- Email: [email protected]
Measuring and monitoring outcomes of disease management programs
CLINICAL THERAPEUTICS®/VOL. 18, NO. 6, 1996
Measuring and Monitoring Outcomes of Disease Management Programs Kent H. Summers, RPh, PhD* Clinical Programs and Pharmacoeconomics, Prudential Health Care, Roseland, New Jersey
ABSTRACT After a brief analysis of the financial and health care motives that have led to the current boom in disease management (DM) programs, this paper discusses the pragmatic realities of a particular DM program for treating asthma. A model of the institutional structures necessary for DM to work is presented, emphasizing a process of continuous quality improvement in technical, clinical, and managerial processes. Significant differences between the conventions of controlled clinical triMs and the realities of actual patient care may lead to unrealistic expectations about DM, making sound managerial practices and effective communication even more important. A "data warehouse" can help health care systems master the intricacies
*Current affiliation: Health Outcomes Research Consultant, Eli Lilly and Company, Indianapolis, Indiana.
0149-2918/96/$3.50
of programwide data collection and analysis, making possible sound decisions regarding treatment regimens and changes in physician and patient behavior. This paper concludes with a discussion of how the Prudential Health Care DM program for asthma makes use of the practices and systems discussed above. INTRODUCTION This paper will focus on the pragmatic realities of the disease management (DM) program for asthma of Prudential Health Care, Roseland, New Jersey (hereafter referred to as Prudential). Specifically, after a brief discussion of our operating philoso p h y - t h e " w h y s " - - I will address the practical application--the " h o w s " - - o f our program. First, I want to talk about "why." The overriding concern is quality: We must improve quality in managed-care organizations (MCOs). The National Committee 13 4 1
CLINICAL THERAPEUTICS ®
for Quality Assurance has established standards with their Healthplan Employer Data Information Set (HEDIS) reports that lead to our systems being measured against each other, giving us an impetus to improve quality by becoming involved in DM programs. HEDIS scores are used to evaluate programs on the basis of preventive measures, such as immunizations and screenings, as well as utilization of resources such as inpatient services, outpatient services, emergency department use, retinal examinations, and prescriptions (measured by average cost and number of prescriptions per member). Obviously, there are also financial motivations for DM. The market always pushes us to improve efficiency, and in many cases it can be a matter of survival. The current market, however, illustrates one of the predicaments that can result. For the last few years, the cost per member per month for what we have to sell stabilized at approximately $112.00 to $115.00. However, in the last year or so, that figure has dropped to about $85.00 in many markets, a 25% decline in revenues. How do you impose a DM program and bring on the extra staff needed in the face of a 25% reduction in revenues?
FOUNDATIONS FOR EFFECTIVE DISEASE MANAGEMENT I would like to describe a model of the institutional structures necessary for DM to work. Many of us in MCOs and pharmacy benefits managers (PBMs) are attracted to the notion of DM, but we must recognize that a fully operational DM plan rests on a firm foundation of other systems. Because DM depends on integrated information systems and the availability of local clinical pharmacists, each element of 1342
this foundation enhances our ability to manage the quality and cost-effectiveness of drug therapy. At the base of the PBM foundation is the claims processing function, followed by the development of a pharmacy network. Technology assessment and reimbursement policies come next, followed by systems for formulary management and pharmaceutical contracting. Once all these elements are thoroughly integrated, we can put into place the drug utilization evaluation and quality improvement systems necessary for a fully functional DM program. Until each fundamental aspect of PBM activity has been soundly implemented, it can be a mistake to try to leapfrog up to DM, because you are not prepared to do it effectively. My conceptual model of DM is a continuous quality improvement (CQI) process, a means by which we can expect to improve the quality and the cost efficiency of the care that we provide.
