinical data to genomic and risk factor data to foster clinical and translational research. Veterans Affairs National Surgical Quality Improvement Program. The VA Health System established what may be the most forward-thinking and effective data and quality control system for the evaluation of surgical care, the VA National Surgical Quality Improvement Program.50-52 Although not specifically targeted at cancer care, the system evaluates the delivery of cancer surgery in the VA system. Information on every major surgical procedure performed in the VA system is registered. These data are analyzed for relationships between processes of care, patient factors, provider factors, and outcomes, including morbidity and mortality rates. This information is used to enhance quality and efficiency. This strategy is now being piloted in nonfederal programs to determine whether the tenets of this system can be applied in community-based practice.53 Collaborative Staging Data System. There are currently 3 systems for recording the stage of cancer at presentation: Summary Stage, EOD, and TNM. These systems serve different purposes. However, the need to collect data using 3 systems with different rules is redundant, inefficient, and contributes to inaccuracies. To resolve the problems associated with collection of data in multiple systems, the staging standard-setting organizations (ie, American Joint Committee on Cancer [AJCC], NCI SEER Program, CDC NPCR, NAACCR (North American Association of Central Cancer Registries), and the National Cancer Registrar’s Association) developed a standardized data collection set that will be used by all population and hospital cancer registries to collect staging information.19 The data elements are in a format that allows derivation of all 3 staging systems and provides for future updating of staging systems as new prognostic factors allow more precise cancer outcome prediction. The Collaborative Staging Data System will be implemented in January 2004. Curr Probl Surg, September 2003

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National Cancer Institute Common Data Elements. The NCI recently established a program to develop common data elements for the collection of information on clinical trials, medical imaging, and clinical care. This program is compiling disease-specific data dictionaries of common elements essential to cancer care. If incorporated into treatment databases, the use of these data elements should enhance the standardization of clinical databases, ease the development of comprehensive cancer care evaluation programs, and streamline the administrative requirements for clinical trials. The NCI Common Data Elements dictionaries are available to the public from the NCI at [http://cii-server5.nci.nih.gov:8080/ pls/ cde_public/ cde_java.show].

Conclusions Existing systems for collecting data on cancer care have serious limitations. Treatment data are incomplete, especially as they pertain to an ever-increasing fraction of care administered outside the hospital. In addition, existing systems do not provide for evaluation of co-morbid disease and risk factors that directly impact on the choice of cancer treatment. Until better data are available, outside groups are likely to continue using flawed data. As employers, regulators, and payers become impatient with the pace of quality improvement, they may act in the spirit that the quest for perfect data is the enemy of otherwise good data.4 Notwithstanding these concerns, new methods for collecting cancer care data must address the limitations of the existing systems as outlined above. The ideal system must collect information on all cases in the target population and must include treatment information (including care administered in the outpatient setting). It should include information on patient factors that affect treatment and outcomes, such as co-morbid conditions, patient satisfaction, and other quality of life indicators. Data should be collected as close to real-time as possible, so the information can be used to offer feedback to providers and support to patients undergoing cancer care, as well as provide a retrospective review of the quality of care. Devising a new data system that replaces existing programs is not a practical solution. The cancer data system of the future is likely to be structured through the use of existing programs, combined with novel application of administrative and hospital data. Increased legislative support, combined with a clear commitment from all parties in the cancer surveillance community to work collaboratively, will ensure major strides in the coming years toward the realization of ideal systems for the evaluation of quality cancer care. 532

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Fig. 1. Components of cancer care. This figure depicts the relationship between the various components of cancer care.

Factors That Affect Quality There is abundant literature that demonstrates that the quality of cancer care varies widely and that this variation adversely affects patient outcome. Although the literature is replete with studies that examine practice patterns in small samples, we focus here on studies that are population based. Although we recognize that there are a multitude of factors that affect the outcome of cancer care, our discussion focuses on how quality is affected by race and ethnicity, hospital and provider volume, and physician specialization.

Components of Quality Cancer Care The relationship between the various components of cancer care, including factors that affect access to care, processes of care, and outcomes of care, is presented in Figure 1. For purposes of quality of care research, outcome most often refers to the impact of care on the patient’s clinical status and quality of life. Common measures of clinical outcome in studies regarding quality of cancer care include mortality (postoperative mortality rate and long-term survival), morbidity (local tumor recurrence and postoperative complications), quality of life, and cost. Curr Probl Surg, September 2003

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Common measures of cost include length of hospital stay, number of re-admissions, and average cost of hospital stay. Components of cancer care associated with outcome can be organized into 2 basic categories: 1) those that affect accessibility to care and 2) those that affect the processes of care. Factors that affect accessibility may act as barriers to care and include the structure of the healthcare system, characteristics of the healthcare personnel, and patient characteristics. The most common structural variables evaluated in studies of quality cancer care are hospital case volume (number of cancer cases treated in a given time period), hospital type (eg, teaching versus nonteaching hospital), and hospital size (number of beds). Characteristics of healthcare personnel that impact accessibility to quality cancer care include the qualifications and experience of surgeons and other healthcare providers. Furthermore, the sociodemographic characteristics of healthcare personnel may affect the way they interact with and prioritize the care of the patients. Patient characteristics refer to the clinical (eg, stage at diagnosis, number of co-morbidities, and urgency of admission) and nonclinical characteristics (eg, age, race, and sex) of the patients. The measurement of process of care most often involves some measure of the actual activities of cancer care. Like factors that affect access to care, the process of care is associated with health outcome. As such, for instances in which there is a strong, well-documented relationship between process of care and health outcome, the process of care itself may be used as a surrogate outcome measure. However, as previously noted, for most types of cancer, there are limited data linking process measure to clinical outcome.

Methodologic Issues Several methodological issues must be considered when interpreting the large body of research that describes the relationship between factors that affect accessibility to care, process of care, and outcome of care. In particular, when comparing results across studies, it is important to compare not only the results of the studies but also the methods used to obtain those results. Apparent differences in study results may be the result of differences in the populations studied and/or other aspects of the research design. The size and representativeness of the study population may also affect the generalizability of the findings. For example, consider the situation in which a study involving a small number of patients from a single institution fails to find a significant association between a specific factor and outcome of care. This same factor is later found to be significantly associated with outcome in large-scale, population-based 534

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studies. This difference in findings may be due to the lack of statistical power or to the nonrepresentative convenience sampling of the singleinstitution study. Differing definitions of independent variables may also impact study results. Consider the wide variation in the definitions of high-, medium-, and low-volume hospitals that is found across different studies of hospital volume and mortality rates, even for studies involving the same type of cancer. For example, there are several well-designed, population-based studies that demonstrate an inverse association between hospital volume and postoperative mortality rates in patients with lung resection for non–small-cell lung cancer.54-57 However, there is also a large, welldesigned, population-based study that failed to demonstrate a significant relationship between hospital volume and the mortality rate associated with pneumonectomy.58 Birkmeyer and colleagues evaluated mortality rates associated with lobectomy and pneumonectomy from the national Medicare claims database (MEDPAR) file for the years 1994 and 1999 (pneumonectomy, N ⫽ 10,410; lobectomy, N ⫽ 75,563) and found statistically significant inverse associations between hospital volume and mortality rates for both procedures.55 The average total hospital volume for all lung resection procedures for the highest-volume quintile was more than 46 lung resections per hospital per year. This figure included both Medicare and non-Medicare patients, as well as patients who underwent either lobectomy or pneumonectomy. Hospitals in the highest-quintile hospital volume group performed 2,183 pneumonectomies in Medicare patients at 79 hospitals over a 4-year period, indicating that hospitals in the highest volume group performed an average of 6.9 pneumonectomies in Medicare patients alone per hospital per year. This volume group included approximately one fifth of all lung resection cases in the study. Begg and colleagues evaluated mortality rates following pneumonectomy in SEER-Medicare linked database patients from 1984 to 1993 (N ⫽ 1375), and although there was a trend for increasing mortality rates with decreasing hospital volume, it was not statistically significant (P ⬍ 0.19).58 However, the highest-volume hospital included in the study performed 28 pneumonectomies in Medicare patients over the entire 10-year study period, an overall average of 2.8 procedures in Medicare patients per year. The highest-quintile hospital volume group in their study performed 271 procedures in 14 hospitals over a 10-year period, an average of 1.9 pneumonectomies per hospital per year. Thus, although the study by Begg included a large number of patients and analyzed volume as a continuous variable, the Birkmeyer study involved 58% more patients and a much wider range of hospital volumes. These differences Curr Probl Surg, September 2003

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alone could account for the fact that 1 study reported statistically significant associations whereas the other did not. Differences in the assessment of co-variates and control for confounding factors may also affect the relationship between dependent and independent variables. For example, consider the case of a study reporting that a large-volume teaching hospital had a similar postoperative mortality rate for a high-risk surgical procedure as that of a small, rural, community hospital. At first glance, this would suggest that volume was not associated with outcome. However, a more in-depth look at the study might reveal that the authors failed to evaluate the number and type of co-morbid conditions in the patients from the 2 hospitals. If high-risk patients were more likely to be referred to the large teaching hospital, adjusting for co-morbidity may actually have led to a finding of a significant difference between hospitals that favored the larger hospital. One should also be aware of variability in the source and quality of data across studies. Missing and incomplete data can sometimes limit the representativeness and therefore, the generalizability of a study. Finally, it is also important to consider the time period and setting of the studies being compared. Quality and patterns of care for many cancers differ across countries and time due to differences in the rate of innovation diffusion, resource availability, and method of reimbursement for treatment. Study Selection Criteria. It is not possible to include here a review of every published study regarding quality of cancer care or all of the factors that affect outcome. The studies reviewed in this section are primarily population based and are derived from comprehensive, area-wide databases such as the discharge data for an entire state over multiple years or the SEER registry. Data from recent time periods have been given more attention than older data, and the manuscripts reviewed are primarily those that have adjusted for patient case mix (ie, co-morbidity and patient sociodemographic characteristics, such as age, race, and sex). When describing studies that concern the effects of hospital volume on mortality rates, we have tried to give some indication about how volume groups were defined. To facilitate comparison of data across studies, we have attempted to present volume groups on the basis of the number of procedures per hospital per year, rather than the number of procedures performed over the entire study period. Process of Care versus Clinical Outcomes. Process of care is not always treated as an outcome variable in studies about the quality of cancer care. In some cases, it serves as an independent predictor of clinical outcome; in others, it serves as the dependent variable (ie, 536

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outcome measure). The frequency of studies that include process of care as a variable and the type of variable it represents in a particular study (dependent versus independent variable) tend to vary according to cancer type and stage. For sites such as breast cancer, there are highly specific, well-established recommendations for all process of care categories, including screening, diagnosis, and treatment for each stage of disease. These are based, in large part, on controlled clinical trials. Therefore, there is a strong, well-documented relationship between process of care and clinical outcome. In this situation, it is possible and often preferable to study the association between factors that affect accessibility and process of care rather than or in addition to clinical outcome. The majority of research regarding quality of care, however, concerns the relationship between factors that affect accessibility and clinical outcome. This is especially common for cancer sites in which there is a lack of effective treatment for later-stage disease, and long-term, overall survival rates are low. In these instances, when data about the process of care are collected, these data often are limited to information about a surgical procedure or technique that is thought to be associated with in-hospital mortality rates or local tumor recurrence. Research Focus. The focus of quality of cancer care research varies considerably according to cancer site. Site-specific factors that appeared to influence research focus include cancer incidence (high versus low); long-term survival (good versus poor); and surgical risk (mortality or morbidity rates and high risk versus low risk). Favorable long-term survival rates are more likely to be noted for cancer types for which effective screening methods were available that allowed diagnosis at an early stage of disease. This tends to result in a focus on quality of care for early-stage disease. This phenomenon is illustrated in research that concerns quality of care for breast cancer, a high-incidence cancer for which there is an effective screening method (mammography) and good long-term prognosis for patients with early-stage disease with appropriate treatment. Research regarding the quality of breast cancer care most often focuses on the factors associated with variation in adherence to treatment guidelines for early-stage disease or the relationship between patient characteristics, such as race or health insurance coverage, process of care, and long-term survival. In contrast, for pancreatic cancer (high surgical risk/no effective screening test/poor long-term prognosis), the most common research focus is hospital or surgeon volume and its association with postoperative mortality rates. These differences in research focus according to cancer site result in differences in the type and quantity of information that is available for different cancer sites. This in turn tends Curr Probl Surg, September 2003

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TABLE 3. Factors that may affect access to and outcomes of cancer care Structure of the care system Hospital characteristics Case volume Type (teaching versus community) Location (urban versus rural) Facilities/design Organization and administration Quality assurance program Cancer registry

Characteristics of providers/surgeons/other healthcare providers Case volume Experience and training/ certification

Affiliations with other providers Business relationships Participation in multidisciplinary groups

Multidisciplinary teams

Use of care pathways Type and number of staff Type of payer (e.g., indemnity, HMO, government)

Characteristics of patients Clinical Tumor stage and characteristics Co-morbid conditions Socioeconomic characteristics Age, sex Race/ethnicity Education/SES Insurance status (availability and type) Geographic location (including proximity to providers)

Age, sex, ethnic background Healthcare beliefs, attitudes, of healthcare providers practices

Consumers Organizations/businesses purchasing health care for members Consumer advocacy groups Regulatory agencies and policy Federal; state; local

to influence the type of interventions that are proposed as possible ways to improve quality of cancer care for patients with a particular type of cancer.

