- Email: [email protected]
Enhancing Work Flow to Reduce Crowding
The Joint Commission Journal on Quality and Patient Safety
Enhancing Work Flow to Reduce Crowding Bruce Siegel, M.D., M.P.H. Marcia J. Wilson, M.B.A. Donna Sickler, M.P.H.
H
ospital crowding has emerged as one of the key challenges facing health care systems in the United States. Between 1994 and 2004, emergency department (ED) visits increased 18%, while the number of EDs decreased more than 12%.1 Not surprisingly, in 2006, 42% of all hospitals and 64% of urban hospitals reported going on ambulance diversion in a given year.2 In an earlier survey of hospital administrators, 90% reported that they had held or “boarded” patients in the ED for two or more hours awaiting an inpatient bed, and one in five hospitals reported boarding patients in the ED for eight hours or more on average.3 In recent years, crowding has received in-depth treatment by a number of authorities, culminating in the 2006 Institute of Medicine (IOM) report, Future of Emergency Care: Hospital Based Emergency Care at the Breaking Point.4 Crowding has been viewed at best as an inconvenience for patients who may be forced to wait hours to see a physician and even longer to be admitted to an inpatient bed. However, it can also erode working conditions and workflow in EDs that are already singularly susceptible to medical error.5 Patients are often unknown to ED caregivers, who must make decisions in relatively short time frames. Patients frequently present to the ED with nonspecific complaints such as “weakness” or “abdominal pain” that carry considerable clinical uncertainty and require extensive diagnostic examinations. Add to this the chaotic and pressured conditions that accompany crowding, and we are faced with a workplace fraught with the potential for November 2007
Article-at-a-Glance Introduction: Approximately one third of hospitals in the United States report increases in ambulance diversion in a given year, whereas up to half report crowded conditions in the emergency department (ED). In a recent national survey, 40% of hospital leaders viewed ED crowding as a symptom of workforce shortages. Many health systems are implementing a variety of strategies to improve flow and reduce crowding. Domains of Improvement: Virtually all work-flow initiatives use operations management techniques that include some or all of four domains: performance measurement, demand forecasting, flow redesign, and capacity management. These are often implemented using rapid improvement techniques. Most initiatives tend to focus on functional increases in inpatient capacity. Implications for Practice and Policy: Successful strategies to improve patient flow are distinguished by an organizationwide commitment to measurement, transparency in data reporting, and sustained management attention. Focusing on transitions between ED and inpatient units and maximizing overall hospital capacity appears necessary for improvement. Hence, reductions in ED crowding require strategies that go far beyond the ED. Conclusion: Health systems can take tangible, immediate steps to improve flow and reduce crowding. Efforts would be enhanced by more controlled trials of existing strategies in the context of uniform performance measures.
Volume 33 Number 11 Supplement
Copyright 2008 Joint Commission on Accreditation of Healthcare Organizations
57
The Joint Commission Journal on Quality and Patient Safety
poor outcomes. Clinicians and administrators link crowding to the workplace in other ways. In a recent national survey, 40% of hospital leaders viewed ED crowding as a symptom of workforce shortages.3 A chaotic work environment serves as a deterrent to recruitment and retention of the highly qualified professionals needed to staff the modern ED. In turn, these vacancies lead to a slower, overtaxed ED. Staff shortages and suboptimal patient flow in areas of the hospital outside the ED can also have significant ramifications for ED crowding. A well-functioning ED must have the ability to move admitted patients to other parts of the institution in a timely fashion. In the presence of any impediment to that flow throughout the entire institution, the ED may quickly become a backed-up reservoir, holding patients who continue to flow in, and unable to move the sickest, admitted patients out to appropriate care in other parts of the institution. The issue of crowding has gained greater visibility with the recent implementation of The Joint Commission’s first-ever patient flow standard.*6 Many health systems are currently implementing a wide variety of strategies to improve flow and reduce ED crowding. These undertakings range dramatically in underlying theory, focus, and scope. Through our role as the national coordinating office for a Robert Wood Johnson Foundation crowding project, Urgent Matters (http://www.urgentmatters.org/), we have overseen a major patient flow improvement collaborative and served as a clearinghouse for flow innovation since 2002. This has given us unparalleled insight into what specific hospitals are doing to address crowding and flow.
Domains of Improvement Most of the crowding initiatives we have seen are “homegrown”; they are planned and implemented by a single health system with little or no external assistance. Others are conducted in the context of national improvement programs such as the Urgent Matters Learning Network, the Institute for Healthcare Improvement IMPACT * Standard LD.3.15: “The leaders develop and implement plans to identify and mitigate impediments to efficient patient flow throughout the hospital.” (pp. LD11–LD-11a). The Joint Commission: 2007 Comprehensive Accreditation Manual for Hospitals: The Official Handbook. Oakbrook Terrace, IL: Joint Commission Resources, 2006.
58
November 2007
Network, and the University Health System Patient Flow Benchmarking Project. Although there is considerable heterogeneity in the design and outcomes of these initiatives, several common threads can be identified. Specifically, hospital-based initiatives to improve patient flow and reduce ED crowding generally attempt to enhance workflow in the care setting by focusing on one or more of four common domains of patient flow improvement—performance measurement, demand forecasting, flow redesign, and capacity management. These are often interdependent concepts, and initiatives benefit by the presence of all these domains. The first two domains, performance measurement and demand forecasting, may be critical elements of a foundation for change. However, they are both forms of assessment, and thus in isolation will not automatically lead to improvements in flow and reductions in crowding. Achieving these desired outcomes requires the addition of some form of redesign in work flow, which can take the form of the third and fourth domains: flow redesign or capacity management. In this section, each of these four domains is discussed in turn, each domain featuring an illustrative case study. However, one may assume that the improvement documented in each case study was the result of interventions in both assessment and work-flow redesign.
PERFORMANCE MEASUREMENT Central to any improvement project is measurement and assessment of the current state and tracking of the effects of any interventions. In attempts to reduce crowding, performance measurement has usually included the introduction and use of time-based measures such as the time from entry into the ED to seeing a physician, duration of the ED visit, the duration and prevalence of admitted patients awaiting transport to an inpatient bed, and duration and frequency of hospital “diversion” or “bypass.” Other measures include the percentage of patients who leave the ED without completing the encounter (“left without being seen”) or the proportion of inpatients discharged by a specific time of day. These measures have gained increased acceptance and are viewed by many leaders in emergency medicine and operations as helpful gauges of throughput.7 Performance measurement is perhaps the most elemental and yet essential of the four Volume 33 Number 11 Supplement
Copyright 2008 Joint Commission on Accreditation of Healthcare Organizations
The Joint Commission Journal on Quality and Patient Safety
domains covered here. Without performance measurement, it is simply not possible to gauge the impact of efforts in any other domains of patient flow improvement.
Table 1. Lehigh Valley Hospital (LVH) and Healthcare Network Capacity Dashboard* Volume Indicators
Case Study: Lehigh Valley Hospital and Healthcare Network ED volume in visits: 76,099 Acute ED beds: 24 Hospital beds: 669 Location: Allentown and Bethlehem, Pennsylvania
The case of Lehigh Valley Hospital and Healthcare Network (LVHHN) is instructive. LVHHN staff and administrators found themselves experiencing common challenges. Ambulance diversion hours were increasing and ED patient satisfaction was low, which staff attributed largely to the inefficient turnover of inpatient beds. Discharge notification to the patient placement office was delayed 74 minutes on average, and the amount of time from when an inpatient left a room to when that room was ready for the next patient was averaging 210 minutes—or 3½ hours. With senior leadership support, LVHHN attempted to increase patient throughput by expanding its measurement of critical performance measures. With several information system upgrades, critical metrics were expanded to 10 measures (Table 1, right) that enabled the creation of a Capacity Dashboard. The dashboard system integrated data from multiple computer systems throughout the hospital, including the ED, operating rooms, and the bed tracking system. The dashboard also allowed LVHHN to set targets for each performance measure and to indicate whether it had achieved its target. With the selected performance measures, LVHHN was able to monitor the effectiveness of any work-flow changes on a routine basis. With this foundation in place, LVHHN implemented in 2004 a systemwide capacity goal for all staff with clearly defined throughput metrics. These goals became part of employees’ annual review and compensation adjustment. For example, housekeeping staff compensation became partially dependent on how quickly patient rooms were readied for their next occupant. Performance data were disseminated, typically during monthly unit meetings and through bulletin board postings, as well as on the unit scorecard, which tracks specific quality and performance measures related to a single department. For example, in one month in 2005, November 2007
LVH Admissions LVH Length of Stay ED Visits Patient Flow/Demand Indicators Discharges Before 11:00 A.M. Discharge Bed SWAT Team Turnaround Time ED Diversions in Hours ED Time to Seen OR Holds in Minutes Pull Average Times—Time from when the clean bed is assigned until the patient is transferred into the bed Transfer Center Acceptance Rate * Used with permission of Lehigh Valley Hospital and Healthcare Network. ED, emergency department; OR, operating room.