REQUIREMENTS FOR DISEASE MANAGEMENT PROGRAMS Several vital elements of good DM programs are also, unfortunately, areas where many programs have serious deficiencies. First, successful DM programs must have effective technical functions. Most importantly, they must have a good, readily accessible database that allows for fairly easy analysis. Evaluation criteria must be statistically valid and reliable, and sufficiently sensitive to measure what you want to measure. There has been less attention paid to the second foundation of a good DM program, which involves clinical considerations. The chief question should be whether a proposed DM program or in-
K.H. SUMMERS
tervention is consistent with the normal care of a patient. We have seen many proposals that suggest that we just force interventions onto physicians from various different sources. But if those proposals greatly interfere with the normal physician-patient relationship, they probably will not be successful. One of the weaknesses of many such proposals is that they are often not developed with sufficient clinical input from medical directors or other clinicians. A third area that is crucial but often overlooked is the managerial function of DM. We continually face the difficulty of implementing DM within the context of all the different care providers and benefit designs we have. As the title of this conference acknowledges, there is often a lot of fantasy about how to meet the admirable goal of improving the quality of care. But actually implementing it is very difficult to do in many cases. I would like to share an example of an ineptly planned contract, one that we did not even submit to formal contract analysis. A firm proposed that they would develop guidelines and patient education materials for patients with diabetes. The initial estimate for the cost to Prudential would have been $200,000.00, which appears reasonable when you consider the costs of treating diabetes. But the final proposal turned out to be $2.9 million simply to give us guidelines and patient education materials. As anyone involved in treating diabetes knows, there is already plenty of patient education material available. Our problem is not getting patient education, it is delivering it to the patient in a way that will actually modify behavior and lead to the best outcomes. This proposal was absolutely off-base in that area.
RESEARCH MODELS AND PRACTICAL PROBLEMS One of the other questions I would like to raise is, "What are your goals for DM?" Many people miss the subtle distinctions between DM and clinical research projects. The fact is that research projects are something of a rearview mirror look at a therapeutic intervention. By the time you have finished the research and get all the data validated and correct, events that occurred during the study may no longer be relevant to ongoing treatments that the health care system is providing. Therefore, it appears more reasonable to approach DM from a managerial perspective than from a research perspective, which takes us back to the CQI approach I mentioned earlier, where constant feedback allows management to make better decisions. A similar problem among some purveyors of DM programs is that they suggest impractical interventions with physicians. The difficulty likely arises from a clash of perspectives: The planners may assume that physicians will operate within the same constraints that would exist in a clinical trial. The fact, of course, is that physicians are paid to take care of patients, and they often do not take kindly to anything that will unnecessarily prolong patients' care or delay their seeing the next patient. They do not have time to deal with a lot of paperwork or interruptions in their daily operations. One significant problem with relying on physician and patient self-reports is that we often encounter reluctance to simply fill out the forms. From the patients' perspective, why should they fill out a scan sheet and submit it? What is the benefit to them? From the physician's per1343
CLINICAL THERAPEUTICS®
Table. Data elements. Categories Administrative Clinical Coverage Dates Medicare Milliman and Robertson Financial Pharmacy Patient Provider
Elements Claim number, MMPO code, location COT ID, ICD-9-CM (1-5), CPT 1-3, LOS, DRG Coverage type, plan type, ID Service, discharge dates Patient ID Diagnosis group, LOS target, % admits AWP, copay/coinsurance, deductible Day's supply, formulary code, therapeutic class SSN, age, HEDIS cohort type Specialty ID, hospital ID
MMPO = managed medical provider organization; COT ID = course of therapy identification; ICD-9-CM= International Classification of Diseases, 9th Revision, Clinical Modification; CPT = Physicians" Current Procedural Terminology; LOS = length of stay; DRG = diagnosis-related group; ID = identification; AWP = average
wholesale price; SSN = Social Security Number; HEDIS = Healthplan Employer Data Information Set. spective, why should patients fill out a 50-item quality-of-life questionnaire? Often, they perceive very little benefit from that. In addition, there is trouble with patient recollection of data, You also have trouble with measurement instruments, particularly with asthma. We found that the classic short form (SF)-36 and now the SF-12 are not sensitive enough to give meaningful feedback on these programs. A more specific instrument is needed (eg, Elizabeth Juniper's Living with Asthma quality-of-life questionnaire). Also, most quality-of-life instruments do not contain data that local administrators want to collect on their patients to determine aspects such as accessibility, patient satisfaction, and other items regarding the impact of these D M programs. DATA C O L L E C T I O N IN PRUDENTIAL'S N E T W O R K We typically use paid claims data to collect data, but Prudential has a stated mis1344