Review of Factors That Affect Care Factors that have the potential to affect cancer care are listed in Table 3. Patient clinical characteristics must be considered when assessing the adequacy of studies that concern the quality of care, since they may affect the association between other factors and clinical outcome. For example, it is important to adjust for co-morbidity when evaluating the relationship between hospital volume and mortality rates. Furthermore, published guidelines that describe accepted standards for cancer treatment are generally organized according to clinical characteristics and differ ac538

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cording to such factors as cancer type, stage, and histologic features. There are many excellent publications that describe patterns of care according to patient clinical characteristics (ie, cancer type, stage, histologic features, and grade) and the manner in which these characteristics impact patterns of care. These data will not be reviewed in this monograph. Other factors, including hospital organization and administrative policies, are hypothesized to impact cancer care, but as yet there is little research directly linking these factors to clinical cancer outcomes, such as mortality and survival rates. The authors of several publications that demonstrate a significant relationship between hospital volume and clinical outcome have suggested that increasing hospital volume may result in decreased mortality and morbidity rates; this may be the result of differences in hospital facilities, organization, and staffing. However, although this is certainly a plausible hypothesis, we found very few studies that evaluated this hypothesis in a quantitative fashion. Similarly, there is a large body of literature that demonstrates that cancer incidence, survival, and quality of care differ according to age, race, and socioeconomic status (SES).23,59 There are also many studies that demonstrate that such sociodemographic characteristics impact the healthcare attitudes and beliefs of both patients and providers.23 This would suggest that some of the differences in quality care according to age, race, and SES may be due to differences in the healthcare beliefs and attitudes of patients and/or their healthcare providers. However, there are few examples of studies that actually attempted to evaluate the effect of provider or patient attitudes on specific aspects of cancer care or clinical outcome. The few existing studies involve small numbers of patients in single institutions and tend to be descriptive. Further research will be necessary to better understand the manner in which specific health beliefs and attitudes impact quality cancer care and clinical outcomes of care.

Cancers Associated with Moderate and High Surgical Risk A large body of literature demonstrates an inverse relationship between hospital case volume and operative mortality rates.60 The strongest associations between hospital case volume and mortality rates have been noted for operations that require a high degree of technical skill and for which complications frequently result in substantial morbidity or mortality rates. The most consistent and compelling data with regard to hospital volume and postoperative mortality rates are those for pancreatic and esophageal cancers. Curr Probl Surg, September 2003

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TABLE 4. Population-based studies show an association between hospital volume and pancreatectomy in-hospital mortality rates

Author, year Gordon et al, 1995 (64)

Study period patients 1988-1993

Imperato, 1996 (68)

Maryland N ⫽ 502 (all) 1984-1991 New York N ⫽ 1972 (all) 1990-1994 California N ⫽ 1705 (all) 1991-1994

Begg et al, 1998 (58)

New York Medicare N ⫽ 579 (ⱖ65 years) 1984-1994

Lieberman et al, 1995 (62)

Glasgow et al, 1996 (62)

Sosa et al, 1998 (63)

SEER/Medicare N ⫽ 742 (ⱖ65 years) 1990-1995 Maryland N ⫽ 1236 (all)

Gordon et al, 1998 (65)

1984-1995

Birkmeyer et al, 1999 (165)

1992-1995

Simunovak et al, 1999 (69)

Birkmeyer et al, 2002 (55)

Birkmeyer et al, 2002 (55)

540

MEDPAR/Medicare N ⫽ 7229 (ⱖ65 years) 1988/89-1994/95 Ontario, Canada N ⫽ 842 (all) 1994-1999 Ontario, Canada N ⫽ 842 (all) 1994-1999 MEDPAR/Medicare N ⫽ 10,530 (ⱖ65 years)

Procedure(s) overall mortality rate Pancreaticoduodenectomy for any reason 7.7% 7.7% Pancreatic resection for cancer 12.9% Pancreatic resection for cancer 9.9% Pancreaticoduodenectomy for any reason 9.7%

Pancreatic resection for cancer 10.1%, 30-day

Pancreatic resection (N ⫽ 496) Bypass (N ⫽ 542) Stent for cancer (N ⫽ 198) Pancreaticoduodenectomy 1984-17.2% 1998-4.9% Radical pancreaticoduodenectomy for cancer and benign conditions

Pancreatic resection for cancer 9.7% Pancreatic resection for cancer 9.7% Pancreatic resection for malignancy 11.0%

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TABLE 4. Continued Mortality rate (%)/hospital volume of cases/year

P value

Low

High

19.1

2.2

⬍1/y

⬎4/y

21.8

4.0

⬍1.25/y

⬎10/y

14.1* ⱕ1/y

3.5* ⱖ10/y

⬍0.001

12.0

2.2

⬍.0002

ⱕ1.25/y

⬎6/y

12.9

5.8

⬍0.6/y

⬎1.1/y

18.8 15.3 9.8 ⬍5/y (1984-1987)-19.5% (1992-1995)-12.4% Mean ⱕ 1/y 16

0.9 4.2 1.6 ⬎20/y 3.2% 1.0% ⬎20 (mean, 51.1)/y 4.1

⬍1/y

ⱖ5/y

11.3

3.4

⬍3/y

⬎6/y

17.6 ⬍1/y

3.8 ⬎16/y

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⬍.001

⬍0.001

⬍0.004

⬍0.001 ⬍0.01 ⬍0.05 ⬍0.001 ⬍0.001 ⬍0.001

⬍0.01

⬍0.001

541

Cancers of the Pancreas and Esophagus. Several large studies demonstrate a significant relationship between increasing hospital volume and decreasing postoperative mortality rates following major pancreatic resection (Table 4).55,58,61-69 All of these studies adjusted for comorbidity and patient characteristics. Two of these studies also adjusted for surgeon case volume.63,66 A brief description of the patients studied, the time period of study, the type of pancreatic resections included in the study, and the mortality rates reported for the highest- and lowest-volume groups in each study have been provided, along with the mortality rate for the overall study population. The overall mortality rates for series involving pancreatic resection for cancer ranged from approximately 9.7% to 12.9% (Table 4). Low-volume centers (⬍1.25 cases per year) that performed pancreatic resection for cancer reported mortality rates that ranged from 12.9% to 21.8%. The definition of high volume varied between studies, and despite adjustment for case mix, there was a tendency for rates to vary somewhat between studies, depending on the age group (studies limited to Medicare patients versus studies that include all adult incident cases in a particular population group); underlying diagnosis (cancer tended to be associated with higher mortality rates than benign conditions); and time period. This same phenomenon was also noted for esophageal cancer. Mortality rates for pancreatectomy for cancer at high-volume hospitals ranged from 3.5% to 5.8%. The relationship between hospital volume and mortality rates remained statistically significant after adjusting for patient case mix and other potential confounders in all studies. What is particularly compelling about these data is the consistency of results across studies that involve different definitions of high versus low volume, study populations of varying size, age, geographic location, disease type and severity, different sources of data, and different methods of analysis. The body of literature about hospital volume and esophagectomy in-hospital mortality is also consistent and compelling. A summary of several large, population-based studies that have demonstrated a statistically significant inverse association between hospital volume and esophagectomy in-hospital mortality rates is presented in Table 5. All but 1 of these studies adjusted for patient case mix. The overall mortality rate for patients undergoing esophagectomy for cancer ranged from 5.4% to 15.5%. Low hospital volume groups (⬍2 to ⬍6 cases per year) experienced in-hospital mortality rates that ranged from 9.2% to 20.3%. High hospital volume groups (⬎6 to ⬎50 cases per year) experienced mortality rates that ranged from 2.5% to 8.4%.55,58,70-73 The results of the various studies summarized in Table 5 are consistent across studies that have 542

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TABLE 5. Population-based studies show an association between hospital volme and esophagectomy in-hospital mortality rates

Author, year

Study period patients

Procedure(s) overall mortality %

Mortality rate (%)/hospital volume of cases/ year for lowest and highest volume groups

P value*

Low

High

Begg et al, 1998 (58)

1984-1993 SEER/Medicare N ⫽ 503 (ⱖ65 y)

Esophagectomy for cancer 12.1%

17.3 ⬍0.6/y†

7.4 ⬎1.1/y

⬍0.001

Patti et al, 1998 (70)

1990-1994 California N ⫽ 1561 (all)

Esophagectomy for cancer 14.1%

16 ⬍6/y

4.8 ⱖ6/y

⬍0.001

Dimick et al, 2001 (71)

1984-1999 Maryland N ⫽ 1136 (all)

Esophageal resections for any condition 10.5%

16 ⬍3/y

2.7 ⬎15/y

⬍0.001

Kuo et al, 2001 (72)

1992-2000 Massachusettes N ⫽ 1193 (all)

Esophagectomy for cancer 5.4%

9.2 ⬍6/y

2.5 ⱖ6/y

⬍0.001

van Lanschot et al, 1993-1998 2001 (73) DNDP‡ N ⫽ 1792 (all)

Esophagectomy for cancer 8.8%

12.1 1–10/y

4.9 ⬎50/y

⬍0.001

Birkmeyer et al, 2002 (55)

Esophagectomy for cancer 15.5%

20.3 ⬍2/y

8.4 ⬎19/y

⬍0.001

1994-1999 Medpar/Medicare N ⫽ 6337 (ⱖ65 yrs)

*The inverse association between hospital volume and mortality rate remained statistically significant after adjusting for case-mix for all of the studies listed except for the study reported by Lauschot et al, which did not adjust for case-mix due to restrictions in the data available. † Listed in the manuscript as ⬍5 and ⬎11 for the entire study period of 10 years. ‡ DNPD, Dutch Network and National Database for Pathology; SEER, Surveillance, Epidemiology, and End Results Program.

different definitions of high versus low volume; study populations of varying size, age, and geographic location; different sources of data; and different methods of analysis. The volume threshold (ie, what is considered “low volume”) associated with a high risk of mortality appears to be considerably higher for esophagectomy than for pancreatic resection. In a recent population-based study of Medicare patients, Birkmeyer and colleagues evaluated the relationship between hospital volume and Curr Probl Surg, September 2003

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TABLE 6. Studies concerning the effect of surgeon and hospital characteristics on lung cancer treatment and outcome Author, year

Study period patients

Procedure(s)/diagnosis

Romano et al, 1992 (57)

1983-1986 California N ⫽ 12,439

Pulmonary resection for lung or bronchial tumors

Silvestri et al, 1998 (166)

1991-1995 South Carolina N ⫽ 1583 (all) 1984-1993 SEER/Medicare N ⫽ 1375 1985-1996 SEER/NIS† Medicare

Lung cancer resection

Begg et al, 1998 (58)

Bach, 2001 (54)

Pneumonectomy for lung cancer

Resection for lung cancer (stage I-IIIa)

Gregor et al, 2001 (74)

1995 Scottish Cancer Registry N ⫽ 4465

Management by lung cancer specialist versus general physician

Birkmeyer, 2002 (55)

1994-1999

Lobectomy (N ⫽ 75,563) Pneumonectomy (N ⫽ 10,410)

MEDPAR/Medicare

Hannan, 2002 (167)

1994-1997 New York State N⫽6954 (all)

Lobectomy of lung

*The inverse association between hospital volume and clinical outcome remained statistically significant after adjusting for case mix. † NIS, National in-patient sample; SEER, Surveillance, Epidemiology, and End Results Program.