LVHHN set a target of 60 minutes for bed turnaround time. The actual average time was 64 minutes, placing them at 93.7% achievement of its goal. The capacity goal also included patient satisfaction targets to ensure that gains in patient flow were matched by an improved patient experience. This focus on real-time measurement may have improved LVHHN’s ability to allocate resources, and in some cases provided justification for hiring additional full-time equivalents (FTEs). With this performance measurement system in place, LVHHN has measured the impact of multiple interventions to reduce crowding since 2004. Ambulance diversions have been cut in half, ED wait times have been reduced by more than 20%, room turnaround time has been reduced to 60 minutes, and ED patient satisfaction scores have increased significantly. Comment. The experience of LVHHN may be fairly generalizable. Although this system may have benefited from a preexisting investment in electronic information systems, these are not necessary for defining essential performance measures with goals. Other hospitals have achieved similar results with manual systems.8,9 The more critical ingredient in LVHHN’s case may be the focus and discipline in articulating the components of a dashboard, sustaining its use, and tying measured performance to perVolume 33 Number 11 Supplement
Copyright 2008 Joint Commission on Accreditation of Healthcare Organizations
59
The Joint Commission Journal on Quality and Patient Safety
sonnel evaluation and compensation. Given that any effort to improve work flow will require measurement, work in this domain will be critical in any hospital seeking to improve throughput.
DEMAND FORECASTING Effective work flow requires the ability to match key resources (for example, beds, staff, supplies) to demand. Although changes in demand can at times seem random to caregivers, swings in activity may be predicted (for example, variation by time of day or season), allowing hospitals to anticipate periods of peak demand. Demand forecasting requires the analysis of historical trends, identification of key demand drivers that could also affect trends (such as closure of a nearby hospital), and development of a forecast that is then tested against reality.10 With forecasting, hospitals can predict when they will need to adjust staffing and other resource levels to meet anticipated patient volumes. Without forecasting, they may often find themselves unable to manage a peak work load, while at other times they may be providing more resources than they or their patients actually need. Case Study: LDS Hospital/Intermountain Healthcare ED volume in visits: 40,000 Acute ED beds: 31 Hospital beds: 520 Location: Salt Lake City
Borrowing forecasting tools used by other service industries such as hotels and restaurants, LDS Hospital (LDS) analyzed historical ED visit data to more accurately match staffing patterns with the demand for ED services. Building on its existing information systems, LDS developed a patient tracking system and collected data on every ED visit for one year. With a database of almost 40,000 visits, ED staff reviewed average hourly census and patient arrivals in the ED. Staff also stratified ED visits by a number of dimensions, including patient acuity and chief complaint. These historical data provided LDS with valuable insights on the variability of demand for ED services for any given 24-hour period. LDS learned that ED patient flow is predictable; within any given 24-hour a similar pattern of patient arrivals and ED census emerges.11 It also found the volume of 60
November 2007
patients in the ED peaked during the 3:00 P.M.–11:00 P.M. shift, a time when hospital staffing levels actually decreased. The sickest patients arrived during the subsequent 11:00 P.M.–7:00 A.M. shift, when many services within the hospital closed and staffing was at its nadir. This finding led LDS staff to investigate strategies for sharing or maximizing capacity during these shifts, including changing employee scheduling and implementing cross-training of key staff—for example, so that respiratory technicians could perform electrocardiograms on the evening and night shifts. Demand forecasts underpinned other changes in the hospital’s work-flow management. The number of patients presenting with chest pain generally peaked in the late morning, a time when most cardiologists are busy with invasive procedures (Figure 1, page 61). By hiring a cardiology hospitalist, the ED could care for patients with chest pain without interrupting the vital work of the cardiac catheterization laboratories. Through forecasting, changes to staffing, and better ED information systems, crowding was significantly reduced at LDS Hospital. Despite a 20% increase in ED visits, the time from patient arrival in the ED to seeing a physician was halved to just 21 minutes. Comment. Demand forecasting may not be an essential element of improving work flow, but its benefits are intuitive. Long waits in many settings are often the result of insufficient resources to meet demand. The data necessary to create forecasts exist in virtually every hospital in the United States. Hospitals know when patients arrive by hour of the day, and the great majority has some way of assessing the acuity of those patients. Even the most rudimentary of forecasts will conceivably allow better management and allocation of scarce resources, including the time of critical professionals like nurses.
FLOW REDESIGN Most patient flow initiatives incorporate some change in the actual processes and pathways by which patients move through the ED and hospital. This in turn drives some alteration in work flow. Most often these changes are aimed at eliminating bottlenecks that have been identified by measuring specific performance indicators; temporary process changes may also be implemented at periods of real or forecasted peak demand. Hence, process redesign Volume 33 Number 11 Supplement
Copyright 2008 Joint Commission on Accreditation of Healthcare Organizations
The Joint Commission Journal on Quality and Patient Safety
Chest Pain Distribution by Hour of the Day Case Study: Memorial Regional Hospital/ Memorial Healthcare System ED volume in visits: 114,000 Acute ED beds: 65 Hospital beds: 690 Location: Hollywood, Florida
Memorial Regional Hospital (Memorial) set a goal of achieving the PCI standard for 100% of eligible patients through their “Code Heart” initiative. In October 2005, the hospital created a PCI task force, which was composed of representatives from the ED, cardiology, quality management, and administration, thus engaging all levels of stakeholders. The task force designed tools to track each step (subsegment) in the time Figure 1. The percentage of emergency department patients presenting with to PCI process in both the ED and the catheterchest pain by hour of the day (0, midnight; 23, 11:00 P.M.) at LDS Hospital in 2004 is shown. ization laboratory. By combining the information from the tracking (Figure 2, page 62) and analymay often be joined to the improvement domains noted sis tools (Figure 3, page 63), the PCI task force was able to above (performance measurement and demand forecastanalyze subsegment times for each patient’s PCI experiing). These initiatives often rely on “process mapping” that ence. The analysis tool provided a means for documenting charts (for instance) all the steps in the pathway for an ED delays and bottlenecks and allowed the creation of an patient to receive a diagnostic intervention such as an action plan to improve performance. interpreted chest x-ray.12 Staff then seek to reduce the Through this plan, Memorial has achieved the number of steps in each pathway to an absolute minimum following: by reducing redundancy while seeking to eliminate or ■ Created medication kits for patients with acute reduce any waits at any particular junction. myocardial infarction to minimize patient time spent in Virtually all hospitals now must publicly report the perthe ED and to allow faster transfer to the catheterization centage of acute myocardial infarction (AMI) patients laboratory with a time from arrival to percutaneous coronary inter■ Left one catheterization laboratory in readiness for a vention (PCI) of 90 minutes or less.* The complex process “Code Heart” when the laboratory is closed of ensuring that such a patient receives timely care usually ■ Designated special parking spaces for physician and involves the ED working in concert with multiple other staff on call to improve arrival times hospital departments such as the cardiac catheterization In addition, ED and cardiac medical directors and laboratory, hence the ED may help or hinder timely AMI administrators review any instances of patients not meetcare. Given the importance of this PCI measure, the assoing the PCI time standard within one day (24 hours) of ciated processes have become major candidates for admission. This allows for immediate feedback to staff redesign.13–15 involved in patient care and for refining of plan implementation. During the 2005 calendar year, the percentage of cases meeting the PCI time standard rose from 52% to * The Centers for Medicare & Medicaid Services changed the time to PCI 72%, and by September 2006, Memorial achieved 100% standard from 120 minutes to 90 minutes effective July 1, 2006. See the Hospital Quality Alliance 2004–2007 Measure Build Out Table at compliance. http://www.cms.hhs.gov/HospitalQualityInits/downloads/HospitalHQA2004_2 Comment. This experience should be replicable in most 007200512.pdf (last accessed Sep. 24, 2007). November 2007
Volume 33 Number 11 Supplement
Copyright 2008 Joint Commission on Accreditation of Healthcare Organizations
61
The Joint Commission Journal on Quality and Patient Safety
Tracking Tool for Patient— Door to Percutaneous Coronary Intervention (PCI) Times
Figure 2. This tool was used at Memorial Regional Hospital to track patients’ door-to-PCI time in the emergency department and the catheterization laboratory. ER, emergency room; EKG, electrocardiogram; PBX, public address system; CCL, cardiac catheterization laboratory.
hospitals. It required little or no special investments in human or capital resources and involves processes that are found in essentially any hospital regardless of whether or not it provides PCI. Most of the analysis requires little more than pen and paper. The key barriers to improvement to redesign may be more about overcoming the iner62
November 2007
tia of past practices and placing new demands and expectations on providers.