sion to adopt the Epics system, an electronic resource utilization and clinical data system. You can imagine the difficulty, though, of implementing that system planwide for Prudential. We have proceeded with the system in Denver, Colorado, on a trial basis. We find that it is one thing to run an integrated system with an in-house pharmacy and medical group within a single city, and another thing entirely to collect clinical data across an entire network in an independent practice association model health maintenance organization (HMO). An additional difficulty is how to deal with data on patients receiving outof-network care for some treatment. For this presentation, I will limit m y discussion to our paid claims systems. The Prudential paid claims systems is a large database, representing more than 300 million pay lines. With about 75 gigabytes of data, it is a fairly large, awkward system. First, some general caveats about paid claims data. In a fee-for-service environment you get fairly decent data because
K.H. SUMMERS
DataScan® )
Figure 1. Access to operational database for informational queries. DataScan ® is purchased from MedStat (Ann Arbor, Michigan). the provider has a clear economic incentive to submit it. As you move into a capitated environment typical of a group model HMO, the quality of your data goes down, because the providers may have little incentive to provide complete data. Sometimes, the best you may get is an encounter form that may or may not tell you why the patient was seen; often you may not even get that much. The table illustrates the richness and complexity of the data elements in the Prudential data system. I believe it is fairly similar to most other paid claims databases. We have administrative data, claim numbers, MMPO codes (the managed medical provider organizations based out of California), and some clinical data. The pharmacy data are also in the database; just a small sampling includes day's supply, drug name, formulary code, and therapeutic class. The patient, including the HEDIS cohort type, is tracked as well as the provider. The list goes on and on, but these data elements are essentially what we deal with when we are looking at the database.
FROM DATABASE TO DECISION MAKING We are trying to convert raw data into information that helps us make better decisions (Figure 1). We have a wide range of information being combined into DataScan ®, a product that we purchase from MedStat (Ann Arbor, Michigan). At the same time, we can also access our pharmacy databaseS. We use RxFocus ® (Argus, Kansas City, Missouri) as a means to access the network pharmacy database, which is a fairly user-friendly interface. However, when we use RxFocus, we are not able to access data from our in-house group model pharmacy database because they are two separate databases. DataScan contains all the data, but unfortunately, it is not very user-friendly. The program requires a long learning curve, and there is a fairly rapid learning decay if the system is not used on a routine basis. The two purposes for which databases are used--an operational perspective or an informational perspective--have significant effects on the focus, architecture, 1345
CLINICAL THERAPEUTICS*
and needed elements in the data systems. For operational purposes, the main focus is on processing transactions efficiently, and so the database architecture historically involves multiple databases, platforms, and formats, usually running on a variety of "legacy systems"--various systems that have been used and adapted over the years to pay claims. The only information elements for this purpose are billing data, as getting claims processed is the primary focus of the system. On the other hand, the focus of informational data processing is to do queries, analysis, and report generation. For an informational database, a single, highly integrated database that one can easily access is preferred, with a single platform and single format to do the analysis and reporting. Finally, the elements needed for an informational focus must extend beyond just billing data, which lets us know about resource utilization, but must also include clinical data and humanistic data, which are just not available from an operations-oriented database. B U I L D I N G AND U S I N G A MEDICAL DATA W A R E H O U S E A "data warehouse" is a term we use to describe the conversion of an operational database into one that can be used for information purposes. A data warehouse is essentially a staging area for raw data that is periodically extracted from the operational database to make informational analyses possible. The process includes extraction from the operational database(s), conversion of those data into a single format running on a single platform, mapping, reformatting, recalculating, restructuring, and summarizing the data into forms that are more amenable to data queries. 1346
Here is a brief example of how we can go about using information from our data warehouse. To begin with, we found over time that using a relational database, which is very user-friendly, does not work because of the size and the quantity of data there. However, we have found that it takes a fraction of the time to develop an SAS ® program (SAS Institute, Inc., Cary, North Carolina) to query the data from a basic, indexed file than it does to use a relational database to answer the same questions. Therefore, when raw data come from operational databases into the data warehouse, part of the conversion process involves converting it into SAS images. Figure 2 illustrates a simple example: We can identify a drug subset or drug profile, take the indices, identify unique patients fitting those indices and take them out of the pharmacy file, and then go into the medical file to develop a combined matched record set. Conversely, we can take the International Classification of
Diseases, 9th Revision, Clinical Modification code associated with a specific disease state and again do the same process in reverse, matching those diagnoses with the drugs that we would expect to see, and then getting a combined patient record set.