mortality rates for several different surgical procedures, including esophagectomy and pancreatic resection. The adjusted 30-day mortality rate for hospitals that performed more than 16 pancreatic resections per year was only 3.8%.55 The adjusted 30-day mortality rate for hospitals performing 3 to 5 pancreatic resections per year was 11.0%. In comparison, the lowest 30-day mortality rate reported for esophagectomy was 8.4% for the highest-volume group, which consisted of hospitals that performed more than 19 procedures per year. An adjusted 30-day mortality rate of 11.4% was noted for hospitals that performed 8 to 19 esophagectomies per year (Table 5). Both groups included patients who were at least 65 years of age and were undergoing operations for cancer. The overall crude 544

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TABLE 6. Continued

Summary of major findings In-hospital mortality rates Lobectomy 4.2% Pneumonectomy 11.6% Hospital volume significant predictor of mortality* Mortality from lobectomy higher for general surgeons than thoracic surgeons (5.3 versus 3.0, P ⬍ 0.05). Nonsignificant difference favoring thoracic surgeons for pneumonectomy Nonsignificant trend toward increasing mortality rate with decreasing volume (adjusted P ⫽ 0.19) Low hospital volume associated with Lower 30-day (P ⫽ 0.04)* and overall survival rates (P ⫽ 0.003)* Higher rate of complications (P ⬍ 0.001)* Treatment by specialist within 6 months associated with significantly improved 3-year survival (P ⬍ 0.0001)

Volume

Adjusted mortality rates for Lobectomy*

Lowest group Highest group

4.0% 5.7% (P ⬍ 0.001)

Pneumonectomy* 10.7% 16.1% (P ⬍ 0.001)

Surgeon and hospital volume both independent predictors of lobectomy mortality rate, even after adjusting for patient case mix

30-day mortality rates associated with the 2 procedures were 11.0% (pancreatic resection) and 15.5% (esophagectomy), respectively. The Medicare patients studied by Birkmeyer and colleagues represent the largest and most recently studied (1994 to 1999) series of esophagectomy (N ⫽ 6337) and pancreatic resection (N ⫽ 10,530) patients. Cancer of the Lung Hospital Volume and Mortality Rates. The published literature that examines hospital volume and outcome in lung cancer surgery is summarized in Table 6. Although pulmonary resection for lung cancer is associated with a higher mortality rate than many other types of cancer surgery, there has been less call to regionalize this type of cancer surgery than there has been for surgery for cancer of the esophagus and pancreas. There are several potential explanations. As is the case with pancreatic Curr Probl Surg, September 2003

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cancer, only a small percentage of those individuals who develop lung cancer actually undergo resection.58 Of those who do, the majority of lung cancer patients undergo lobectomy rather than pneumonectomy (Table 6).55 In-hospital mortality rates associated with lobectomy are generally lower than those associated with pneumonectomy. In addition, one large, population-based study by Begg and colleagues failed to find an association between hospital volume and 30-day mortality rates after pneumonectomy.58 However, the range of hospital volumes included in the study by Begg and colleagues was extremely narrow in comparison to other studies of this nature. This, plus the fact that the study of Begg and colleagues included only a fraction of the individuals included in the study by Birkmeyer and colleagues suggests that the relationship between the mortality rate after pneumonectomy and hospital volume should not be dismissed on the basis of the results from the study of Begg and colleagues. In another study, hospital volume was demonstrated to be associated inversely with not only 30-day survival rates but also 2-year and overall 5-year survival rates.54 In addition, in this study, lower hospital volume was associated significantly with a higher risk of major surgical complications.54 Surgeon Characteristics and Mortality Rates. A few studies have demonstrated an association between in-hospital mortality rates following surgery for lung cancer and surgeon characteristics, including surgeon specialty and volume (Table 6). In a study of 6954 patients from New York State who underwent lobectomy, both surgeon and hospital volume were found to be independent risk factors for in-hospital mortality rates in a multivariate model that adjusted for patient case mix.56 A Scottish study conducted by Gregor and colleagues found that treatment by a specialist was an independent predictor of access to potentially curative treatment and that treatment by a specialist within 6 months of diagnosis significantly improved the 3-year survival rate, even after taking into account the stage at diagnosis and other aspects of the patient case mix.74 Hepatic Resection and Gastrectomy. Hepatic resection and gastrectomy for cancer are 2 other procedures for which data demonstrate a significant inverse association between hospital volume and in-hospital operative mortality rates.55,56,58,75,76 In the case of gastrectomy, overall in-hospital mortality rates are high in comparison with other types of cancer surgery, such as nephrectomy or colectomy.55 For example, in the large, population-based study by Birkmeyer and colleagues, adjusted mortality rates for the highest hospital volume quintile in the study were almost identical for gastrectomy (8.6%, ⬎21 cases per year) and esophagectomy (8.4%, ⬎19 cases per year).55 However, although the 546

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adjusted in-hospital mortality rate for esophagectomy climbs to 20.3% in the lowest hospital volume quintile, the adjusted mortality rate reaches only 11.4% in the lowest hospital volume quintile for gastrectomy (P ⬍ 0.001). A recent study by Hannon and colleagues evaluated the joint effect of hospital and surgeon volume on in-hospital mortality rate for all adult patients who underwent gastrectomy in New York State between 1994 and 1997.56 Both hospital and surgeon volume had an impact on mortality rates, with mortality rates ranging from 9.6% in the highsurgeon/high-hospital-volume group to 17.5% in the low-surgeon/lowhospital-volume group. As with other cancer sites, an increasing number of co-morbid conditions and increasing age were also associated with an increasing risk of in-hospital death among gastric cancer patients. The largest series of hepatic resection patients who have been studied is reported in the 10-year SEER/Medicare study by Begg and colleagues.58 This study involved patients who underwent hepatic resection for metastatic disease secondary to colorectal cancer. The overall mortality rate in this study was 4.2%, with adjusted mortality rates ranging from 1.7% in hospitals that performed fewer than 12 cases in 10 years (ie, ⱕ1.1 per year, on average) to a high of 5.4% in hospitals that performed 1 to 5 cases over 10 years (P ⫽ 0.05). In a study by Glasgow and colleagues of hepatic resection for primary liver cancer in California hospitals, adjusted mortality rates ranged from 9.4% in the highest hospital volume group (ⱕ17 in 5 years, or ⱕ3.3 per year) to 22.2% in the lowest hospital volume group, which averaged fewer than 0.6 cases per year.76 When volume was defined as total resections for both malignant and benign disease, the relationship remained statistically significant. However, the study by Glasgow and colleagues included a much wider volume range and a different type of cancer patient than the study by Begg and colleagues. Choti and colleagues studied the relationship between hepatic resection mortality rates and hospital volume for 3 groups of patients: those with primary liver cancer, those with metastatic liver disease, and those with nonmalignant liver disease.75 The relationship between hospital volume and operative mortality rates was less pronounced for patients who underwent minor resections and resections for metastases than other types of resection. It appears that there had been a degree of regionalization in Maryland: 1 high-volume center accounted for approximately 44% of all procedures (an average of 40.6 per year). In this state, there was a 5-fold difference in mortality rate between the other 35 hospitals (mortality rate, 7.9%), which each performed an average of 1.5 procedures per year and the single, high-volume hospital (mortality rate, 1.5%, P ⫽ 0.05).75 Curr Probl Surg, September 2003

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Low to Moderate Surgical Risk Cancers The measurement of perioperative mortality and morbidity rates is not a sensitive measure of quality for cancers for which treatment carries a low risk. However, the same factors of hospital and provider case volume, training, and experience may affect other outcomes such as quality of life, organ preservation, cost, cancer recurrence, and long-term survival rates. The impact of volume on outcome in such situations may also not be solely a function of how well a specific procedure is performed. Outcome differences may also reflect the availability of multi-disciplinary postsurgical care and the knowledge base to understand the need for such treatment. Cancers of the Thyroid, Prostate, and Ovary. There are surprisingly few population-based studies about the effects of surgeon and hospital characteristics on treatment outcome for thyroid, prostate, and ovarian cancer. Sosa and colleagues utilized Maryland hospital discharge data for the period 1991 to 1996 to study the association between surgeon case volume and the rate of complications associated with thyroidectomy.77 After adjusting for case mix, procedure type, and hospital volume, these investigators found that patients of low-volume surgeons undergoing thyroidectomy for cancer or benign conditions other than adenoma were significantly more likely to experience complications associated with their operation than were patients of highvolume surgeons. As noted by the authors, “. . . There is a paucity of objective evidence outside of clinical series published by endocrine surgeons to support a consistent association between surgeon experience and patient outcomes. There is no benchmark for comparison that represents the outcomes of thyroid patients operated on by community surgeons.” The study by Sosa and colleagues is a large, population-based, state-wide study (N ⫽ 5,860). Outcomes (eg, complication rates, length of stay, and hospital charges) have been adjusted for case mix and hospital volume using multivariate models. State average rates for the various types of complications that were studied are included in the report to provide a basis for comparison. As such, this study represents an excellent example of what can be accomplished with the use of hospital discharge data to study endpoints other than mortality rates. Begg and colleagues used SEER/Medicare-linked data to evaluate postoperative complications, late urinary complications, and long-term incontinence after radical prostatectomy according to hospital and surgeon volume in 11,522 men older than 65 years.78 These investigators found that both hospital volume and surgeon volume were significantly inversely associated with the percentage of patients who experienced postoperative, potentially life-threatening complications, late urinary complications, and long-term 548

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urinary complications. The authors state an important rationale for their study and others like it when they write “. . . Our view is that the apparently small effect of hospital volume on the rate of death after common procedures may mask more profound differences in outcome with respect to quality of life endpoints that are of great importance to patients.” Two studies demonstrate a significant association between increased long-term survival from ovarian cancer and treatment by a gynecologic specialist.79,80 The study by Nguyen and colleagues also presents data that suggest that women who are treated by general surgeons are significantly less likely to receive appropriate staging or optimal tumor debulking. One of the difficulties with these data is that both reports were published before 1995, and both involved patients who were diagnosed in the 1980s. We were unable to find any other population-based studies that involved patients who were diagnosed and treated during more recent time periods. A slightly more recent process of care study by Munoz and colleagues included 785 women diagnosed in 1991.81 These investigators examined the causes, type, and receipt of recommended staging. Older women with late-stage disease did not receive recommended treatment, and the majority of women with early-stage disease did not receive recommended staging or treatment. Cancers of the Colon and Rectum Hospital Volume, Surgeon Characteristics, and Clinical Outcome in Colorectal Cancer. Several large, population-based studies have now demonstrated a small but statistically significant association between hospital volume and in-hospital or 30-day mortality rates for patients with colorectal cancer, including reports by Hannan and colleagues (1994 to 1997, N ⫽ 22,129); Birkmeyer (1994 to 1999, N ⫽ 304,285); Harmon and colleagues (1992 to 1996, N ⫽ 9739); Schrag and colleagues (1991 to 1996, N ⫽ 27,986); and Rosen and colleagues (1986 to 1998, N ⫽ 13,000).55,56,82-84 In most instances, the absolute difference between the highest- and lowest-volume group does not exceed 2%, and the overall study mortality rates are less than 5%, even when the study population is older than 65 years. Some studies have failed to demonstrate a significant association between hospital volume and colorectal surgery mortality rates. However, these studies had substantially smaller sample sizes than those that reported a significant association between hospital volume and mortality rates, and in many instances, the smaller studies actually reported a difference in volume that was similar in magnitude to those studies that reached statistical significance.85,86 Several studies have also evaluated the impact of surgeon volume or surgeon training on in-hospital or 30-day mortality rates.56,82,84,87 AlCurr Probl Surg, September 2003

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TABLE 7. Studies concerning the effect of surgeon-related factors on rectal cancer treatment and outcome

Author, year Hermanek et al, 1995 (168)

Study period patients

Surgeon-related factor

1984-1986 N ⫽ 1539 Germany* 1980-1993 N ⫽ 1399 Stockholm*

Surgeon volume ⱖ6.66/y versus ⬍6.66/y

Porter et al, 1998 (89)

1993-1990 N ⫽ 683 Edmonton, Canada

Colorectal versus other specialty ⱖ2.62/y versus ⬍2.62/y

Dorrance et al, 2000 (90)

1990-1993 N ⫽ 378 (University hospital)

Colorectal specialist versus other surgeons

Read et al, 2002 (93)

1977-1995 N ⫽ 384 Missouri (university hospital)