CAPACITY MANAGEMENT Constrained overall hospital capacity has a direct effect on ED crowding. If a hospital’s critical care units, other Volume 33 Number 11 Supplement
Copyright 2008 Joint Commission on Accreditation of Healthcare Organizations
The Joint Commission Journal on Quality and Patient Safety
Analysis Tool for Patient— Door to Percutaneous Coronary Intervention (PCI) Times
Figure 3. This tool was used at Memorial Regional Hospital to analyze patients’ door-to-PCI times. MR, medical record; ED, emergency department; CCL, cardiac catheterization laboratory; CVI, Cardiac and Vascular Institute; EKG, electrocardiogram; Cath, catheter.
inpatient units, and diagnostic services are at saturation, admitted patients will be forced to remain in the ED, and all patients’ throughput will be slowed. Capacity management initiatives are conducted in recognition of this reality and the idea that all hospital departments must address crowding.4 These techniques are designed to provide administrators and caregivers much better information on whether a hospital’s care systems are approaching saturation, as well as a plan to functionally increase the hospital’s capacity at times of high demand. Hence, performance measurement and forecasting are often integral parts of November 2007
capacity management. These strategies often focus on expediting the exit of “boarded” patients in the ED by freeing up inefficiently used inpatient capacity. Capacity management may be, in some ways, the most radical of the four domains addressed in this article. By seeking to address an institution’s capacity as a whole, it may demand that departments outside the ED rethink their operations and their relationship to emergency services. With capacity management, many more staff within an institution may become accountable for improving patient flow. Volume 33 Number 11 Supplement
Copyright 2008 Joint Commission on Accreditation of Healthcare Organizations
63
The Joint Commission Journal on Quality and Patient Safety
Excerpt from the Full Capacity Protocol
Figure 4. An excerpt from the Full Capacity Protocol used at Stony Brook University Hospital is shown. Its complete text can be found at http://www.hospitalovercrowding.com (last accessed Sep. 21, 2007).
Case Study: Stony Brook University Hospital ED volume in visits: 73,000 Acute ED beds: 32 Hospital beds: 502 Location: Stony Brook, New York Stony Brook University Hospital confronted ED crowding in 1999 as a result of persistently low patient satisfaction scores. To address the impact of ED crowding on 64
November 2007
patients’ experiences, a continuous quality improvement (CQI) steering committee implemented the Full Capacity Protocol (Figure 4, above). Under the protocol, patients awaiting admission are transferred to acute care hallway beds on inpatient units when the ED is no longer able to evaluate and treat patients in a timely fashion. Obtaining support for the new protocol meant overcoming objections by staff including the perception that boarding patients in the hallway of acute care units was Volume 33 Number 11 Supplement
Copyright 2008 Joint Commission on Accreditation of Healthcare Organizations
The Joint Commission Journal on Quality and Patient Safety
“against the rules” and would be “bad for the patients.” The CQI steering committee analyzed the differences in nurse-to-patient staffing ratios when boarding patients in the ED versus boarding patients on inpatient units and was able to make a compelling argument for the new protocol: The accumulation of boarded patients in the ED led to extremely low ratios of nurses to patients in the ED. In contrast, moving those patients to the much larger inpatient units had only limited effects on the units’ staffing ratios; these inpatient units continued to provide more favorable nursing ratios than those available in the ED. The protocol is activated by the hospital’s bed coordinator with the approval of the hospital medical director. This chain of command helped recast the problem of crowding as a hospital (not an ED) issue requiring a broad institutional response. The protocol defines the types of patients eligible for acute care hallway beds (for example, no patients requiring intensive care), the types of units that could accept patients, and a limit of two patients boarding in each inpatient unit. In implementing the protocol, about half of the patients sent to inpatient hallways were in a regular room within an hour of transfer from the ED. The remainder averaged about eight hours in the hallway. Implementing the Full Capacity Protocol had implications for the culture of the entire hospital; boarding patients in the ED could no longer be just an “ED problem.” By moving the patient to the acute care hallway, the ED team is relieved of caring for large numbers of boarders, and the patient is closer to the inpatient team that will oversee his or her care. Moving the patient to the inpatient unit hallway also serves as a visual reminder to staff that a patient is waiting for a room. The hospital believes the additional work load on the inpatient staff is small, and over time, not found to be a problem. Monitoring the patient via telemetry, providing a call bell and privacy screen, and identifying a bathroom for the patient to use are keys to success. Patients overwhelmingly prefer movement to the inpatient unit as opposed to remaining in a hallway in the ED. Coupled with other efforts to reduce ED crowding, initiation of the Full Capacity Protocol has led to increased patient satisfaction at Stony Brook University Hospital and improved staff job satisfaction. ED patient satisfaction ratings have increased to the 80th percentile, with inpatient scores unaffected. November 2007
Comment. Although this protocol may intuitively make much sense, it may face the greatest resistance of the four improvement domains outlined in this article. Like other interventions that require high degrees of institutionwide support (for example, Litvak’s theory of “smoothing artificial variability”16,17), the Full Capacity Protocol demands that the greatest number of health professionals change their routine practices in very fundamental and perhaps inconvenient ways. Inpatient nurses who had once been able to rely on a somewhat controllable flow of admitted patients to their unit may now face less predictable peaks and valleys of demand. It may be eminently fair and have very positive implications for the patient but it also may engender the greatest resistance.