DISEASE SELECTION AND MANAGEMENT IN PRUDENTIAL'S SYSTEM It sometimes appears as if asthma and diabetes are the only two diseases that exist in the DM market. That is largely because HEDIS scorecards run us toward them. Prudential has more than 8 million members nationwide, which varies year to year. Between 3 and 4 million of the patients on the system also have the drug benefit in addition to the medical benefit. Our
K.H. SUMMERS
Primary Subset: DRUG PROFILE (eg, NDCs)
e.a.oo
l
Un,ue,.a,,eo, Rx + IP Case Rx + IP Claim Rx + OP Claim Rx + IP Case + OP Rx + IP Claim + OP
/-~econdary S u b s ~ ( MEDICAL CLAIM ) ~ . D a t a (eg, ICD-9-CM)~.J
0
Combined Match Records
)
Figure 2. Relational data matching. NDCs = National Drug Codes; IDs = identifications; ICD-9-CM = International Classification of Diseases, 9th Revision, Clinical Modification; Rx = treatment; IP = inpatient; OP = outpatient. DM programs focus primarily on our HMO and HMO-Plus products (the managed medical programs), as opposed to some of the administrative services-only accounts or indemnity programs that we also support with drug utilization management. Our managed medical program uses the primary care physician gatekeeper concept, providing a focus of decision making that makes sense to use as a foothold for DM.
PRUDENTIAL'S ASTHMA PROGRAM One of the difficulties of monitoring a program is the timeliness and accuracy of the data. On the one hand you want to provide information feedback to decision makers that will modify their behaviors. In our case, we are trying to meet HEDIS goals for asthma, which involve reducing inpatient admissions. If, on the other hand, you could look at a patient population and
physicians could evaluate how they are doing versus the planned population or the national population, that might be a reasonable goal for monitoring. The problem with that is the delay in getting data so that a timely report can be provided. Additionally, although these measures are not currently implemented, we would like to see some of the health-related quality-of-life instruments used on an ongoing basis to monitor patient care. As I noted earlier, there are problems with both patient and physician motivation when it comes to getting them to actually fill out the forms for such monitoring. This problem will be solved only if physicians see more value to it. One solution would be to take advantage of current technology to scan the forms and get fairly rapid feedback, so that data could be quickly put into report form, something akin to current clinical laboratory reports. The physician could then use that information to make patient care more efficient and ef1347
CLINICAL THERAPEUTICS ®
fective. Currently, there is no reason to talk to a patient about some symptoms they may be having if the symptoms are not immediately bothering them. Better quality-of-life reporting might help physicians more efficiently detect problems. Unfortunately, we have no time to act on such an aggressive proposition fight now. Instead, we are relying on the standard, reliable drug data, which are still reasonably accurate and readily available. Essentially, our monitoring process looks at the utilization of anti-inflammatories as a percentage of both anti-inflammatory prescriptions plus beta-agonist prescriptions. To the extent that patients are treating their asthma more prophylactically as opposed to symptomatically, it suggests that your outcome will be better. We are able to get fairly rapid feedback to the physician, comparing individual patients with the physician's group of patients, then comparing the physician's group with the plan, and finally comparing the plan's average populations with the national population. I will finish by discussing outcomes. As mentioned earlier, by the time we look in the rearview mirror at outcomes, we
1348
will have more time for analysis. Because this is an ongoing program, I expect we will encounter difficulties that we have yet to fully resolve. For instance, I am against using quality-of-life instruments unless they are specific and sensitive enough to measure the outcomes that we expect to see. Given the difficulty in getting patients to fill them out and return them, that appears rather unlikely. "Patient satisfaction" is in all the HEDIS requirements, but I have always had a problem with the loose way that term is used, as well as with the validity and reliability of instruments used to measure it. But we do not know how valid or reliable those instruments are to measure that. CONCLUSIONS I would like to close by reiterating our need for valid, reliable, and sensitive instruments to measure outcomes in DM programs. The measurement process cannot impose unduly on normal activities in physician offices. Furthermore, these measures should provide physicians with helpful information in a timely manner.