Colorectal surgeon versus noncolorectal surgeons

Stocchi et al, 2001 (91)

1979-1992 N ⫽ 673 U.S.-Midwest*

Surgeon volume ⬎10/y versus ⱕ10/y

Birbeck et al, 2002 (92)

1986-1997 N ⫽ 586 Leeds, UK*

Gastrointestinal surgeons versus other surgeons

Martling et al, 2002 (95)

1995-1997 N ⫽ 652, all patients in Stockholm, Sweden

Surgeon volume ⬎12/y versus ⱕ12/y TME workshop participants versus non-participants

Holm et al, 1997 (88)

Certified specialist ⱖ10 y versus ⬍10 y

*Clinical trial participants LR, local recurrence; DSS, disease-specific survival; TME, total mesorectal excision; RR, relative risk. GI, gastrointestinal. Note: All studies listed above conducted multivariate analyses that adjusted for the effect of patient clinical, demographic, and treatment variables associated with LR and DSS. 1 ⫽ increased 2 ⫽ decreased.

though it is not uncommon for studies with a smaller sample size to find a significant association between surgeon characteristics and other factors, sample size appears to be a major factor in determining whether a study is able to demonstrate a significant association between surgeon case load and mortality rates.88,89 One study that reported on surgeon volume is particularly noteworthy.82 Harmon and colleagues studied a sufficiently large population to evaluate the effect of both surgeon volume and hospital volume simultaneously. 550

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TABLE 7. Continued Summary of major findings LR RR: 1.71 (P ⬍ 0.03) No association with DSS LR RR: 0.8 (95% CI, 0.6-1.0) DSS RR: 0.8 (95% CI, 0.7-1.0) University hospital status independent predictor of 2LR, 1DSS. LAR (CRS versus other) 72.5% versus 35.1% (P ⬍ 0.001) LAR (high versus low volume) 62.2% versus 50.8% (P ⫽ 0.007) Surgeon volume and specialty independent risk factors for LR and DSS. CRS: Higher use of LAR (21.5% versus 9.82% (P ⫽ 0.003) Reduced LR: RR 3.42 (P ⫽ 0.04) (other versus CRS) Reduced overall recurrence (P ⬍ 0.03) CRS: Lower LR 7% versus 16% (P ⫽ 0.005) Improved DSS 77% versus 68% (P ⫽ 0.005) Greater use LAR 50% versus 30% (P ⫽ 0.00004) Free radial margins associated with LR (P ⫽ 0.01) 1Number, % nodes 2DSS; 1%nodes 1LR Tumor adherence associated with 1LR 2DSS DSS for patients of GI surgeons was significantly better by end of study than that for patients of other surgeons. (P ⬍ 0.03). LR 10% versus 4% (P ⫽ 0.02) DSS 89% versus 2% (P ⫽ 0.007) Greater use of sphincter-sparing surgery, TME, and radiation therapy in TME workshop participants.

For this study, surgeon volume categories were as follows: ⬍5 per year; 5 to 10 per year; and ⬎10 per year. Hospital volume categories were ⬍40 per year; 40 to 70 per year; and ⬎70 per year. Harmon and colleagues found that both hospital and surgeon volume contributed to the risk of mortality, with low-volume-hospital/low-volume-surgeon groups experiencing the highest mortality rates. Mortality rates ranged from 5.08% for the low/low group to 2.39% for the high/high group. The results of multivariate analyses that adjusted for patient case mix revealed that the mortality rates of low-volume surgeons were better at higher volume hospitals but still never quite equaled the more favorable mortality rates of high-volume surgeons. Medium-volume surgeons achieved outcomes Curr Probl Surg, September 2003

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similar to high-volume surgeons when operating in medium-volume or high-volume hospitals but not when operating in low-volume hospitals. Rosen and colleagues examined the relationship between surgeon specialty and hospital mortality rates for 2805 patients who underwent colorectal operations in a single state between 1989 and 1994.84 Although Rosen and colleagues demonstrated a significantly lower mortality rate for colorectal surgeons versus other surgeons (1.4% versus 7.3%, P ⫽ 0.0001), perhaps the most interesting finding from this study was that the difference in mortality rates for board-certified colorectal surgeons versus institutional surgeons increased as the patients’ severity of illness increased. For patients with an Admission Severity Group rating of 3 (with 4 being the most severe), the mortality rates for colorectal surgeons versus other surgeons were 5.7% versus 16.4% (P ⫽ 0.001). These data suggest that the tendency to adjust for co-morbidity in surgeon versus outcome studies may sometimes obscure important information about the role of a surgeon’s training and experience in providing quality care for patients who are considered high risk as a result of co-morbidity. Surgeon Characteristics, Process of Care Factors, and Treatment Outcome in Rectal Cancer. There appears to be significant variability in the quality of colorectal cancer care. There is also a growing body of literature that links surgeon training and experience and process of care factors with local tumor recurrence and disease-specific survival rates in patients with rectal cancer.89-92 Recent studies about the effects of surgeon-related factors on rectal cancer treatment and outcomes are summarized in Table 7. Studies regarding process of care factors such as surgical technique require data that are generally not available in large, population-based data sets such as the Medicare/SEER-linked database or individual state hospital discharge files. For this reason, the sample size of such studies tends to be considerably smaller than that for hospital volume studies. To facilitate data collection in studies concerning process of care, it also is common to conduct secondary analyses using data that were collected as part of a clinical trial. Several studies summarized in Table 7 fall into this category. Several studies have demonstrated statistically significant associations between surgeon specialty88,92,93 or surgeon case volume91,94,95 and local tumor recurrence and/or disease-specific survival rates in patients with rectal cancer. A recent study by Porter and colleagues is unique, in that it assesses the effects of both surgeon specialization and surgeon volume simultaneously.89 This study found that both specialization and volume were independent risk factors for local tumor recurrence and disease552

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specific survival rates. Cox proportional hazard (multivariate) models that included clinical and treatment variables revealed that advanced stage, vascular or neural invasion, adjuvant therapy, rectal perforation or tumor spill during operation, as well as surgeon specialty and volume, were independent predictors of local tumor recurrence. After adjusting for other significant factors in the model, patients undergoing operation by a noncolorectal surgeon were found to be approximately 2.5 times more likely to experience local tumor recurrence; patients whose surgeon performed fewer 21 resections during the study period were 1.8 times more likely to experience local recurrence. Furthermore, the data suggest that having a low-volume surgeon (⬍21 resections) who was also a noncolorectal-trained surgeon increased the risk of recurrence 4.5-fold. Similar results were noted for disease-specific survival rates, with 5-year survival rates ranging from 67.3% for the patients of high-volume, colorectal specialists to a low of 39.2% for the patients of low-volume surgeons who did not have colorectal specialty training. Porter and colleagues also found that both colorectal-trained surgeons and high-volume surgeons were significantly more likely to perform sphincter-sparing low anterior resection (LAR) than their noncolorectaltrained or low-case-volume colleagues.89 This phenomenon was also noted in several other studies.90,93,95 There was also significant variation in the overall use of LAR versus abdominoperineal resection (APR) between studies. Although LAR is not associated with differences in local recurrence or long-term survival rates, patients who undergo sphinctersparing operations have been demonstrated to have improved quality of life in comparison with individuals who undergo APR.96-98 Therefore, surgeon specialization and experience are important quality of care issues with regard to cancer of the rectum. Several other factors related to surgical technique have been found to be associated with both clinical outcome (ie, local recurrence, survival) and surgeon characteristics (eg, specialty, volume), including length of resection specimen, number/percent of positive lymph nodes, and free radial margins. Reinbach and colleagues studied the effects of surgeon specialty interest on the type of resection performed in patients with colorectal cancer for 116 Scottish patients accrued over a 1-year period.99 These investigators found that surgeons with colorectal cancer expertise resected twice as much colon (P ⬍ 0.0001), were more likely to remove adjacent clinically involved organs for left-sided colon and rectal cancers (15% versus 0%), and retrieve a larger number of lymph nodes from the mesentery (13% versus 7.5%, P ⫽ 0.08) than surgeons from other specialty groups. Birbeck and colleagues examined the role of circumCurr Probl Surg, September 2003

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ferential resection margin involvement (CRM⬍1 mm) as a likely predictor of recurrence.92 These investigators found that CRM involvement influenced both local tumor recurrence and survival rates, doubling the risk of death and increasing by 3.5 times the risk of local recurrence. If the data concerning CRM reported by Birbeck and colleagues can be replicated by other researchers, this finding may represent a unique opportunity for surgeons to markedly improve treatment outcome by improving their technique in one specific aspect of rectal cancer surgery. Although the implications of the study by Birbeck and colleagues were intriguing, they also suggest variability in surgical techniques, even among surgeons participating in a standardized clinical trial. Another report clearly demonstrates that clinically significant variability may occur, even within the context of a randomized clinical trial.91 This study evaluated the role of surgeon and tumor variables in local recurrence and survival rates for 673 patients with stage II/III rectal cancer patients who were enrolled in 3 adjuvant clinical trials. Stocchi and colleagues documented “suboptimal practices” while conducting the study. They noted that several technical variables were poorly documented in the records, including data specific for inadvertent bowel perforation, extent of radial spread, and radial free tumor margins (factors that have been associated with local recurrence and survival rates). Recommendations of the College of American Pathologists Consensus Statement of 1999 suggest that a minimum of 12 lymph nodes is necessary to perform adequate staging for colorectal cancer.100 The consensus statement developed by the NCI (Guidelines 2000 for Colon and Rectal Surgery) states that a minimum of 12 lymph nodes must be examined for entry into a colon adjuvant therapy trial in which lymph nodes are negative for disease.101 Despite the fact that the data reported by Stocchi and colleagues were generated as part of a randomized clinical trial, 65% of patients had fewer than 12 lymph nodes examined, 18% had fewer than 4 lymph nodes examined, and 1.2% had no lymph nodes examined at all.91 Data concerning lymph node status was missing in 3.4% of cases. Stocchi and colleagues note that “. . . evidence that surgical treatment itself did not meet typical oncologic ideals was indicated by the rate of tumor transection (51% of adherent tumors) and the rate of distal margins less than 1 cm (15%), especially because en bloc resection and margins of 1 to 2 cm are considered the standard. . . .” These data graphically illustrate the magnitude of the problem faced by surgeons with regard to improving the quality of care for colorectal cancer. 554

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Breast Cancer. Breast cancer is a high-incidence cancer with low surgical risk. Well-established guidelines for the treatment of breast cancer have now been available for several years.29,102 Breast cancer is also a disease for which there is a wealth of high-level evidence to support best practices in all aspects of care, including screening, diagnosis, treatment, and follow-up. It is also a disease for which there is a large body of literature that demonstrates substantial variation in cancer treatment in all aspects of care.103 The results of several studies indicate that hospital, surgeon, and patient characteristics are associated with long-term breast cancer survival rates. Chaudhry and colleagues compared the survival experience of 938 Ontario women who were diagnosed with node-negative breast cancer in 1991 according to the type of hospital where treatment was initially received.104 The crude 5-year survival rate was 88.7% for women who had their initial operation in a community hospital and 92.5% for women who had their initial operation in an teaching hospital. The results of multivariate analyses that adjusted for patient case mix (including severity of disease) and other treatments received revealed that women treated at teaching hospitals had a 33% overall reduction in risk compared with women treated at community hospitals. Further analysis revealed that this difference was due primarily to a 53% relative reduction in the risk of death among women with tumors smaller than 20 mm in diameter. There was no significant difference in long-term survival rates according to treating hospital status for women with larger tumors. Chaudhry and colleagues also found differences in the process of care at each type of hospital. Based on their results, they suggest that subtle differences in the process of care, especially systematic differences in the use of nodal dissection between the 2 types of hospital, may have been responsible for the differences in survival rates that were observed. Helsper and colleagues studied variations in breast cancer treatment and survival rates according to whether or not a patient was treated at an NCI-designated cancer center (NCICC) by a surgical oncologist (SO).105 A total of 28,604 women diagnosed between 1990 and 1998 (of 43,411 incident cases) were identified through the Los Angeles County Cancer Surveillance System. The authors found that patients treated by a SO were significantly more likely to undergo BCS (62.2% versus 46.2%; P ⬍ 0.0001) and had significantly better 5-year survival rates (P ⬍ 0.0001) than women treated by other types of surgeons. Both groups of surgeons treated a similar percentage of women with advanced disease. NCICC hospitals treated a higher percentage of patients with advanced disease than other hospitals, but there was no significant difference in survival Curr Probl Surg, September 2003