Implications for Practice and Policy Although the range of strategies to reduce crowding is great, several overarching themes emerge. First, hospitals and their leaders can effect real change and improvement in this area. This change often seems to be less about the introduction of new resources and more about a deliberate approach that incorporates measurement, forecasting, redesign of flow and work processes, and an emphasis on management of the whole hospital’s capacity. Notably, none of the cases related above required considerable new resources. They instead depended on better, focused use of existing data and measurement systems, and in some instances a willingness to embrace and promote far-reaching cultural changes. In successful initiatives the critical factors are strong, sustained leadership as well as the open, transparent communication of project goals and data. The four domains of improvement are also highly interrelated. Clearly, it is impossible to gauge the impact of a process redesign or other interventions without reliable performance measurement. Indeed, although the four domains were separated for purpose of our discussion, patient flow initiatives generally combine elements of several or all four domains. One or two domains may have much greater prominence, but once institutions begin, for example, to rigorously measure throughput performance, the other domains often follow though some may be underdeveloped. The robust combination of all four domains would seem the most potent recipe for significant change, but the empirical evidence to support this is Volume 33 Number 11 Supplement
Copyright 2008 Joint Commission on Accreditation of Healthcare Organizations
65
The Joint Commission Journal on Quality and Patient Safety
scarce, for reasons now noted. The relative emphasis of each domain in any initiative may affect the relative ease of that initiative’s adoption. An initiative that focuses on measuring and forecasting demand may be most palatable to a hospital because it requires the least change in work flow, processes, jobs, and cultural expectations. A greater emphasis on redesigning processes may be more challenging, whereas a strategy such as the Full Capacity Protocol may pose the strongest challenge to an institution’s internal norms. Hence we might expect such hospitalwide changes to face the stiffest resistance. Achieving results in work-flow initiatives has also meant a fundamental reframing of an “ED problem” as a hospitalwide challenge. Although this paradigm shift is explicit in the capacity management domain, it is actually implicit in many applications in the other domains. Leaders attempting change may run the risk of failure should they, for instance, revamp performance measurement, set clear goals, and attempt interventions without making crowding a priority for everyone in the institution. The result is that crowding is viewed as the province of the ED, with little attention to the problems of patient transition from the ED to other services or to hospital capacity. Indeed, simply using the term ED crowding implies that crowding is solely an ED issue. Unfortunately, the ED cannot fix crowding in a hospital. There are other implications of such a paradigm shift. When leadership makes the issue of crowding a challenge for the entire hospital, it can signal that it is a major problem with implications for patient safety and work conditions for all staff. Efforts to reduce crowding can then have the broadest possible impact on improving the work environment. A strategy to reduce crowding can then be viewed not only as a way to reduce ED waits and boarding but as an initiative that can improve conditions for staff and patients throughout the organization. Although these changes are promising, there is a lot more to be done. In most patient flow initiatives, there is an assumption that crowding has serious implications for patient safety and medical error. This is not a surprise, given the clinical uncertainty and difficult conditions in timely diagnosis and handoffs in a crowded environment. There is some evidence of this in the literature,19,20 but more could be done to understand the strength of this 66
November 2007
association. Linking rates of error or other markers of poor quality care to periods of crowding is not methodologically simple. Add to that the reluctance of most institutions to open themselves up to such public examination, and it becomes clear why there has not been more activity in this area. Without such work, however, we may be missing a major threat to the patient’s safety. Comparisons between initiatives and among institutions are very difficult, given the lack of standard, common performance measures and benchmarks. In some areas of clinical quality, we now have nationally recognized uniform measures, such as the Hospital Quality Alliance measures jointly promulgated by the Joint Commission, the Centers for Medicare & Medicaid Services, and the National Quality Forum.21 As a result, virtually every hospital in the United States can compare its performance on specific dimensions of cardiac and pneumonia care with every other hospital. Consumers can also access this data. None of this is the case in patient flow and crowding. Individual hospitals collect data on the basis of different performance measures at uneven intervals and have little ability to compare performance to others. There have been efforts to move to nationally uniform measures, perhaps most notably through the recent Consensus Statement of the Emergency Department Benchmarking Alliance.7 Yet without true national consensus and uniformity on measurement, those working to reduce crowding are in many senses “flying blind.” The adoption of national measures of crowding and patient flow by the Hospital Quality Alliance would move this field dramatically ahead. Leaders could compare their institution’s performance to that of others. Perhaps even more important, it would greatly increase the attention devoted to crowding and patient flow. Hospital administrators might find it harder not to address these issues when performance data on wait times and the percent of patients who leave the hospital before being treated is in their local newspaper. The lack of national measures also contributes to the absence of more formal, controlled trials of crowding reduction strategies. It would not be realistic to expect the rigor of a randomized controlled trial in most performance improvement projects. The complexity of hospitals and the systems that affect flow and crowding does not lend itself to such evaluation frameworks. However, for the most Volume 33 Number 11 Supplement
Copyright 2008 Joint Commission on Accreditation of Healthcare Organizations
The Joint Commission Journal on Quality and Patient Safety
part, the literature on crowding reduction is composed of single-institution case studies in which impact is measured using different measures over different time frames, without any case control. So although it may be reasonable to conclude on the basis of the existing literature that we can reduce crowding, it may not be reasonable to assert that one strategy is better than another. We need better designed, prospective studies of strategies to reduce crowding using controls and well-defined measures. Many more EDs and hospitals might move aggressively to eliminate crowding if there was stronger evidence on what works.
Conclusion Crowding can have a significant impact on hospital work conditions, quality, and patient safety. This may be especially evident in the ED, which may be particularly susceptible to medical error. Health systems can take tangible, immediate steps to improve flow across the entire institution and reduce crowding in the ED. This can be of immediate benefit to the work environment by eliminating chaotic, stressful environments that foster error. The resulting improved morale and job satisfaction will also help in the recruitment and retention of the experienced health professionals needed in a high-performing health system. J Bruce Siegel, M.D., M.P.H., is Research Professor, Department of Health Policy, The George Washington University School of Public Health and Health Services, Washington, DC; Marcia J. Wilson, M.B.A., is Research Scientist. Donna Sickler, M.P.H., is Research Associate. Please address correspondence to Bruce Siegel, M.D., M.P.H., [email protected].
References 1. Burt C.W., McCaig L.F.: Staffing, capacity, and ambulance diversion in emergency departments: United States, 2003–04. Advance Data from Vital and Health Statistics 376: 1–23, Sep. 27, 2006. 2. American Hospital Association: The State of America’s HospitalsTaking the Pulse: Findings from the 2006 American Hospital Association Survey of Hospital Leaders. http://www.aha.org/aha/content/2006/ PowerPoint/StateHospitalsChartPack2006.PPT (last accessed Sep. 21, 2007). 3. U.S. Government Accountability Office (GAO): Hospital Emergency Departments: Crowded Conditions Vary Among Hospitals and Communities. GAO-03-460, Mar. 2003. http://www.gao.gov/new.items/ d03460.pdf (last accessed Sep. 21, 2004).
November 2007
4. Institute of Medicine: Future of Emergency Care: Hospital-Based Emergency Care: At the Breaking Point. Washington, DC: National Academy Press, Jun. 2006. 5. Croskerry P., et al.: Emergency medicine: A practice prone to error? Canadian Journal of Emergency Medicine 3:271–276, Oct. 2001. 6. Joint Commission Resources (JCR): Safer Emergency Care. Oakbrook Terrace, IL: JCR, 2007. 7. Welch S., et al.: Emergency department performance measures and Benchmarking Summit. Acad Emerg Med 13, Oct. 2006. http://www.aemj.org/content/vol13/issue10 (last accessed Oct. 10, 2007). 8. Wilson M.J., Nguyen K.: Bursting at the Seams: Improving Patient Flow to Help America’s Emergency Departments. The George Washington University Medical Center, Sep. 2004. http://www.urgentmatters.org/ reports/UM_WhitePaper_BurstingAtTheSeams.pdf (last accessed Sep. 21, 2007). 9. Wilson M.J., Siegel B., Williams M.: Perfecting Patient Flow: America’s Safety Net Hospitals and Emergency Department Crowding. The George Washington University Medical Center, May 2005. http://www.urgent matters.org/reports/NAPH_Perfecting_Patient_Flow.pdf (last accessed Sep. 21, 2007). 10. Finarelli H.J. Jr., Johnson T.: Effective demand forecasting in 9 steps. Healthc Financ Manage 58:52–56,58, Nov. 2004. 11. Welch S.J., Jones S.S., Allen T.: Mapping the 24-hour emergency department cycle to improve patient flow. Jt Comm J Qual Patient Saf 33:247–255, May 2007. 12. Mahaffey S.: Optimizing patient flow in the enterprise: Hospitals can combine process management with information technology to redesign patient flow for maximum efficiency and clinical outcomes. Health Manag Technol 25:34–36, Aug. 2004. 13. American College of Cardiology: D2B: An Alliance for Quality.http://www.d2balliance.org (last accessed Sep. 21, 2007). 14. Bradley E.H., et al.: Achieving door-to-balloon times that meet quality guidelines: How do successful hospitals do it? J Am Coll Cardiol 46:1236–1241, Oct. 4, 2005. 15. Bradley E.H., et al.: Strategies for reducing the door-to-balloon time in acute myocardial infarction. N Engl J Med 355:2308–2320, Nov. 30, 2006. http://content.nejm.org/cgi/content/short/355/22/2308 (last accessed Oct. 10, 2007). 16. Chessare J.: Making room in the ED by starting with . . . elective surgery? Patient Flow Urgent Matters, Jan. 10, 2004. http://www.urgentmatters.org/enewsletter/volume1/issue2/ Inn_bmc.asp (last accessed Sep. 24, 2007). 17. Litvak E., et al.: Managing unnecessary variability in patient demand to reduce nursing stress and improve patient safety. Jt Comm J Qual Patient Saf 31:330–338, Jun. 2005. 18. Barron W.M., et al.: Critical success factors for performance improvement programs. Jt Comm J Qual Patient Saf 31:220–226, Apr. 2005. 19. Fordyce J., et al.: Errors in a busy emergency department. Ann Emerg Med 42:324–333, Sep. 2003. 20. Woo Liu S., et al.: Frequency of adverse events and errors among patients boarding in the emergency department. Acad Emerg Med 12(suppl. 1):49–50, May 2005 [abstract]. 21. Centers for Medicare & Medicaid Services: Hospital Compare. http://www.cms.hhs.gov/HospitalQualityInits/25_HospitalCompare. asp#TopOfPage%20 (last accessed Sep. 21, 2007).