Measuring and Monitoring Outcomes of Disease Management Programs Kent H. Summers, RPh, PhD* Clinical Programs and Pharmacoeconomics, Prudential Health Care, Roseland, New Jersey
ABSTRACT After a brief analysis of the financial and health care motives that have led to the current boom in disease management (DM) programs, this paper discusses the pragmatic realities of a particular DM program for treating asthma. A model of the institutional structures necessary for DM to work is presented, emphasizing a process of continuous quality improvement in technical, clinical, and managerial processes. Significant differences between the conventions of controlled clinical triMs and the realities of actual patient care may lead to unrealistic expectations about DM, making sound managerial practices and effective communication even more important. A "data warehouse" can help health care systems master the intricacies
*Current affiliation: Health Outcomes Research Consultant, Eli Lilly and Company, Indianapolis, Indiana.
0149-2918/96/$3.50
of programwide data collection and analysis, making possible sound decisions regarding treatment regimens and changes in physician and patient behavior. This paper concludes with a discussion of how the Prudential Health Care DM program for asthma makes use of the practices and systems discussed above. INTRODUCTION This paper will focus on the pragmatic realities of the disease management (DM) program for asthma of Prudential Health Care, Roseland, New Jersey (hereafter referred to as Prudential). Specifically, after a brief discussion of our operating philoso p h y - t h e " w h y s " - - I will address the practical application--the " h o w s " - - o f our program. First, I want to talk about "why." The overriding concern is quality: We must improve quality in managed-care organizations (MCOs). The National Committee 13 4 1
CLINICAL THERAPEUTICS ®
for Quality Assurance has established standards with their Healthplan Employer Data Information Set (HEDIS) reports that lead to our systems being measured against each other, giving us an impetus to improve quality by becoming involved in DM programs. HEDIS scores are used to evaluate programs on the basis of preventive measures, such as immunizations and screenings, as well as utilization of resources such as inpatient services, outpatient services, emergency department use, retinal examinations, and prescriptions (measured by average cost and number of prescriptions per member). Obviously, there are also financial motivations for DM. The market always pushes us to improve efficiency, and in many cases it can be a matter of survival. The current market, however, illustrates one of the predicaments that can result. For the last few years, the cost per member per month for what we have to sell stabilized at approximately $112.00 to $115.00. However, in the last year or so, that figure has dropped to about $85.00 in many markets, a 25% decline in revenues. How do you impose a DM program and bring on the extra staff needed in the face of a 25% reduction in revenues?
FOUNDATIONS FOR EFFECTIVE DISEASE MANAGEMENT I would like to describe a model of the institutional structures necessary for DM to work. Many of us in MCOs and pharmacy benefits managers (PBMs) are attracted to the notion of DM, but we must recognize that a fully operational DM plan rests on a firm foundation of other systems. Because DM depends on integrated information systems and the availability of local clinical pharmacists, each element of 1342
this foundation enhances our ability to manage the quality and cost-effectiveness of drug therapy. At the base of the PBM foundation is the claims processing function, followed by the development of a pharmacy network. Technology assessment and reimbursement policies come next, followed by systems for formulary management and pharmaceutical contracting. Once all these elements are thoroughly integrated, we can put into place the drug utilization evaluation and quality improvement systems necessary for a fully functional DM program. Until each fundamental aspect of PBM activity has been soundly implemented, it can be a mistake to try to leapfrog up to DM, because you are not prepared to do it effectively. My conceptual model of DM is a continuous quality improvement (CQI) process, a means by which we can expect to improve the quality and the cost efficiency of the care that we provide.