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rates between patients treated at NCICC versus other hospitals. Similarly, there was no significant difference in survival rates between patients treated by a SO and non-SO surgeons at NCICC hospitals. However, patients treated by a SO in a non-NCICC hospital had significantly longer survival rates than patients treated by other types of surgeons at a non-NCICC hospital, resulting in a 36.3% reduction in risk of death. Hospital case volume is also associated with 5-year survival rates. In a large, population-based study that utilized the New York State hospital discharge database and data from the New York State Cancer Registry, Roohan and colleagues studied 47,890 women diagnosed with breast cancer between 1984 and 1989 to evaluate the association between hospital volume and 5-year survival rates.106 After adjusting for stage at disease, type of surgery, co-morbidity, age, and other sociodemographic factors, these investigators found that patients treated at very-low-volume hospitals (ⱕ10 breast cancer operations per year) had a 60% higher risk of death than patients receiving care at high-volume hospitals (150 breast cancer operations per year). Patients in the low- and moderate-volume hospitals were also at increased risk of death compared with patients treated at the high-volume hospitals (low, 30%; moderate, 19% higher risk). Roohan and colleagues hypothesized that the high-volume hospitals are more likely to provide effective postsurgical adjuvant treatments than other hospitals. A second study using data from the ACoS NCDB confirmed that long-term mortality rates are related to hospital volume of breast cancer.107 Patients treated at hospitals that performed 11 to 25 cases annually had a 10% worse 5-year survival rate compared with those treated at hospitals with high volumes. Those treated at hospitals that performed 10 or fewer procedures had a 20% worse survival rate. These hospitals accounted for approximately 40% of the hospitals reporting to the NCDB in the study time period of 1985 to 1991. The significance of this volume-outcome association is underscored by the fact that only 30% of hospitals in the United States participate in the NCDB. The lowvolume hospitals participating may be those with better outcomes than similar hospitals that do not participate, because they are willing to make the substantial effort and investment in cancer programs. Yet even at these interested hospitals, low volume correlates inversely with survival. Neither of these studies evaluated process of care activities. However, a recent study of 723 early-stage breast cancer patients treated at 1 of 4 New York City hospitals between 1994 and 1996108 provides data to support Roohan’s hypothesis concerning the relationship between hospital volume and provision of postsurgical adjuvant treatments. In this 556

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study, Bickell and colleagues found that the quality of care (as measured by adherence to breast cancer treatment guidelines) varied markedly according to hospital. In particular, women treated at the 3 lower-volume hospitals (50 to 150 cases per year) were significantly more likely to be missing recommended adjuvant treatments (33% versus 18%) than women at the highest-volume hospital (⬎150 cases per year). This relationship remained statistically significant even after adjusting for differences in stage of disease, age, insurance status, and race. Substantial other data document that the quality of breast cancer care varies widely. These findings were recently summarized by Malin and colleagues.103 A key element of these findings is that despite the existence of long-standing, well-established treatment guidelines for patients with early-stage disease, there is considerable variation in the use of recommended treatments, including BCS followed by local radiation therapy, hormone therapy in women who are receptor positive, and axillary node dissection. The data also suggest that the use of BCS has increased somewhat over the last 2 decades in many areas of the United States, and the overall use of radiation therapy following BCS appears to be approaching a more acceptable level.108-115 However, there is still marked variation in the use of BCS, not only between states, but between cities within the same state, and as described above, even between hospitals in the same city that are part of the same hospital system.108,116 In more recent studies (women diagnosed since 1991), use of BCS varied from a low of 20.7% in the state of Florida to a high of 74% in Massachusetts.110,117 Gregorio and colleagues found that within the state of Connecticut, the use of BCS for early stage-disease varied from 49.6% to 72.7% for different metropolitan areas within the same state. In their study of 4 relatively large New York City hospitals, Bickell and colleagues found that the rate of BCS use for patients with early-stage disease varied from 49% to 69%.

Sociodemographic Factors That Influence Access to Quality Care: Age, Race, and Insurance Status Given the tremendous impact that quality of cancer care may have on clinical outcome, any factor that affects one’s ability to access quality care is likely to impact on clinical outcome. Nonclinical patient characteristics such as age, race or ethnicity, and socioeconomic status have all been demonstrated to affect a patient’s access to quality cancer care. Older age, nonwhite race, and being under- or uninsured have all been demonstrated to place a patient at increased risk of less aggressive treatment and to increase the likelihood that treatment will deviate from Curr Probl Surg, September 2003

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recommended guidelines.23,59,86,118 These factors have also been associated with shorter time until death due to cancer.23,59,118,119 Some of these differences in survival rates may be attributed to the fact that the elderly, African-Americans, certain other minorities, and individuals who are uninsured or have Medicaid are significantly more likely to be diagnosed with later-stage disease than for other groups.117,119-122 However, not all the disparities in care and survival rates are related to differences in the stage at presentation and access to care. The disparities in care between various racial and ethnic groups were the subject of another recent IOM report entitled, “Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care.”123 Differences in Stage at Diagnosis by Age, Race, and Insurance Status. Bradley and colleagues studied 51,296 Michigan adults diagnosed with cancer of the breast, cervix, colon and rectum, lung, or prostate between 1996 and 1998.119 These investigators found that individuals younger than 65 years who were insured by Medicaid had the greatest risk of late-stage diagnosis across all 5 disease sites analyzed. In Florida, Roetzheim and colleagues found that women with breast cancer who were uninsured or insured by Medicaid had higher mortality rates as a result of later stage at diagnosis than women with health maintenance organization (HMO) or private fee-for-service insurance.117 Similarly, a large, population-based study of New Jersey women (N ⫽ 4675) diagnosed with invasive breast cancer between 1985 and 1987 demonstrated that those younger than 65 years with Medicaid or no insurance were significantly more likely to present with advanced disease than women with private insurance (P ⬍ 0.01).120 These investigators also found that women with private insurance were more likely to have a primary care physician, and this in turn was associated with the use of mammography. Insurance status has also been demonstrated to impact on the use of mammography screening among older women. Those patients on Medicare who do not have supplemental insurance are significantly less likely to undergo mammography than those with some type of supplemental insurance.124 The author concluded that requiring co-payments for preventive services was an obstacle to the effective mass screening of older women for breast cancer. Results from the 1992 National Health Interview Survey demonstrated a similar phenomenon for Medicareinsured adults older than 65 years with versus without supplemental insurance for the use of 5 of 6 different screening tests studied (eg, mammography, clinical breast examination, Papanicolaou test, fecal occult blood test, and digital rectal examination) These investigators also 558

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found that fecal occult blood test screening in the elderly was significantly more frequent for individuals in managed care plans.115 Some authors have suggested that differences in the stage at diagnosis by race are probably due to differences in SES, as reflected by insurance status. However, although differences in insurance status may explain some of the relationship between race and stage at diagnosis, there is evidence to suggest that race also has an influence on the stage at diagnosis quite separate from that caused by the association between race and insurance status or SES. In a study about the effects of health insurance and race on early detection of cancer in Florida residents during 1994, Roetzheim and colleagues found that after adjusting for age, sex, marital status, education, income, and co-morbidity, individuals who were uninsured or had Medicaid were significantly more likely to be diagnosed at a late stage of disease for colorectal cancer (odds ratio [OR] ⫽ 1.67, P ⫽ 0.004), melanoma (OR ⫽ 2.50, P ⫽ 0.004), breast cancer (OR ⫽ 1.43, P ⫽ 0.001), and prostate cancer (OR ⫽ 1.47, P ⫽ 0.02) than individuals with commercial indemnity insurance.125 However, they also found racial differences in stage at diagnosis that could not be explained by insurance coverage or SES, with non-Hispanic African-Americans significantly more likely to be diagnosed with late-stage breast and prostate cancers than non-Hispanic whites. This relationship has also been noted for patients with endometrial cancer, a site for which there is no effective mass screening test.121 The NCDB Report on Endometrial Cancer in African-American Women described differences in case presentation between 52,307 non-Hispanic white women and 3226 African-American women diagnosed with primary carcinoma of the endometrium between 1988 and 1994.121 This report noted that more African-American patients were diagnosed with less favorable histologic findings than white patients and at more advanced stages of disease. Income did not appear to account for differences in the grade or stage at diagnosis.

Differences in Treatment and Survival According to Age, Race, and Insurance Status Race, insurance status, and age also have been associated with differences in treatment and long-term survival rates. In the study by Ayanian and colleagues described above, the authors found that in addition to differences in stage at diagnosis, survival rates were worse among women with local and regional disease who did not have private insurance, and that race, number of co-morbidities, and age were all associated with the patient’s type of insurance.120 Similarly, although the Michigan study by Curr Probl Surg, September 2003

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Bradley and colleagues reported that insurance status was associated with stage at diagnosis for cancers of the prostate, breast, colon and rectum, and melanoma, these investigators also found that when the stage at diagnosis and demographic characteristics were held constant, patients younger than 65 years who were insured by Medicaid also had a greater risk of death from cancer for all sites, with the exception of cancer of the prostate.119 These data suggest that although individuals receiving Medicaid are more likely to be diagnosed with later-stage disease, the association between insurance status and stage at diagnosis cannot totally explain the higher death rates found among those insured by Medicaid. In their study of 723 women with early-stage breast cancer, Bickell and colleagues found that after adjusting for patient case mix, hospital, race, age, and other characteristics that could affect choice of treatment, women with Medicaid or no insurance were at significantly increased risk of not receiving efficacious radiation (no insurance, adjusted OR ⫽ 4.3, P ⬍ 0.01) and systemic treatment (Medicaid, adjusted OR ⫽ 1.9, P ⬍ 0.01).108 The authors suggest that their data provide 1 possible explanation for the findings of Ayanian and colleagues and others of lower long-term breast cancer survival rates for women who are uninsured or on Medicaid.119,120 The association between insurance status and treatment has also been demonstrated for other types of cancer. For example, Greenberg and colleagues demonstrated that individuals without private insurance are significantly less likely to receive operations for non–smallcell lung cancer than patients with private insurance.126 Mitchell and colleagues found that access to bone marrow transplantation for the treatment of leukemia and lymphoma was also affected by type of insurance in the 4 states that were studied, with patients on Medicaid, HMO, or the uninsured less likely to receive transplantation than those with Blue Cross or private insurance.127 The association between age and treatment choice has been linked both to insurance status (Medicare) and to the increasing number of comorbidities encountered with advancing age. Edge and colleagues found that among women 67 years and older who were diagnosed with stage I or II breast cancer, increasing age, lower functional status, and greater surgeon training were associated with decreasing odds of undergoing axillary lymph node dissection.128 In addition to their findings concerning Medicaid and treatment, Bickell and colleagues noted that women on Medicare were less likely to receive radiation therapy after BCS, a trend that has also been reported by others, and noted that this might have been due to the association of older age with Medicare status.108 Other investigators have reported similar findings regarding less frequent use of 560

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radiation therapy and also chemotherapy in postmenopausal women than in younger women, presumably because of the toxicity associated with radiation treatment and chemotherapy combined with the greater likelihood of co-morbidity in postmenopausal women.104,110 However, in all 3 of these studies, older women tested and found to be estrogen receptor positive were also less likely to receive systemic hormone therapy, a recommended treatment for early-stage disease in postmenopausal women that is not associated with adverse side effects.104,108,110 Similar differences in the treatment of older patients have also been noted for other types of cancer, including cancers of the lung and colon and rectum, with a tendency to treat older patients less aggressively than younger patients.86,129 Although there are frequently valid medical reasons for less aggressive treatment of older individuals, in some instances less aggressive treatment appears to be due to a lack of knowledge concerning the potential effects of chemotherapy or other treatment in the elderly due to a lack of clinical trials involving a sufficient number of elderly patients to provide reliable data. As stated by Mandelblatt and colleagues, “Cancer is a disease of old age, yet there is limited research on barriers to cancer screening, treatment, and posttreatment care among the elderly.” Clearly, this issue must be addressed.23 Perhaps the most compelling data concerning differences in access to quality of care and subsequent survival rates are those associated with race. According to the ACS, African-Americans have a 33% increased risk of dying of cancer compared with whites, and data from the NCI SEER Program demonstrate that age-adjusted cancer incidence is higher among African-Americans than any other group.130 For lung, colorectal, and prostate cancers, there are lower incidence and mortality rates for whites versus blacks and higher 5-year survival rates. The differences in incidence and mortality rates according to race are particularly striking for prostate cancer, with black men more than 1.5 times more likely to develop prostate cancer than white men and more than twice as likely to die from it. Equally disturbing are the data concerning female cancer of the breast. Despite the fact that there is a higher incidence of breast cancer among white women than black women, annual mortality rates are higher among black women, and 5-year survival rates are lower.121,131 A recent review by Shavers and Brown entitled “Racial and Ethnic Disparities in the Receipt of Cancer Treatment” provides a comprehensive, well-written review of the recent literature concerning the association between race, treatment, and outcome for several cancer sites.59 This review summarizes the results of several studies concerning the relationship between race and quality of cancer care. These investigators and others present Curr Probl Surg, September 2003

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data from several studies that document differences in the type of treatment received and the clinical outcome according to race, even after controlling for the effects of stage at diagnosis and differences in insurance status.59,86,121,131-133 Based on their review, Shaver and Brown suggest the need to “develop strategies to facilitate receipt of appropriate cancer care.” Hicks and colleagues reached a similar conclusion based on the results of the NCDB Report on Endometrial Cancer in AfricanAmerican Women, in which they recommend “. . . All patients, regardless of race, should be treated appropriately by medical and prognostic factors and not by race.”121

Quality Improvement It is clear that there are major quality problems in cancer care. Some quality problems stem from issues associated with surgeon expertise and experience. For each disease site, there are factors associated with surgeon training, specialization, and case volume that affect quality and outcome. In addition, it is clear from the above discussion that many quality problems are also rooted in the organization of the healthcare system. The following section suggests possible mechanisms to address quality problems, including provider-related issues.