Volume 33 Number 11 Supplement
Copyright 2008 Joint Commission on Accreditation of Healthcare Organizations
67
Enhancing Work Flow to Reduce Crowding Bruce Siegel, M.D., M.P.H. Marcia J. Wilson, M.B.A. Donna Sickler, M.P.H.
H
ospital crowding has emerged as one of the key challenges facing health care systems in the United States. Between 1994 and 2004, emergency department (ED) visits increased 18%, while the number of EDs decreased more than 12%.1 Not surprisingly, in 2006, 42% of all hospitals and 64% of urban hospitals reported going on ambulance diversion in a given year.2 In an earlier survey of hospital administrators, 90% reported that they had held or “boarded” patients in the ED for two or more hours awaiting an inpatient bed, and one in five hospitals reported boarding patients in the ED for eight hours or more on average.3 In recent years, crowding has received in-depth treatment by a number of authorities, culminating in the 2006 Institute of Medicine (IOM) report, Future of Emergency Care: Hospital Based Emergency Care at the Breaking Point.4 Crowding has been viewed at best as an inconvenience for patients who may be forced to wait hours to see a physician and even longer to be admitted to an inpatient bed. However, it can also erode working conditions and workflow in EDs that are already singularly susceptible to medical error.5 Patients are often unknown to ED caregivers, who must make decisions in relatively short time frames. Patients frequently present to the ED with nonspecific complaints such as “weakness” or “abdominal pain” that carry considerable clinical uncertainty and require extensive diagnostic examinations. Add to this the chaotic and pressured conditions that accompany crowding, and we are faced with a workplace fraught with the potential for November 2007
Article-at-a-Glance Introduction: Approximately one third of hospitals in the United States report increases in ambulance diversion in a given year, whereas up to half report crowded conditions in the emergency department (ED). In a recent national survey, 40% of hospital leaders viewed ED crowding as a symptom of workforce shortages. Many health systems are implementing a variety of strategies to improve flow and reduce crowding. Domains of Improvement: Virtually all work-flow initiatives use operations management techniques that include some or all of four domains: performance measurement, demand forecasting, flow redesign, and capacity management. These are often implemented using rapid improvement techniques. Most initiatives tend to focus on functional increases in inpatient capacity. Implications for Practice and Policy: Successful strategies to improve patient flow are distinguished by an organizationwide commitment to measurement, transparency in data reporting, and sustained management attention. Focusing on transitions between ED and inpatient units and maximizing overall hospital capacity appears necessary for improvement. Hence, reductions in ED crowding require strategies that go far beyond the ED. Conclusion: Health systems can take tangible, immediate steps to improve flow and reduce crowding. Efforts would be enhanced by more controlled trials of existing strategies in the context of uniform performance measures.
Volume 33 Number 11 Supplement
Copyright 2008 Joint Commission on Accreditation of Healthcare Organizations
57
The Joint Commission Journal on Quality and Patient Safety
poor outcomes. Clinicians and administrators link crowding to the workplace in other ways. In a recent national survey, 40% of hospital leaders viewed ED crowding as a symptom of workforce shortages.3 A chaotic work environment serves as a deterrent to recruitment and retention of the highly qualified professionals needed to staff the modern ED. In turn, these vacancies lead to a slower, overtaxed ED. Staff shortages and suboptimal patient flow in areas of the hospital outside the ED can also have significant ramifications for ED crowding. A well-functioning ED must have the ability to move admitted patients to other parts of the institution in a timely fashion. In the presence of any impediment to that flow throughout the entire institution, the ED may quickly become a backed-up reservoir, holding patients who continue to flow in, and unable to move the sickest, admitted patients out to appropriate care in other parts of the institution. The issue of crowding has gained greater visibility with the recent implementation of The Joint Commission’s first-ever patient flow standard.*6 Many health systems are currently implementing a wide variety of strategies to improve flow and reduce ED crowding. These undertakings range dramatically in underlying theory, focus, and scope. Through our role as the national coordinating office for a Robert Wood Johnson Foundation crowding project, Urgent Matters (http://www.urgentmatters.org/), we have overseen a major patient flow improvement collaborative and served as a clearinghouse for flow innovation since 2002. This has given us unparalleled insight into what specific hospitals are doing to address crowding and flow.
Domains of Improvement Most of the crowding initiatives we have seen are “homegrown”; they are planned and implemented by a single health system with little or no external assistance. Others are conducted in the context of national improvement programs such as the Urgent Matters Learning Network, the Institute for Healthcare Improvement IMPACT * Standard LD.3.15: “The leaders develop and implement plans to identify and mitigate impediments to efficient patient flow throughout the hospital.” (pp. LD11–LD-11a). The Joint Commission: 2007 Comprehensive Accreditation Manual for Hospitals: The Official Handbook. Oakbrook Terrace, IL: Joint Commission Resources, 2006.
58
November 2007
Network, and the University Health System Patient Flow Benchmarking Project. Although there is considerable heterogeneity in the design and outcomes of these initiatives, several common threads can be identified. Specifically, hospital-based initiatives to improve patient flow and reduce ED crowding generally attempt to enhance workflow in the care setting by focusing on one or more of four common domains of patient flow improvement—performance measurement, demand forecasting, flow redesign, and capacity management. These are often interdependent concepts, and initiatives benefit by the presence of all these domains. The first two domains, performance measurement and demand forecasting, may be critical elements of a foundation for change. However, they are both forms of assessment, and thus in isolation will not automatically lead to improvements in flow and reductions in crowding. Achieving these desired outcomes requires the addition of some form of redesign in work flow, which can take the form of the third and fourth domains: flow redesign or capacity management. In this section, each of these four domains is discussed in turn, each domain featuring an illustrative case study. However, one may assume that the improvement documented in each case study was the result of interventions in both assessment and work-flow redesign.
PERFORMANCE MEASUREMENT Central to any improvement project is measurement and assessment of the current state and tracking of the effects of any interventions. In attempts to reduce crowding, performance measurement has usually included the introduction and use of time-based measures such as the time from entry into the ED to seeing a physician, duration of the ED visit, the duration and prevalence of admitted patients awaiting transport to an inpatient bed, and duration and frequency of hospital “diversion” or “bypass.” Other measures include the percentage of patients who leave the ED without completing the encounter (“left without being seen”) or the proportion of inpatients discharged by a specific time of day. These measures have gained increased acceptance and are viewed by many leaders in emergency medicine and operations as helpful gauges of throughput.7 Performance measurement is perhaps the most elemental and yet essential of the four Volume 33 Number 11 Supplement
Copyright 2008 Joint Commission on Accreditation of Healthcare Organizations
The Joint Commission Journal on Quality and Patient Safety
domains covered here. Without performance measurement, it is simply not possible to gauge the impact of efforts in any other domains of patient flow improvement.
Table 1. Lehigh Valley Hospital (LVH) and Healthcare Network Capacity Dashboard* Volume Indicators
Case Study: Lehigh Valley Hospital and Healthcare Network ED volume in visits: 76,099 Acute ED beds: 24 Hospital beds: 669 Location: Allentown and Bethlehem, Pennsylvania
The case of Lehigh Valley Hospital and Healthcare Network (LVHHN) is instructive. LVHHN staff and administrators found themselves experiencing common challenges. Ambulance diversion hours were increasing and ED patient satisfaction was low, which staff attributed largely to the inefficient turnover of inpatient beds. Discharge notification to the patient placement office was delayed 74 minutes on average, and the amount of time from when an inpatient left a room to when that room was ready for the next patient was averaging 210 minutes—or 3½ hours. With senior leadership support, LVHHN attempted to increase patient throughput by expanding its measurement of critical performance measures. With several information system upgrades, critical metrics were expanded to 10 measures (Table 1, right) that enabled the creation of a Capacity Dashboard. The dashboard system integrated data from multiple computer systems throughout the hospital, including the ED, operating rooms, and the bed tracking system. The dashboard also allowed LVHHN to set targets for each performance measure and to indicate whether it had achieved its target. With the selected performance measures, LVHHN was able to monitor the effectiveness of any work-flow changes on a routine basis. With this foundation in place, LVHHN implemented in 2004 a systemwide capacity goal for all staff with clearly defined throughput metrics. These goals became part of employees’ annual review and compensation adjustment. For example, housekeeping staff compensation became partially dependent on how quickly patient rooms were readied for their next occupant. Performance data were disseminated, typically during monthly unit meetings and through bulletin board postings, as well as on the unit scorecard, which tracks specific quality and performance measures related to a single department. For example, in one month in 2005, November 2007
LVH Admissions LVH Length of Stay ED Visits Patient Flow/Demand Indicators Discharges Before 11:00 A.M. Discharge Bed SWAT Team Turnaround Time ED Diversions in Hours ED Time to Seen OR Holds in Minutes Pull Average Times—Time from when the clean bed is assigned until the patient is transferred into the bed Transfer Center Acceptance Rate * Used with permission of Lehigh Valley Hospital and Healthcare Network. ED, emergency department; OR, operating room.