REQUIREMENTS FOR DISEASE MANAGEMENT PROGRAMS Several vital elements of good DM programs are also, unfortunately, areas where many programs have serious deficiencies. First, successful DM programs must have effective technical functions. Most importantly, they must have a good, readily accessible database that allows for fairly easy analysis. Evaluation criteria must be statistically valid and reliable, and sufficiently sensitive to measure what you want to measure. There has been less attention paid to the second foundation of a good DM program, which involves clinical considerations. The chief question should be whether a proposed DM program or in-
K.H. SUMMERS
tervention is consistent with the normal care of a patient. We have seen many proposals that suggest that we just force interventions onto physicians from various different sources. But if those proposals greatly interfere with the normal physician-patient relationship, they probably will not be successful. One of the weaknesses of many such proposals is that they are often not developed with sufficient clinical input from medical directors or other clinicians. A third area that is crucial but often overlooked is the managerial function of DM. We continually face the difficulty of implementing DM within the context of all the different care providers and benefit designs we have. As the title of this conference acknowledges, there is often a lot of fantasy about how to meet the admirable goal of improving the quality of care. But actually implementing it is very difficult to do in many cases. I would like to share an example of an ineptly planned contract, one that we did not even submit to formal contract analysis. A firm proposed that they would develop guidelines and patient education materials for patients with diabetes. The initial estimate for the cost to Prudential would have been $200,000.00, which appears reasonable when you consider the costs of treating diabetes. But the final proposal turned out to be $2.9 million simply to give us guidelines and patient education materials. As anyone involved in treating diabetes knows, there is already plenty of patient education material available. Our problem is not getting patient education, it is delivering it to the patient in a way that will actually modify behavior and lead to the best outcomes. This proposal was absolutely off-base in that area.
RESEARCH MODELS AND PRACTICAL PROBLEMS One of the other questions I would like to raise is, "What are your goals for DM?" Many people miss the subtle distinctions between DM and clinical research projects. The fact is that research projects are something of a rearview mirror look at a therapeutic intervention. By the time you have finished the research and get all the data validated and correct, events that occurred during the study may no longer be relevant to ongoing treatments that the health care system is providing. Therefore, it appears more reasonable to approach DM from a managerial perspective than from a research perspective, which takes us back to the CQI approach I mentioned earlier, where constant feedback allows management to make better decisions. A similar problem among some purveyors of DM programs is that they suggest impractical interventions with physicians. The difficulty likely arises from a clash of perspectives: The planners may assume that physicians will operate within the same constraints that would exist in a clinical trial. The fact, of course, is that physicians are paid to take care of patients, and they often do not take kindly to anything that will unnecessarily prolong patients' care or delay their seeing the next patient. They do not have time to deal with a lot of paperwork or interruptions in their daily operations. One significant problem with relying on physician and patient self-reports is that we often encounter reluctance to simply fill out the forms. From the patients' perspective, why should they fill out a scan sheet and submit it? What is the benefit to them? From the physician's per1343
CLINICAL THERAPEUTICS®
Table. Data elements. Categories Administrative Clinical Coverage Dates Medicare Milliman and Robertson Financial Pharmacy Patient Provider
Elements Claim number, MMPO code, location COT ID, ICD-9-CM (1-5), CPT 1-3, LOS, DRG Coverage type, plan type, ID Service, discharge dates Patient ID Diagnosis group, LOS target, % admits AWP, copay/coinsurance, deductible Day's supply, formulary code, therapeutic class SSN, age, HEDIS cohort type Specialty ID, hospital ID
MMPO = managed medical provider organization; COT ID = course of therapy identification; ICD-9-CM= International Classification of Diseases, 9th Revision, Clinical Modification; CPT = Physicians" Current Procedural Terminology; LOS = length of stay; DRG = diagnosis-related group; ID = identification; AWP = average
wholesale price; SSN = Social Security Number; HEDIS = Healthplan Employer Data Information Set. spective, why should patients fill out a 50-item quality-of-life questionnaire? Often, they perceive very little benefit from that. In addition, there is trouble with patient recollection of data, You also have trouble with measurement instruments, particularly with asthma. We found that the classic short form (SF)-36 and now the SF-12 are not sensitive enough to give meaningful feedback on these programs. A more specific instrument is needed (eg, Elizabeth Juniper's Living with Asthma quality-of-life questionnaire). Also, most quality-of-life instruments do not contain data that local administrators want to collect on their patients to determine aspects such as accessibility, patient satisfaction, and other items regarding the impact of these D M programs. DATA C O L L E C T I O N IN PRUDENTIAL'S N E T W O R K We typically use paid claims data to collect data, but Prudential has a stated mis1344