Factors That Impact on Our Ability to Provide Quality Care The success of our efforts to improve quality of care and even the definition of success vary according to cancer site, geographic location, time period, and other circumstances. However, the ultimate success of efforts to improve quality of care for a given cancer site or in a particular location is almost always related to whether or not the underlying factors that impact on the quality of care and the relative importance of these factors have been identified correctly. Therefore, when planning an intervention that is designed to improve quality of care, it is first necessary to identify the barriers to quality improvement that are preventing patients from receiving the best possible quality of care. Lack of Information. One possibility is that the data necessary to assess the care provided are not available, and the research necessary to utilize this information has not been conducted. Lack of Resources. There may be a lack of funding, or the data necessary to conduct the research is not readily accessible (ie, the relative lack of large, population-based studies concerning the relationship between process of care). 562

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Failure to Utilize Available Resources (ie, Funding or Data). Another possibility is the failure to utilize available resources to utilize available resources as a result of political concerns (ie, the fact that only a few researchers have successfully established the necessary collaborations to utilize insurance data to study quality of cancer care). Insufficient Attention to Quality Evaluation (Eg, Apparently Limited Interest in Evaluation of the Quality of Ovarian Cancer Care). Insufficient attention to quality evaluation is yet another possibility. Failure to Effectively Utilize Available Information. The information necessary to plan, implement, and evaluate an intervention that could improve quality of care may be available, but this information has not been utilized. Inadequate Communication. The information that is available may not have been communicated adequately to the institutions, agencies, surgeons, or patients who need the information (ie, an apparent failure to communicate the importance of surgical resection techniques, such as positive CRM in reducing the risk of local tumor recurrence and improving the odds of long-term survival in patients with rectal cancer). Resistance to Change. The available information may have been communicated, but individuals (either healthcare providers and/or patients) are resistant to change (ie, failure to adhere to established guidelines for the treatment of early-stage breast cancer). Lack of Financial, Human, or Institutional Resources. The available information may have been communicated, but 1 or more of the resources necessary to develop, implement, and evaluate the success of an intervention based on that information are lacking (ie, failure to implement clinical care pathways in the hospital setting due to lack of sufficient resources to implement the necessary program of training and evaluation). Lack of Understanding. An individual healthcare provider may lack a complete understanding of his or her own attitudes and beliefs and the subtle ways in which these attitudes and beliefs may affect interactions with a particular patient and/or the treatment choices that are recommended for that patient (ie, studies demonstrate that surgeons are more likely to treat the cancers of elderly and African-Americans patients less aggressively than those of younger and/or white patients, even when there is no specific medical justification for doing so). Physical, financial, and emotional factors may also impact on a patient’s ability to access and comply with the care that we as surgeons are willing and able to provide. Structural Barriers. The existing health system is designed in some regards for failure. There is no universal mechanism to collect and utilize Curr Probl Surg, September 2003

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clinical information about cancer patients, including day-to-day medical care data such as laboratory, pathology, and radiology reports. Providers often cannot access information on their practices and on the outcomes of patients they treat. There are few programs that assist patients in navigation of the healthcare system or that assist patients in followthrough with beneficial therapies. A conceptual framework for redesign of the healthcare processes was provided by the IOM in its report on “Crossing the Quality Chasm.”7,134 The system must be patient centered, providing patients the information and support to make choices in health care, and to address patient needs. Decision-making must be patient centered and evidence based. Mechanisms must be in place to ensure safety, including communication to prevent errors. Physicians must collaborate in the redesign of processes to enhance care. Quality improvement must be focused at 4 levels within the health system. These include patients’ experience, “microsystems” where care is delivered (eg, physician offices), the organizations that support these microsystems, and the environment of laws, rules, payment, and training.134 To allow these measures to be effective, the organization of the healthcare system itself must undergo radical change. The IOM recently concluded that, “The health care delivery system is incapable of meeting the present, let alone the future needs of the American public.”8 Although there are a few notable, integrated healthcare systems that provide comprehensive, coordinated care, the majority of patients in America receive cancer care from an ad hoc network of providers, with no central oversight, medical record system, data collection system, or other relationship. Immediate needs include enhanced clinical informatics systems to enhance communication, access to patient information, medical information to guide treatment, and decision support. Another substantial barrier to change is the legal climate that holds individual providers culpable for failings of the healthcare system. Although most providers are committed to enhancing patient outcomes and are eager to participate in real quality improvement, they are reasonably concerned that they will be held personally liable for past quality issues, many of which are the result of systems failures. Other industries have addressed this personal liability issue by establishing mechanisms for reporting errors and quality issues that protect the individual from personal liability. A notable example is the airline industry, where reporting of adverse events results in analysis of the root cause of problems rather than sanctioning of the individual pilot or 564

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controller. Such root cause analysis is slowly finding its way into medical care. Major reform in the system of error reporting in medicine, including tort reform, will be critical to the success of quality improvement.8

Principles of Quality Improvement Quality improvement at the provider and patient care level is an ongoing process that generally results from a cyclical mechanism of defining standards, measuring care, using the information to alter practice, and refining standards of care. Mechanisms that can be used to alter practice and improve the quality of cancer care range from continuing education, to administrative changes that define by whom and where certain conditions can be treated. Although administrative changes such as regionalization may be warranted in limited circumstances, such as pancreatectomy, most cancer care will not be regionalized or centralized, and therefore, quality improvement must be constructive and include all providers and institutions. National solutions are needed so that all Americans have access to high-quality care. Toward this end, most national organizations involved in cancer care are developing quality initiatives. In addition, there is proposed legislation in Congress that will promote quality enhancement at the national level (S.2965, September 2002). Introduced by Senators Kennedy and Frist, the Quality of Care for Individuals with Cancer Act addresses many of the measures suggested by the NCPB in 1999.3 It increases support for national data collection, channeled to a large extent through the NPCR and SEER population registry programs, requires entities using federal funds to establish and implement core sets of quality measures, and provides support for research in quality enhancement. Although this national commitment is critical to the quality improvement initiative, especially at the data collection phase, success in quality improvement will require implementation at the local level.135 A key element to any quality enhancement program is local provider participation and commitment. Because the organization of health care varies among communities, implementing national policy first requires local solutions. There are a limited number of successful quality improvement models. The most effective programs have been developed in integrated healthcare systems. One of the most notable quality initiatives is the quality management program and surgical care evaluation programs of the VA healthcare system.50,51 Documenting quality enhancement on a community-wide basis is more difficult because often the involved parties have no business relationship, and in many cases are business competitors. Curr Probl Surg, September 2003

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This makes collaboration in a community-wide quality enhancement model counterproductive. In choosing the right type of intervention to improve quality care, there are several factors that warrant consideration. These factors include the ability to provide quality of care for a particular cancer site, the barriers that impede the ability to achieve quality cancer care, such as the availability of information and resources, and the obstacles that prevent equal access to quality care for certain subpopulations of patients. The variety of problems that have been identified and the differences in resources available in various communities and for various types of cancer suggest that several different solutions will be necessary, and solutions will vary according to each community’s resources and needs. Although many options have been discussed, some of which have been adopted successfully for other diseases, our review of the literature demonstrates that the level of effort and the type of interventions that have been attempted to improve quality of care vary markedly according to cancer site and geographic location. Furthermore, successful efforts to improve quality of care have been hampered severely by a lack of information and understanding, narrowly focused research efforts, poor communication of available information, and resistance to change.

Interventions Available to Improve Quality Quality improvement interventions generally fall into 1 of 6 categories: provider education, feedback of practice and quality data, active participation of physicians in efforts to change, administrative rules, financial incentives, and financial penalties.136 The intervention must be tailored to individual quality problems and diseases. Solutions to ensure high-quality care for high-risk operations that may be restricted to certain providers will differ from interventions to assure uniform high-quality care for screening and treatment of high-incidence conditions with low morbidity rates in which all providers will participate. It is likely that successful quality improvement programs will be hybrids that encompasses different classes of measures. Comprehensive programs will likely include education, data feedback to providers; report cards for employers, payers, and the public; and administrative and legislative measures. Finally, implementation of quality improvement measures may require financial incentives or penalties. Reliance on altruism or legislation may be insufficient to induce health systems and providers to change. Education. Education for providers is a key to improving quality. Despite the rapid advance of medical knowledge and technology, there 566

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are limited requirements for formal education after the completion of medical training. Unfortunately, traditional techniques of continuing medical education have been demonstrated to have little impact on practice. Practice guidelines, which have proliferated over the last decade, are another means of provider education. However, it is clear that passive distribution of guidelines has little impact on quality of care. For example, in 1990 the NCI disseminated the results of its consensus development panel on the use of BCS. This had little impact on overall utilization of BCS.137,138 It is therefore unlikely that quality will be improved definitively by simply offering educational services to providers. Defining Practice Standards and Providing Feedback Practice Feedback. Codifying practice standards and distributing them to physicians are mechanisms to address community-wide quality. This may include feedback of information on community-wide aggregate and physician-specific data on the quality of care. Assuming that the data are available, the impact of the programs will depend on the means used to distribute them. Options include written report cards, community opinion leaders to communicate standards and results to the entire provider community, and targeting of providers with outlier quality performance for “academic detailing.” In such a program, outliers are contacted by the quality team of the sponsoring organization to review quality results and examine potential solutions. This team may include opinion leader physicians, the chief of staff of their hospital, or specially trained education teams. In addition, programs may include reporting quality data to the public. There are very few data in the literature to demonstrate the success of this strategy. A program in Minnesota and Massachusetts used community opinion leaders successfully to address breast cancer surgery issues.110 A similar program based on the leadership of the chiefs of surgery at hospitals in Rhode Island failed to demonstrate any change in patterns of breast cancer care.44 Report Cards. Provider and institution report cards are another method to provide feedback to providers to influence care. In addition, report cards may be given to payers, to others who pay for care to allow them to select high-quality providers, and to the public. Report cards require information on care in the entire community. Models using report cards have generally been limited to measures such as hospital mortality rates for certain procedures (eg, cardiac surgery). The provider unit for profiling with report cards may not be the individual physician. One study in the treatment of diabetes suggests that the best unit for profiling may be groups of providers or institutions.139 Curr Probl Surg, September 2003