LVHHN set a target of 60 minutes for bed turnaround time. The actual average time was 64 minutes, placing them at 93.7% achievement of its goal. The capacity goal also included patient satisfaction targets to ensure that gains in patient flow were matched by an improved patient experience. This focus on real-time measurement may have improved LVHHN’s ability to allocate resources, and in some cases provided justification for hiring additional full-time equivalents (FTEs). With this performance measurement system in place, LVHHN has measured the impact of multiple interventions to reduce crowding since 2004. Ambulance diversions have been cut in half, ED wait times have been reduced by more than 20%, room turnaround time has been reduced to 60 minutes, and ED patient satisfaction scores have increased significantly. Comment. The experience of LVHHN may be fairly generalizable. Although this system may have benefited from a preexisting investment in electronic information systems, these are not necessary for defining essential performance measures with goals. Other hospitals have achieved similar results with manual systems.8,9 The more critical ingredient in LVHHN’s case may be the focus and discipline in articulating the components of a dashboard, sustaining its use, and tying measured performance to perVolume 33 Number 11 Supplement
Copyright 2008 Joint Commission on Accreditation of Healthcare Organizations
59
The Joint Commission Journal on Quality and Patient Safety
sonnel evaluation and compensation. Given that any effort to improve work flow will require measurement, work in this domain will be critical in any hospital seeking to improve throughput.
DEMAND FORECASTING Effective work flow requires the ability to match key resources (for example, beds, staff, supplies) to demand. Although changes in demand can at times seem random to caregivers, swings in activity may be predicted (for example, variation by time of day or season), allowing hospitals to anticipate periods of peak demand. Demand forecasting requires the analysis of historical trends, identification of key demand drivers that could also affect trends (such as closure of a nearby hospital), and development of a forecast that is then tested against reality.10 With forecasting, hospitals can predict when they will need to adjust staffing and other resource levels to meet anticipated patient volumes. Without forecasting, they may often find themselves unable to manage a peak work load, while at other times they may be providing more resources than they or their patients actually need. Case Study: LDS Hospital/Intermountain Healthcare ED volume in visits: 40,000 Acute ED beds: 31 Hospital beds: 520 Location: Salt Lake City
Borrowing forecasting tools used by other service industries such as hotels and restaurants, LDS Hospital (LDS) analyzed historical ED visit data to more accurately match staffing patterns with the demand for ED services. Building on its existing information systems, LDS developed a patient tracking system and collected data on every ED visit for one year. With a database of almost 40,000 visits, ED staff reviewed average hourly census and patient arrivals in the ED. Staff also stratified ED visits by a number of dimensions, including patient acuity and chief complaint. These historical data provided LDS with valuable insights on the variability of demand for ED services for any given 24-hour period. LDS learned that ED patient flow is predictable; within any given 24-hour a similar pattern of patient arrivals and ED census emerges.11 It also found the volume of 60
November 2007
patients in the ED peaked during the 3:00 P.M.–11:00 P.M. shift, a time when hospital staffing levels actually decreased. The sickest patients arrived during the subsequent 11:00 P.M.–7:00 A.M. shift, when many services within the hospital closed and staffing was at its nadir. This finding led LDS staff to investigate strategies for sharing or maximizing capacity during these shifts, including changing employee scheduling and implementing cross-training of key staff—for example, so that respiratory technicians could perform electrocardiograms on the evening and night shifts. Demand forecasts underpinned other changes in the hospital’s work-flow management. The number of patients presenting with chest pain generally peaked in the late morning, a time when most cardiologists are busy with invasive procedures (Figure 1, page 61). By hiring a cardiology hospitalist, the ED could care for patients with chest pain without interrupting the vital work of the cardiac catheterization laboratories. Through forecasting, changes to staffing, and better ED information systems, crowding was significantly reduced at LDS Hospital. Despite a 20% increase in ED visits, the time from patient arrival in the ED to seeing a physician was halved to just 21 minutes. Comment. Demand forecasting may not be an essential element of improving work flow, but its benefits are intuitive. Long waits in many settings are often the result of insufficient resources to meet demand. The data necessary to create forecasts exist in virtually every hospital in the United States. Hospitals know when patients arrive by hour of the day, and the great majority has some way of assessing the acuity of those patients. Even the most rudimentary of forecasts will conceivably allow better management and allocation of scarce resources, including the time of critical professionals like nurses.
FLOW REDESIGN Most patient flow initiatives incorporate some change in the actual processes and pathways by which patients move through the ED and hospital. This in turn drives some alteration in work flow. Most often these changes are aimed at eliminating bottlenecks that have been identified by measuring specific performance indicators; temporary process changes may also be implemented at periods of real or forecasted peak demand. Hence, process redesign Volume 33 Number 11 Supplement
Copyright 2008 Joint Commission on Accreditation of Healthcare Organizations
The Joint Commission Journal on Quality and Patient Safety
Chest Pain Distribution by Hour of the Day Case Study: Memorial Regional Hospital/ Memorial Healthcare System ED volume in visits: 114,000 Acute ED beds: 65 Hospital beds: 690 Location: Hollywood, Florida
Memorial Regional Hospital (Memorial) set a goal of achieving the PCI standard for 100% of eligible patients through their “Code Heart” initiative. In October 2005, the hospital created a PCI task force, which was composed of representatives from the ED, cardiology, quality management, and administration, thus engaging all levels of stakeholders. The task force designed tools to track each step (subsegment) in the time Figure 1. The percentage of emergency department patients presenting with to PCI process in both the ED and the catheterchest pain by hour of the day (0, midnight; 23, 11:00 P.M.) at LDS Hospital in 2004 is shown. ization laboratory. By combining the information from the tracking (Figure 2, page 62) and analymay often be joined to the improvement domains noted sis tools (Figure 3, page 63), the PCI task force was able to above (performance measurement and demand forecastanalyze subsegment times for each patient’s PCI experiing). These initiatives often rely on “process mapping” that ence. The analysis tool provided a means for documenting charts (for instance) all the steps in the pathway for an ED delays and bottlenecks and allowed the creation of an patient to receive a diagnostic intervention such as an action plan to improve performance. interpreted chest x-ray.12 Staff then seek to reduce the Through this plan, Memorial has achieved the number of steps in each pathway to an absolute minimum following: by reducing redundancy while seeking to eliminate or ■ Created medication kits for patients with acute reduce any waits at any particular junction. myocardial infarction to minimize patient time spent in Virtually all hospitals now must publicly report the perthe ED and to allow faster transfer to the catheterization centage of acute myocardial infarction (AMI) patients laboratory with a time from arrival to percutaneous coronary inter■ Left one catheterization laboratory in readiness for a vention (PCI) of 90 minutes or less.* The complex process “Code Heart” when the laboratory is closed of ensuring that such a patient receives timely care usually ■ Designated special parking spaces for physician and involves the ED working in concert with multiple other staff on call to improve arrival times hospital departments such as the cardiac catheterization In addition, ED and cardiac medical directors and laboratory, hence the ED may help or hinder timely AMI administrators review any instances of patients not meetcare. Given the importance of this PCI measure, the assoing the PCI time standard within one day (24 hours) of ciated processes have become major candidates for admission. This allows for immediate feedback to staff redesign.13–15 involved in patient care and for refining of plan implementation. During the 2005 calendar year, the percentage of cases meeting the PCI time standard rose from 52% to * The Centers for Medicare & Medicaid Services changed the time to PCI 72%, and by September 2006, Memorial achieved 100% standard from 120 minutes to 90 minutes effective July 1, 2006. See the Hospital Quality Alliance 2004–2007 Measure Build Out Table at compliance. http://www.cms.hhs.gov/HospitalQualityInits/downloads/HospitalHQA2004_2 Comment. This experience should be replicable in most 007200512.pdf (last accessed Sep. 24, 2007). November 2007
Volume 33 Number 11 Supplement
Copyright 2008 Joint Commission on Accreditation of Healthcare Organizations
61
The Joint Commission Journal on Quality and Patient Safety
Tracking Tool for Patient— Door to Percutaneous Coronary Intervention (PCI) Times
Figure 2. This tool was used at Memorial Regional Hospital to track patients’ door-to-PCI time in the emergency department and the catheterization laboratory. ER, emergency room; EKG, electrocardiogram; PBX, public address system; CCL, cardiac catheterization laboratory.
hospitals. It required little or no special investments in human or capital resources and involves processes that are found in essentially any hospital regardless of whether or not it provides PCI. Most of the analysis requires little more than pen and paper. The key barriers to improvement to redesign may be more about overcoming the iner62
November 2007
tia of past practices and placing new demands and expectations on providers.