sion to adopt the Epics system, an electronic resource utilization and clinical data system. You can imagine the difficulty, though, of implementing that system planwide for Prudential. We have proceeded with the system in Denver, Colorado, on a trial basis. We find that it is one thing to run an integrated system with an in-house pharmacy and medical group within a single city, and another thing entirely to collect clinical data across an entire network in an independent practice association model health maintenance organization (HMO). An additional difficulty is how to deal with data on patients receiving outof-network care for some treatment. For this presentation, I will limit m y discussion to our paid claims systems. The Prudential paid claims systems is a large database, representing more than 300 million pay lines. With about 75 gigabytes of data, it is a fairly large, awkward system. First, some general caveats about paid claims data. In a fee-for-service environment you get fairly decent data because
K.H. SUMMERS
DataScan® )
Figure 1. Access to operational database for informational queries. DataScan ® is purchased from MedStat (Ann Arbor, Michigan). the provider has a clear economic incentive to submit it. As you move into a capitated environment typical of a group model HMO, the quality of your data goes down, because the providers may have little incentive to provide complete data. Sometimes, the best you may get is an encounter form that may or may not tell you why the patient was seen; often you may not even get that much. The table illustrates the richness and complexity of the data elements in the Prudential data system. I believe it is fairly similar to most other paid claims databases. We have administrative data, claim numbers, MMPO codes (the managed medical provider organizations based out of California), and some clinical data. The pharmacy data are also in the database; just a small sampling includes day's supply, drug name, formulary code, and therapeutic class. The patient, including the HEDIS cohort type, is tracked as well as the provider. The list goes on and on, but these data elements are essentially what we deal with when we are looking at the database.
FROM DATABASE TO DECISION MAKING We are trying to convert raw data into information that helps us make better decisions (Figure 1). We have a wide range of information being combined into DataScan ®, a product that we purchase from MedStat (Ann Arbor, Michigan). At the same time, we can also access our pharmacy databaseS. We use RxFocus ® (Argus, Kansas City, Missouri) as a means to access the network pharmacy database, which is a fairly user-friendly interface. However, when we use RxFocus, we are not able to access data from our in-house group model pharmacy database because they are two separate databases. DataScan contains all the data, but unfortunately, it is not very user-friendly. The program requires a long learning curve, and there is a fairly rapid learning decay if the system is not used on a routine basis. The two purposes for which databases are used--an operational perspective or an informational perspective--have significant effects on the focus, architecture, 1345
CLINICAL THERAPEUTICS*
and needed elements in the data systems. For operational purposes, the main focus is on processing transactions efficiently, and so the database architecture historically involves multiple databases, platforms, and formats, usually running on a variety of "legacy systems"--various systems that have been used and adapted over the years to pay claims. The only information elements for this purpose are billing data, as getting claims processed is the primary focus of the system. On the other hand, the focus of informational data processing is to do queries, analysis, and report generation. For an informational database, a single, highly integrated database that one can easily access is preferred, with a single platform and single format to do the analysis and reporting. Finally, the elements needed for an informational focus must extend beyond just billing data, which lets us know about resource utilization, but must also include clinical data and humanistic data, which are just not available from an operations-oriented database. B U I L D I N G AND U S I N G A MEDICAL DATA W A R E H O U S E A "data warehouse" is a term we use to describe the conversion of an operational database into one that can be used for information purposes. A data warehouse is essentially a staging area for raw data that is periodically extracted from the operational database to make informational analyses possible. The process includes extraction from the operational database(s), conversion of those data into a single format running on a single platform, mapping, reformatting, recalculating, restructuring, and summarizing the data into forms that are more amenable to data queries. 1346
Here is a brief example of how we can go about using information from our data warehouse. To begin with, we found over time that using a relational database, which is very user-friendly, does not work because of the size and the quantity of data there. However, we have found that it takes a fraction of the time to develop an SAS ® program (SAS Institute, Inc., Cary, North Carolina) to query the data from a basic, indexed file than it does to use a relational database to answer the same questions. Therefore, when raw data come from operational databases into the data warehouse, part of the conversion process involves converting it into SAS images. Figure 2 illustrates a simple example: We can identify a drug subset or drug profile, take the indices, identify unique patients fitting those indices and take them out of the pharmacy file, and then go into the medical file to develop a combined matched record set. Conversely, we can take the International Classification of
Diseases, 9th Revision, Clinical Modification code associated with a specific disease state and again do the same process in reverse, matching those diagnoses with the drugs that we would expect to see, and then getting a combined patient record set.