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There are limited data to validate that institutions identified by quality measures in report cards actually provide globally higher quality care. Some data suggest that report cards may not discriminate between highand low-quality institutions.140 Furthermore, report cards are not timely and do not account for quality of processes of care.141 Therefore, providers are advised to participate in comprehensive quality improvement, beyond the scope of relatively simplified report cards.142 This should include documentation and reporting of process measures and outcomes. Practice Guidelines. Practice guidelines are defined by the IOM as “systematically developed statements to assist practitioner and patient decisions about appropriate health care for specific circumstances.”27,143 Guidelines may be used as an educational tool to providers and, as reviewed above, may be a source of quality indicators for cancer care. However, there are limited data on the impact of guidelines on the quality of medical practice in general and of cancer care in particular.33,144 A major ongoing initiative to address quality of care is the guideline program of the Province of Ontario. Under this program, guidelines for many cancer conditions have been established.31,145-147 The guidelines are detailed and evidence based to the greatest extent possible, and physicians in practice are directly asked to provide feedback to the guideline development committees. They are currently working to implement data collection programs to measure the impact of these guidelines.148-150 To date, there is little information on the impact of this program on outcome and practice. Another program using guidelines and related data collection for quality feedback is the Outcomes Project of the NCCN.47 The NCCN has the most comprehensive set of practice guidelines in oncology, covering all aspects of care in virtually all cancer types. The NCCN Outcomes Project is working to close the guideline cycle loop by collecting treatment information and feeding that data back to providers and the guideline development teams. Care Pathways. Care pathways are another tool to enhance quality. Pathways differ from guidelines in that pathways provide step-by-step methods to operationalize guidelines. Care pathways have generally been implemented within hospital systems for the care of major surgical procedures associated with high cost and length of stay, such as pancreatectomy and esophagectomy. Implementation of pathways has been demonstrated to improve quality, optimize resource utilization, and reduce the cost of care.151-153 In addition, the data developed from care pathways can be used as feedback to providers in an institution to alter 568

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behavior and reduce variation in care while enhancing outcome and reducing cost.154 Administrative and Structural Changes. Quality improvement will require changes in the healthcare system and changes in policies and practices at the health system and community levels. One major issue is the need for informatics systems to assure that health data are readily available to all physicians and to support implementation of care pathways and guidelines and collection of data for quality assessment. Other needed changes are mechanisms for root cause analysis to examine care problems in a nonpunitive fashion. Community-wide or nationwide administrative changes may include extending health coverage to all individuals, implementation of community-wide pathways and guidelines, and in some circumstances, regionalization of care to high-quality and high-volume centers. The best examples of a health system comprehensive change for quality improvement are in the federal hospital system. Over the last decade, the VA has implemented state-of-the-art electronic records and order entry systems and extensive systems for data collection on all surgical procedures and related morbidity rates.50 Similarly, the Department of Defense is enacting system-wide programs for implementation of practice guidelines and data collection for cancer care.155 Although such sweeping changes are difficult to implement in private systems, the success of these programs contributes valuable lessons and experience. Health systems that employ providers may have the greatest opportunity to enact such changes. Regionalization of Care. Regionalization of care is appropriate in some circumstances, such as high-risk surgery. Indeed, the NCPB specifically called for such regionalization as 1 of its recommendations. This strategy should also be readily accepted by most providers. They may initially respond with the argument that regionalization is a threat to the autonomy of physicians and patients. However, most physicians should readily acknowledge the compelling data that support this measure. In addition, taking such resource-intense procedures away from the low-volume surgeons and hospitals will not have any significant financial ramifications. Indeed, for a hospital, it may be cost-effective to refer such patients because of the extraordinarily high cost of care associated with complications of these procedures.156 Regionalization will not be an appropriate solution for all cancer care. For high-incidence cancers for which there is low treatment-related morbidity, regionalization to centers may itself pose major barriers to quality. For example, breast cancer is a common disease treated in all Curr Probl Surg, September 2003

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medical settings. Many women with breast cancer are older and may have limited resources to travel. Furthermore, requiring women to travel for such a common service as mammography or breast biopsy may lead some women to forego that service. An example of the potential negative impact of centralization is the inverse relationship of the distance between a woman’s residence and a radiation oncology facility and the use of BCS for breast cancer.114 Existing centers may also not have the capacity to accommodate the high volume of care of breast and colon cancer. Finally, the evidence regarding these cancers suggests that the major factors that affect quality and outcome are related to decision-making and access to comprehensive postsurgical care, not procedure-related morbidity. Regionalization of low-risk surgery may not address this issue. Indeed, system-wide changes to collect information on care, provide support to community physicians through appropriate case management, and institute a “virtual cancer center” may accomplish more than regionalization to improve quality. Although it is not appropriate for every type of cancer surgery, regionalization is necessary in some instances. A major effort to regionalize care for high-risk medical procedures has been initiated by an employer consortium called the Leapfrog Group.9 This is an employerbased consortium of private and public groups that provide health benefits to as many as 33 million consumers. They are mobilizing the purchasing power of employers to reduce unnecessary deaths from high-risk procedures performed in low-quality settings. The Leapfrog Group is working to regionalize care for its members for 5 conditions, including cardiac and vascular surgery. In oncology, the only procedure included in this initiative is esophagectomy. They propose to limit these procedures to centers that perform substantial volumes of cases and that maintain other systems to promote quality and reduce error. These include the use of physician computer order entry and the presence of full-time, intensivecare physician staffing. Trauma and cardiac surgery are both examples of surgeons regionalizing care with clearly enhanced quality.157,158 Through its requirement for a certificate of need for cardiac services, New York State effectively regionalizes cardiac care. To complement this process, New York implemented a statewide system to collect treatment risk factors and outcome (mortality rates) for all patients undergoing cardiac procedures.159 The program provides hospital and physician-specific riskadjusted mortality data to hospitals for quality improvement and to the public on an annual basis. Since the implementation of this program in the 570

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early 1990s, the overall mortality rate for cardiac surgery and the variability in outcome between providers has improved substantially.159 To some degree, referral patterns and market forces have already accomplished regionalization for high-risk surgery. To determine the extent of regionalization, we examined publicly available data regarding the number of operations for cancers of the pancreas and esophagus in 8 different states for which data were available to us (9 states for pancreatectomy). For each of these states, we used the most recent case data available to determine how many cancer operations were performed in each state per year and at which hospitals within the state for the same time period. Data were obtained from sources available to the public from Health Care Choices, Inc. The data are published on the World Wide Web at www.healthcarechoices.com. This group obtained data from each state health department on surgical procedures for cardiac conditions and several cancers, including cancer of the pancreas, esophagus, colon, and breast. These data are likely a reasonable estimate of the number of such cases. However, identification of a case depended on accurate discharge coding using ICD-9 diagnosis and procedure codes. For pancreatectomy, only procedures coded as radical pancreaticoduodenectomy or total pancreatectomy were captured. Lesser pancreas operations or operations improperly coded with unbundled codes may not have been identified. Therefore, although these data demonstrate the overall trends in care, it is possible that they understate the total number of cases. The accuracy of the data may also vary by state, depending on the completeness of data collection in each state. Data on the population density and the type and number of hospitals available to individuals in that state are provided in Table 8. Iowa is the most rural of the states. For cancer of the pancreas, the median number of cancer resections per hospital in the year studied was fewer than 2. In each state, there were only a few hospitals that reported more than 6 procedures per year. Three states had 1 hospital that performed almost 50% of the cases in the state. These data are similar to the findings from a recent study that used hospital discharge coding data from a national sample to examine hospital pancreas surgery volume and mortality rates. The average number of procedures performed annually at hospitals that perform these operations in the United States was 1.5.160 Esophageal cancer resection is specifically targeted in the Leapfrog Group initiative. Data for esophageal cancer resection were available for 8 states and are presented in Table 9. Of the hospitals that performed operations for esophageal cancer in these 8 states, 50% of hospitals in 6 of the states performed only 1 esophageal cancer operation per year. More Curr Probl Surg, September 2003

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TABLE 8. Population density and hospital availability in 9 selected states for which cancer surgery data are available

State

California Florida Iowa Maryland Massachusetts New Jersey New York Washington Wisconsin

Population density Number of Population, (number general adult Year 2000 of acute care people/ hospitals sq. mi.) 33,871,648 15,982,378 2,926,324 5,296,486 6,349,097 8,414,350 18,976,457 5,894,121 5,363,675

217.2 296.4 52.4 541.9 809.8 1134.4 401.9 88.6 98.8

433 198 78 47 71 91 254 87 121

Number of critical access hospitals

Number of comprehensive cancer centers

12 8 43 0 2 0 7 19 25

6 1 1 1 1 1 4 1 1

Source: State Health Department data reported at www.ahd.com; http://quickfacts.census.gov/ qfd/states/. TABLE 9. Volume surgery per hospital for esophagus cancer in selected states Hospital case volume*

State

California 1998 Florida 1999 Iowa 2000 Maryland 1999 New Jersey 2000 New York 1999 Washington 7/97-6/98 Wisconsin 2000

% of cases % of cases Number Number Number of at hospitals at of of cases per 50th percenRange performing hospitals cases hospitals hospital tile <6 cases/ performing y‡ 1 cases/y 190 108 16 60 23 140 35

86 51 7 18 15 47 18

2.21 2.11 2.28 3.33 1.53 2.98 1.94

1 1 1 ⬍6 1 2 1

1-17 1-11 1-10 ⬍6-16 1-7 1-34 1-9

70.5 77.8 37.5 58.3 69.6 52.8 74.3

25.9 16.8 21.4 ⬍9.8 26.1 8.3 17.9

42

24

1.75

1

1-6

85.7

21.4

*Cases per year per hospital. ‡ This category includes cases from hospitals performing ⱕ1 case per year. Data from State Health Departments data as reported at www.heatlhcarechoices.com (169)

than 20% of all esophageal cancer operations in the states of California, Iowa, New Jersey, and Wisconsin were performed at hospitals that reported only 1 esophageal cancer operation per year. In Maryland and New York, fewer than 10% of operations took place in hospitals that reported only 1 operation per year. The Leapfrog Group recommends that hospitals perform at least 6 esophageal procedures per year. These data suggest that with regard to esophageal cancer surgery, the majority of 572

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resections continue to be performed at low-volume hospitals. The rural state of Iowa is the only state listed where more than one half of the esophageal cancer operations were performed at facilities that met the Leapfrog Guidelines. The quality issue with those cancers associated with high-risk surgery may not be limited to the issue of surgical care. The majority of patients with these diseases have tumors that are not amenable to resection. There is little literature on the quality of care provided to those patients who have disease not amenable to resection. However, it is reasonable to envision that treatment will vary widely, including varied use of systemic, radiation, and palliative therapies. Therefore, in conjunction with programs to regionalize surgery for these diseases, it will be imperative that programs are developed to evaluate and assure quality care for all patients with these diseases. This must include mechanisms for referral into specialty centers and back to their local community for aspects of nonsurgical care. Such programs will need to incorporate case management, case review, clinical trials, and palliative and end-of-life care. Achieving regionalization will require positive actions by regulatory agencies or by parties responsible for payment. Ultimately, it is likely to be financial incentives or requirements that result in referral of care to high-volume centers. This may be best engineered through payers who should require that such procedures only be performed at centers with demonstrated outcomes and quality systems. Legislative action may also be necessary to regionalize this care. Legislative Efforts to Improve Quality. Legislation may be one of the only ways to assure that critical elements of quality initiatives affect all patients. Data collection systems that are legislative mandates are one example. Data collection on a community-wide basis is very difficult and may be adversely affected by the program of the HIPAA. Legislatively mandated population registries, enhanced to impact quality evaluation, will play a major role in quality evaluation. An example of a legislated change in practice that has effectively improved quality of cancer care is the enhancement of mammography under the Mammography Quality Standards Act (MQSA).161 Mammography providers are required to meet specific quality standards to qualify for reimbursement by HCFA (eg, Medicare). Standards include adequacy of equipment, volume requirements, and ongoing internal auditing of results. Although this has not corrected all problems with mammography care in America, MQSA has clearly raised the standard received by all women undergoing mammography and without affecting access to mammography services.162,163 Curr Probl Surg, September 2003

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Conversely, other legislative efforts may have little value or, if enacted without careful thought, may even be detrimental. An example of legislative mandates that have had little impact on quality is the requirement to inform women regarding choices of BCS versus mastectomy. Several states adopted such measures in the late 1980s in response to the emerging evidence regarding BCS. Unfortunately, these measures had little impact on surgical practice.164 Expansion of the healthcare payment system to include all Americans is another area where legislation is critical to quality improvement. The lack of health payment coverage for more than 40 million Americans is arguably one of the primary barriers to quality outcome for cancer care, resulting in higher costs and mortality rates. This problem hits hardest in minority and socioeconomically disadvantaged groups. A bill to address the quality of cancer care was recently introduced in Congress by Senators Kennedy and Frist. This bill provides substantial support for data collection and development of quality improvement programs and will require the use of quality measures by all programs that receive federal funds. Given the bipartisan support for this bill in Congress, there is reasonable hope this bill will pass in this legislative session.