CAPACITY MANAGEMENT Constrained overall hospital capacity has a direct effect on ED crowding. If a hospital’s critical care units, other Volume 33 Number 11 Supplement
Copyright 2008 Joint Commission on Accreditation of Healthcare Organizations
The Joint Commission Journal on Quality and Patient Safety
Analysis Tool for Patient— Door to Percutaneous Coronary Intervention (PCI) Times
Figure 3. This tool was used at Memorial Regional Hospital to analyze patients’ door-to-PCI times. MR, medical record; ED, emergency department; CCL, cardiac catheterization laboratory; CVI, Cardiac and Vascular Institute; EKG, electrocardiogram; Cath, catheter.
inpatient units, and diagnostic services are at saturation, admitted patients will be forced to remain in the ED, and all patients’ throughput will be slowed. Capacity management initiatives are conducted in recognition of this reality and the idea that all hospital departments must address crowding.4 These techniques are designed to provide administrators and caregivers much better information on whether a hospital’s care systems are approaching saturation, as well as a plan to functionally increase the hospital’s capacity at times of high demand. Hence, performance measurement and forecasting are often integral parts of November 2007
capacity management. These strategies often focus on expediting the exit of “boarded” patients in the ED by freeing up inefficiently used inpatient capacity. Capacity management may be, in some ways, the most radical of the four domains addressed in this article. By seeking to address an institution’s capacity as a whole, it may demand that departments outside the ED rethink their operations and their relationship to emergency services. With capacity management, many more staff within an institution may become accountable for improving patient flow. Volume 33 Number 11 Supplement
Copyright 2008 Joint Commission on Accreditation of Healthcare Organizations
63
The Joint Commission Journal on Quality and Patient Safety
Excerpt from the Full Capacity Protocol
Figure 4. An excerpt from the Full Capacity Protocol used at Stony Brook University Hospital is shown. Its complete text can be found at http://www.hospitalovercrowding.com (last accessed Sep. 21, 2007).
Case Study: Stony Brook University Hospital ED volume in visits: 73,000 Acute ED beds: 32 Hospital beds: 502 Location: Stony Brook, New York Stony Brook University Hospital confronted ED crowding in 1999 as a result of persistently low patient satisfaction scores. To address the impact of ED crowding on 64
November 2007
patients’ experiences, a continuous quality improvement (CQI) steering committee implemented the Full Capacity Protocol (Figure 4, above). Under the protocol, patients awaiting admission are transferred to acute care hallway beds on inpatient units when the ED is no longer able to evaluate and treat patients in a timely fashion. Obtaining support for the new protocol meant overcoming objections by staff including the perception that boarding patients in the hallway of acute care units was Volume 33 Number 11 Supplement
Copyright 2008 Joint Commission on Accreditation of Healthcare Organizations
The Joint Commission Journal on Quality and Patient Safety
“against the rules” and would be “bad for the patients.” The CQI steering committee analyzed the differences in nurse-to-patient staffing ratios when boarding patients in the ED versus boarding patients on inpatient units and was able to make a compelling argument for the new protocol: The accumulation of boarded patients in the ED led to extremely low ratios of nurses to patients in the ED. In contrast, moving those patients to the much larger inpatient units had only limited effects on the units’ staffing ratios; these inpatient units continued to provide more favorable nursing ratios than those available in the ED. The protocol is activated by the hospital’s bed coordinator with the approval of the hospital medical director. This chain of command helped recast the problem of crowding as a hospital (not an ED) issue requiring a broad institutional response. The protocol defines the types of patients eligible for acute care hallway beds (for example, no patients requiring intensive care), the types of units that could accept patients, and a limit of two patients boarding in each inpatient unit. In implementing the protocol, about half of the patients sent to inpatient hallways were in a regular room within an hour of transfer from the ED. The remainder averaged about eight hours in the hallway. Implementing the Full Capacity Protocol had implications for the culture of the entire hospital; boarding patients in the ED could no longer be just an “ED problem.” By moving the patient to the acute care hallway, the ED team is relieved of caring for large numbers of boarders, and the patient is closer to the inpatient team that will oversee his or her care. Moving the patient to the inpatient unit hallway also serves as a visual reminder to staff that a patient is waiting for a room. The hospital believes the additional work load on the inpatient staff is small, and over time, not found to be a problem. Monitoring the patient via telemetry, providing a call bell and privacy screen, and identifying a bathroom for the patient to use are keys to success. Patients overwhelmingly prefer movement to the inpatient unit as opposed to remaining in a hallway in the ED. Coupled with other efforts to reduce ED crowding, initiation of the Full Capacity Protocol has led to increased patient satisfaction at Stony Brook University Hospital and improved staff job satisfaction. ED patient satisfaction ratings have increased to the 80th percentile, with inpatient scores unaffected. November 2007
Comment. Although this protocol may intuitively make much sense, it may face the greatest resistance of the four improvement domains outlined in this article. Like other interventions that require high degrees of institutionwide support (for example, Litvak’s theory of “smoothing artificial variability”16,17), the Full Capacity Protocol demands that the greatest number of health professionals change their routine practices in very fundamental and perhaps inconvenient ways. Inpatient nurses who had once been able to rely on a somewhat controllable flow of admitted patients to their unit may now face less predictable peaks and valleys of demand. It may be eminently fair and have very positive implications for the patient but it also may engender the greatest resistance.