DISEASE SELECTION AND MANAGEMENT IN PRUDENTIAL'S SYSTEM It sometimes appears as if asthma and diabetes are the only two diseases that exist in the DM market. That is largely because HEDIS scorecards run us toward them. Prudential has more than 8 million members nationwide, which varies year to year. Between 3 and 4 million of the patients on the system also have the drug benefit in addition to the medical benefit. Our
K.H. SUMMERS
Primary Subset: DRUG PROFILE (eg, NDCs)
e.a.oo
l
Un,ue,.a,,eo, Rx + IP Case Rx + IP Claim Rx + OP Claim Rx + IP Case + OP Rx + IP Claim + OP
/-~econdary S u b s ~ ( MEDICAL CLAIM ) ~ . D a t a (eg, ICD-9-CM)~.J
0
Combined Match Records
)
Figure 2. Relational data matching. NDCs = National Drug Codes; IDs = identifications; ICD-9-CM = International Classification of Diseases, 9th Revision, Clinical Modification; Rx = treatment; IP = inpatient; OP = outpatient. DM programs focus primarily on our HMO and HMO-Plus products (the managed medical programs), as opposed to some of the administrative services-only accounts or indemnity programs that we also support with drug utilization management. Our managed medical program uses the primary care physician gatekeeper concept, providing a focus of decision making that makes sense to use as a foothold for DM.
PRUDENTIAL'S ASTHMA PROGRAM One of the difficulties of monitoring a program is the timeliness and accuracy of the data. On the one hand you want to provide information feedback to decision makers that will modify their behaviors. In our case, we are trying to meet HEDIS goals for asthma, which involve reducing inpatient admissions. If, on the other hand, you could look at a patient population and
physicians could evaluate how they are doing versus the planned population or the national population, that might be a reasonable goal for monitoring. The problem with that is the delay in getting data so that a timely report can be provided. Additionally, although these measures are not currently implemented, we would like to see some of the health-related quality-of-life instruments used on an ongoing basis to monitor patient care. As I noted earlier, there are problems with both patient and physician motivation when it comes to getting them to actually fill out the forms for such monitoring. This problem will be solved only if physicians see more value to it. One solution would be to take advantage of current technology to scan the forms and get fairly rapid feedback, so that data could be quickly put into report form, something akin to current clinical laboratory reports. The physician could then use that information to make patient care more efficient and ef1347
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fective. Currently, there is no reason to talk to a patient about some symptoms they may be having if the symptoms are not immediately bothering them. Better quality-of-life reporting might help physicians more efficiently detect problems. Unfortunately, we have no time to act on such an aggressive proposition fight now. Instead, we are relying on the standard, reliable drug data, which are still reasonably accurate and readily available. Essentially, our monitoring process looks at the utilization of anti-inflammatories as a percentage of both anti-inflammatory prescriptions plus beta-agonist prescriptions. To the extent that patients are treating their asthma more prophylactically as opposed to symptomatically, it suggests that your outcome will be better. We are able to get fairly rapid feedback to the physician, comparing individual patients with the physician's group of patients, then comparing the physician's group with the plan, and finally comparing the plan's average populations with the national population. I will finish by discussing outcomes. As mentioned earlier, by the time we look in the rearview mirror at outcomes, we
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will have more time for analysis. Because this is an ongoing program, I expect we will encounter difficulties that we have yet to fully resolve. For instance, I am against using quality-of-life instruments unless they are specific and sensitive enough to measure the outcomes that we expect to see. Given the difficulty in getting patients to fill them out and return them, that appears rather unlikely. "Patient satisfaction" is in all the HEDIS requirements, but I have always had a problem with the loose way that term is used, as well as with the validity and reliability of instruments used to measure it. But we do not know how valid or reliable those instruments are to measure that. CONCLUSIONS I would like to close by reiterating our need for valid, reliable, and sensitive instruments to measure outcomes in DM programs. The measurement process cannot impose unduly on normal activities in physician offices. Furthermore, these measures should provide physicians with helpful information in a timely manner.