Research Surgeons must act individually and collectively to make meaningful changes to improve cancer care. Surgeons have demonstrated leadership in cancer care for more than a century. They were the first to recognize the need for and establish a system for community-wide cataloging and assessment of cancer care. Surgeons are often the first point of interaction for patients with cancer and are often responsible for making the initial diagnosis and defining the initial course of action that may make the most important contribution to quality outcome and survival. These same steps of cancer care may be the most at risk for variation in quality and where suboptimal care is immediately evident. Surgeons are uniquely qualified to define quality standards and to establish mechanisms to evaluate and enhance quality. Segments of the surgical community have already developed quality enhancement programs that have markedly improved the care for the whole population, most notably in trauma and cardiac care. Certain subspecialties have also limited care to a handful of specialists in cancer care, notably in gynecologic oncology. Surgeons therefore are well positioned to develop care models for common cancers, including breast, colorectal, and lung cancer, as well as for those diseases associated with high-risk procedures. 574

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Surgeons must work collaboratively with other parties interested in quality enhancement. It is clear that the quality of care has become a national priority. Therefore, in addition to providers from the other oncologic disciplines, surgeons must work with payers, employer groups, consumers, advocates, and government. This work will need to be done at the local, regional, and national level. Functions such as setting priorities, defining and validating quality measures, and providing additional funding for quality initiatives will require national action. However, implementation will require local and regional action that engage all affected parties. Successful programs will include providers and local opinion leaders. Furthermore, differences in local health system organization, mechanisms of payment, and geography will need to be resolved with local solutions. The issue of the quality of cancer care has clearly been recognized by national surgical groups, including the ACoS and the Society of Surgical Oncology. The College has reorganized the CoC specifically around the issue of quality of care and is using the NCDB to evaluate and improve care.46

Recommendations 1. Research: There is a need for additional research into the best means of measuring and improving quality and into factors that affect the quality of care. a) Increased research is needed for moderate-risk cancers with high associated mortality rates where there has been a paucity of studies evaluating the quality of care and its impact on outcome, including lung cancer, hepatic resection, gastrectomy, and ovarian cancer. In addition, attention should be paid to the treatment of uncommon cancer such as sarcoma. b) Creative use of existing databases should be fostered, and there should be increased efforts to develop new sources of data and thorough case identification. c) Additional population-based studies that utilize hospital discharge data to conduct studies of morbidity rates and long-term operative complications, such as those of Begg and colleagues (for prostate cancer) and Sosa and colleagues (for thyroidectomy), could provide much-needed information on the impact of treatment-related morbidity on the patients’ quality of life.77,78 Efforts to establish collaborative endeavors with insurance companies and to link insurance claims data with state tumor registry data and Social Security death index data would greatly improve our ability to Curr Probl Surg, September 2003

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study the association between process of care and clinical outcome.38 Additional research comparing treatment and process of care factors in patients with multiple co-morbidities and highsurgical risk cancers, particularly the manner in which complications are addressed in the high-volume versus low-volume setting, could provide information that might be used to reduce mortality and morbidity rates in high surgical risk patients. d) Research about the mechanisms to evaluate the quality of care and to correlate the quality of care with outcomes is also needed. This includes validation of quality measures and research about the best use of these measures. e) Research is also needed regarding factors meaningful to patients beyond cancer recurrence and survival and into ways to enhance the quality of life, the subjective experience, and adjustment to cancer care. 2. Regulatory/legislative changes: The government is a key player in changing the healthcare system. Legislation is needed in several areas. a) Health coverage should be provided for the uninsured and underserved. i. Affordable universal health insurance programs should be established. ii. Co-pays for Medicare patients in need of cancer-related services should be reduced or eliminated, iii. Medicaid and uninsured patients should be assisted to ensure that they are not prevented from obtaining recommended cancer screening and diagnostic and treatment procedures due to financial hardship. b) The scope and penetration of data collection systems should be enhanced, and assurances should be made that cancer care data reporting is permitted under privacy standards. c) The reimbursement system for medical care should be changed to provide incentives for quality enhancement and quality care, including reimbursement for the cost of collecting and analyzing data on the quality of care. d) Lastly, tort reform should be enacted to minimize the personal liability of providers stemming from participation in quality assurance programs while providing appropriate compensation for injuries stemming from medical errors. 3. Address quality over the continuum of care: Cancer treatment begins at the first suspicion of the diagnosis. Steps early in diagnostic evaluation may influence the outcome and quality of life (eg, selection 576

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of biopsy technique for sarcomas or breast cancer may impact on organ preservation). Quality evaluation programs must include screening and diagnostic care, postsurgical care, follow-up surveillance, treatment of advanced disease, and end-of-life care. 4. Target high-risk groups for improved screening and early diagnosis efforts: The relationship between the stage at diagnosis and prognosis has been well documented, and the importance of efforts to increase screening and early diagnosis is understood by healthcare professionals. Special efforts should be made to reach underserved populations with screening programs and increase the routine use of primary care physicians for individuals in traditionally underserved populations. Efforts should also be made to remove the financial barriers to obtaining mammograms caused by co-pays for women with Medicare coverage who do not have supplemental insurance. Outreach programs to provide regularly scheduled opportunities for screening in rural areas should be encouraged. 5. Healthcare system reorganization. System-wide changes in healthcare systems are needed to measure quality and to ensure that all patients have access to quality programs. a) Health insurance coverage should be provided for all. b) Electronic information systems for medical care and surveillance should be improved, including the use of an electronic medical record. These systems should be community wide and should enhance the availability of clinical information, order entry, and communication between providers and provide enhanced data collection for case management, quality oversight, and population surveillance. c) Provider education should be increased. i. Our understanding and communication with patients from different educational and/or cultural backgrounds should be improved to ensure compliance with treatment recommendations. ii. There should be increased efforts to communicate treatment guidelines regarding the best quality care to the surgical community, and there should be better methods for evaluating the rate of diffusion and acceptance of innovations in the field via continuing medical education (CME) programs, journals, professional meetings, and other forms of media. iii. Increased training of general surgical residents and CME programs for surgeons regarding the importance of staging, Curr Probl Surg, September 2003

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proper surgical resection techniques, and recommended guidelines for treatment of cancer is also needed. d) The care for high-morbidity cancer surgery should be regionalized. The available data overwhelmingly demonstrate an urgent need to regionalize surgery for cancers with low incidence and high surgical risk, such as pancreatic cancer and esophageal cancer. Surgeons outside institutions with documented case volume and excellent outcomes should refer patients to these centers. Keying on the institution rather than case volume places responsibility on the institution to assure that the programmatic needs for comprehensive treatment of these patients are met and that the individual providers are qualified, well trained, and undergoing ongoing education. e) Care mechanisms should be improved to reduce the risk of mortality associated with certain patient clinical characteristics, including regionalization of surgery for moderate-risk surgery in patients at high risk because of these characteristics. There are certain clinical characteristics that have the potential to influence clinical outcome, regardless of the type of cancer or variables studied. These include the stage at diagnosis, the number and type of co-morbidities, and the nature of hospital admission (ie, elective, emergent, or urgent). Operative mortality and morbidity rates are increased significantly with increasing number of serious comorbidities and increasing urgency of admission; the long-term survival rate is decreased with increasing stage at diagnosis. In most of the studies we evaluated, these clinical characteristics were considered potential confounders and were treated as such in statistical analyses by adjusting for them using multivariate models. i. These data should be used to reduce the number of emergent and urgent cancer operations and increase the number of elective operations through outreach programs to high-risk patients and physicians.160 Despite the well-documented relationship between nature of admission and the risk of mortality for high surgical risk cancers, little attention has been given to the potential for reducing mortality and morbidity rates associated with treatment for cancer by attempting to increase the proportion of admissions/operations that are elective in nature as opposed to urgent or emergent. Increased efforts to communicate the importance of prompt scheduling of such operations to patients and their surgeons are extremely important. In addition, it is important to assist primary care physicians and 578

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other healthcare providers in contact with high-risk patients in the identification of such patients for referral before procedures must be performed emergently. ii. Patients at greatest risk of life-threatening complications as a result of surgery, the best techniques to reduce their mortality risk associated with particular types of complications, and patient co-morbidities should be identified. For more common cancers for which the risk increases with increasing number of co-morbidities, surgeons located at low-volume hospitals could choose to refer such patients to centers of excellence that are better prepared to respond to the inevitable complications that are likely to occur in such patients. Studies that suggest such a strategy might result in lower operative mortality rates for high surgical risk cancers include those of Choti and colleagues and Edge and colleagues.75,156 The study by Choti concerning hospital mortality rate associated with hepatic resection demonstrated a much lower mortality rate in the high-volume specialty center that performed approximately 44% of all these procedures for the state of Maryland. Choti and colleagues suggested that the facility’s newer techniques and equipment (ie, intraoperative ultrasonography, argon beam coagulation, ultrasonic dissection, and anatomic resectional techniques) were responsible for differences in mortality rates. Similarly, Edge and colleagues and Begg and colleagues suggest that the operative mortality rate associated with pancreatectomy is significantly lower at hospitals with cancer programs than at other hospitals.58,156 f) Clinical care pathways for surgical procedures and cancer care should be established at all hospitals and health systems and include mechanisms in conjunction with care pathways to collect cancer treatment data and to provide that information to physicians and other professionals. Care pathways should be community wide. Community-wide care pathways are especially important for the diagnosis and treatment of patients with high-incidence cancers with low-risk operations, who are likely to be treated in all practice settings, and can be used for the education of patients and primary and specialty providers and as a basis for data collection for quality assessment and case management. g) Multi-disciplinary teams should be established in all settings where cancer is treated. These teams need to work collaboratively to develop care pathways and in regular case review and ongoing Curr Probl Surg, September 2003

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quality assessment. When rural regions, small institutions, or unaffiliated providers in ambulatory settings are involved, this may require the establishment of “virtual cancer centers” and “virtual multi-disciplinary teams.” These teams should be directed by centers of excellence and involve the use of technical innovations, such as videoconferencing, to circumvent the problem of distance between centers, thus allowing the utilization of experts located in different geographic regions in multi-disciplinary teams. h) Useful mechanisms should be developed to report practice and quality data to providers, institutions, and the public. i. Surgeons must work with other national groups to establish meaningful quality measures and measurement and reporting systems. These data should be provided to individual providers and provider groups, hospitals, and payers for use in constructive programs for quality enhancement. It should be recognized that for common cancers associated with low to moderate treatment-related risk, the majority of care is likely to continue to occur in community practice, and information must be used to assist providers in improving care. ii. Quality information must be provided both to the public and to hospital systems. Public reporting of data is likely to meet with the most provider resistance, and no reporting system will be ideal. However, the public is entitled to meaningful information about the care in their community. The public, and particularly the informed consumer advocate, is likely to be the ally of quality cancer care providers and of parties involved in meaningful quality enhancement.

Conclusions: What Can Surgeons Do to Improve Quality of Care? The surgeon is usually the first specialist to treat a cancer patient. As such, a surgeon may have a major influence on a patient’s choices concerning adjuvant therapy and the nonsurgical healthcare providers who will be involved in that patient’s care. Furthermore, for most types of cancer, it is the surgeon, working in conjunction with the pathologist, who is responsible for ensuring that the disease is properly staged, which in turn determines the nonsurgical treatment a patient will ultimately receive. Therefore, as surgeons we have the power to influence not only the quality of the surgical treatment we provide but also every aspect of a cancer patient’s care. The ultimate goal of every healthcare provider involved in cancer care is to 580

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provide cancer patients with clinical interventions and other healthcare services that will maximize long-term survival while minimizing the negative impact of the disease and its treatment on quality of life. To accomplish this goal, we as surgeons must constantly strive not only to develop new treatments that are more effective with less associated morbidity but also to ensure that all patients, regardless of geographic location, age, race, or financial circumstance, have equal access to those treatments. What more can surgeons do to improve the quality of cancer care? Perhaps the best answer to this question was that provided by Fleshman in a recent editorial: “What more can we do? Academic surgeons can commit to training residents and community surgeons to perform the appropriate . . . cancer resection. . . . We can be relentless in our requests for adequate staging . . . by our pathologist. It is time to face issues such as centers of excellence or specialty practice regardless of site. Our goal of ‘excellence’ must be turned to action rather than empty words to fulfill our promise to provide outstanding care for our patients.”100

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