Implications for Practice and Policy Although the range of strategies to reduce crowding is great, several overarching themes emerge. First, hospitals and their leaders can effect real change and improvement in this area. This change often seems to be less about the introduction of new resources and more about a deliberate approach that incorporates measurement, forecasting, redesign of flow and work processes, and an emphasis on management of the whole hospital’s capacity. Notably, none of the cases related above required considerable new resources. They instead depended on better, focused use of existing data and measurement systems, and in some instances a willingness to embrace and promote far-reaching cultural changes. In successful initiatives the critical factors are strong, sustained leadership as well as the open, transparent communication of project goals and data. The four domains of improvement are also highly interrelated. Clearly, it is impossible to gauge the impact of a process redesign or other interventions without reliable performance measurement. Indeed, although the four domains were separated for purpose of our discussion, patient flow initiatives generally combine elements of several or all four domains. One or two domains may have much greater prominence, but once institutions begin, for example, to rigorously measure throughput performance, the other domains often follow though some may be underdeveloped. The robust combination of all four domains would seem the most potent recipe for significant change, but the empirical evidence to support this is Volume 33 Number 11 Supplement
Copyright 2008 Joint Commission on Accreditation of Healthcare Organizations
65
The Joint Commission Journal on Quality and Patient Safety
scarce, for reasons now noted. The relative emphasis of each domain in any initiative may affect the relative ease of that initiative’s adoption. An initiative that focuses on measuring and forecasting demand may be most palatable to a hospital because it requires the least change in work flow, processes, jobs, and cultural expectations. A greater emphasis on redesigning processes may be more challenging, whereas a strategy such as the Full Capacity Protocol may pose the strongest challenge to an institution’s internal norms. Hence we might expect such hospitalwide changes to face the stiffest resistance. Achieving results in work-flow initiatives has also meant a fundamental reframing of an “ED problem” as a hospitalwide challenge. Although this paradigm shift is explicit in the capacity management domain, it is actually implicit in many applications in the other domains. Leaders attempting change may run the risk of failure should they, for instance, revamp performance measurement, set clear goals, and attempt interventions without making crowding a priority for everyone in the institution. The result is that crowding is viewed as the province of the ED, with little attention to the problems of patient transition from the ED to other services or to hospital capacity. Indeed, simply using the term ED crowding implies that crowding is solely an ED issue. Unfortunately, the ED cannot fix crowding in a hospital. There are other implications of such a paradigm shift. When leadership makes the issue of crowding a challenge for the entire hospital, it can signal that it is a major problem with implications for patient safety and work conditions for all staff. Efforts to reduce crowding can then have the broadest possible impact on improving the work environment. A strategy to reduce crowding can then be viewed not only as a way to reduce ED waits and boarding but as an initiative that can improve conditions for staff and patients throughout the organization. Although these changes are promising, there is a lot more to be done. In most patient flow initiatives, there is an assumption that crowding has serious implications for patient safety and medical error. This is not a surprise, given the clinical uncertainty and difficult conditions in timely diagnosis and handoffs in a crowded environment. There is some evidence of this in the literature,19,20 but more could be done to understand the strength of this 66
November 2007
association. Linking rates of error or other markers of poor quality care to periods of crowding is not methodologically simple. Add to that the reluctance of most institutions to open themselves up to such public examination, and it becomes clear why there has not been more activity in this area. Without such work, however, we may be missing a major threat to the patient’s safety. Comparisons between initiatives and among institutions are very difficult, given the lack of standard, common performance measures and benchmarks. In some areas of clinical quality, we now have nationally recognized uniform measures, such as the Hospital Quality Alliance measures jointly promulgated by the Joint Commission, the Centers for Medicare & Medicaid Services, and the National Quality Forum.21 As a result, virtually every hospital in the United States can compare its performance on specific dimensions of cardiac and pneumonia care with every other hospital. Consumers can also access this data. None of this is the case in patient flow and crowding. Individual hospitals collect data on the basis of different performance measures at uneven intervals and have little ability to compare performance to others. There have been efforts to move to nationally uniform measures, perhaps most notably through the recent Consensus Statement of the Emergency Department Benchmarking Alliance.7 Yet without true national consensus and uniformity on measurement, those working to reduce crowding are in many senses “flying blind.” The adoption of national measures of crowding and patient flow by the Hospital Quality Alliance would move this field dramatically ahead. Leaders could compare their institution’s performance to that of others. Perhaps even more important, it would greatly increase the attention devoted to crowding and patient flow. Hospital administrators might find it harder not to address these issues when performance data on wait times and the percent of patients who leave the hospital before being treated is in their local newspaper. The lack of national measures also contributes to the absence of more formal, controlled trials of crowding reduction strategies. It would not be realistic to expect the rigor of a randomized controlled trial in most performance improvement projects. The complexity of hospitals and the systems that affect flow and crowding does not lend itself to such evaluation frameworks. However, for the most Volume 33 Number 11 Supplement
Copyright 2008 Joint Commission on Accreditation of Healthcare Organizations
The Joint Commission Journal on Quality and Patient Safety
part, the literature on crowding reduction is composed of single-institution case studies in which impact is measured using different measures over different time frames, without any case control. So although it may be reasonable to conclude on the basis of the existing literature that we can reduce crowding, it may not be reasonable to assert that one strategy is better than another. We need better designed, prospective studies of strategies to reduce crowding using controls and well-defined measures. Many more EDs and hospitals might move aggressively to eliminate crowding if there was stronger evidence on what works.
Conclusion Crowding can have a significant impact on hospital work conditions, quality, and patient safety. This may be especially evident in the ED, which may be particularly susceptible to medical error. Health systems can take tangible, immediate steps to improve flow across the entire institution and reduce crowding in the ED. This can be of immediate benefit to the work environment by eliminating chaotic, stressful environments that foster error. The resulting improved morale and job satisfaction will also help in the recruitment and retention of the experienced health professionals needed in a high-performing health system. J Bruce Siegel, M.D., M.P.H., is Research Professor, Department of Health Policy, The George Washington University School of Public Health and Health Services, Washington, DC; Marcia J. Wilson, M.B.A., is Research Scientist. Donna Sickler, M.P.H., is Research Associate. Please address correspondence to Bruce Siegel, M.D., M.P.H., [email protected].
References 1. Burt C.W., McCaig L.F.: Staffing, capacity, and ambulance diversion in emergency departments: United States, 2003–04. Advance Data from Vital and Health Statistics 376: 1–23, Sep. 27, 2006. 2. American Hospital Association: The State of America’s HospitalsTaking the Pulse: Findings from the 2006 American Hospital Association Survey of Hospital Leaders. http://www.aha.org/aha/content/2006/ PowerPoint/StateHospitalsChartPack2006.PPT (last accessed Sep. 21, 2007). 3. U.S. Government Accountability Office (GAO): Hospital Emergency Departments: Crowded Conditions Vary Among Hospitals and Communities. GAO-03-460, Mar. 2003. http://www.gao.gov/new.items/ d03460.pdf (last accessed Sep. 21, 2004).
November 2007
4. Institute of Medicine: Future of Emergency Care: Hospital-Based Emergency Care: At the Breaking Point. Washington, DC: National Academy Press, Jun. 2006. 5. Croskerry P., et al.: Emergency medicine: A practice prone to error? Canadian Journal of Emergency Medicine 3:271–276, Oct. 2001. 6. Joint Commission Resources (JCR): Safer Emergency Care. Oakbrook Terrace, IL: JCR, 2007. 7. Welch S., et al.: Emergency department performance measures and Benchmarking Summit. Acad Emerg Med 13, Oct. 2006. http://www.aemj.org/content/vol13/issue10 (last accessed Oct. 10, 2007). 8. Wilson M.J., Nguyen K.: Bursting at the Seams: Improving Patient Flow to Help America’s Emergency Departments. The George Washington University Medical Center, Sep. 2004. http://www.urgentmatters.org/ reports/UM_WhitePaper_BurstingAtTheSeams.pdf (last accessed Sep. 21, 2007). 9. Wilson M.J., Siegel B., Williams M.: Perfecting Patient Flow: America’s Safety Net Hospitals and Emergency Department Crowding. The George Washington University Medical Center, May 2005. http://www.urgent matters.org/reports/NAPH_Perfecting_Patient_Flow.pdf (last accessed Sep. 21, 2007). 10. Finarelli H.J. Jr., Johnson T.: Effective demand forecasting in 9 steps. Healthc Financ Manage 58:52–56,58, Nov. 2004. 11. Welch S.J., Jones S.S., Allen T.: Mapping the 24-hour emergency department cycle to improve patient flow. Jt Comm J Qual Patient Saf 33:247–255, May 2007. 12. Mahaffey S.: Optimizing patient flow in the enterprise: Hospitals can combine process management with information technology to redesign patient flow for maximum efficiency and clinical outcomes. Health Manag Technol 25:34–36, Aug. 2004. 13. American College of Cardiology: D2B: An Alliance for Quality.http://www.d2balliance.org (last accessed Sep. 21, 2007). 14. Bradley E.H., et al.: Achieving door-to-balloon times that meet quality guidelines: How do successful hospitals do it? J Am Coll Cardiol 46:1236–1241, Oct. 4, 2005. 15. Bradley E.H., et al.: Strategies for reducing the door-to-balloon time in acute myocardial infarction. N Engl J Med 355:2308–2320, Nov. 30, 2006. http://content.nejm.org/cgi/content/short/355/22/2308 (last accessed Oct. 10, 2007). 16. Chessare J.: Making room in the ED by starting with . . . elective surgery? Patient Flow Urgent Matters, Jan. 10, 2004. http://www.urgentmatters.org/enewsletter/volume1/issue2/ Inn_bmc.asp (last accessed Sep. 24, 2007). 17. Litvak E., et al.: Managing unnecessary variability in patient demand to reduce nursing stress and improve patient safety. Jt Comm J Qual Patient Saf 31:330–338, Jun. 2005. 18. Barron W.M., et al.: Critical success factors for performance improvement programs. Jt Comm J Qual Patient Saf 31:220–226, Apr. 2005. 19. Fordyce J., et al.: Errors in a busy emergency department. Ann Emerg Med 42:324–333, Sep. 2003. 20. Woo Liu S., et al.: Frequency of adverse events and errors among patients boarding in the emergency department. Acad Emerg Med 12(suppl. 1):49–50, May 2005 [abstract]. 21. Centers for Medicare & Medicaid Services: Hospital Compare. http://www.cms.hhs.gov/HospitalQualityInits/25_HospitalCompare. asp#TopOfPage%20 (last accessed Sep. 21, 2007).
Volume 33 Number 11 Supplement
Copyright 2008 Joint Commission on Accreditation of Healthcare Organizations
67