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Monitoring and evaluating disease management: information requirements
CLINICAL THERAPEUTICS®/VOL. 18, NO. 6, 1996
Monitoring and Evaluating Disease Management: Information Requirements Edward P. Armstrong, PharmD Center for Pharmaceutical Economics and Department of Pharmacy Practice and Science, College of Pharmacy, The University of Arizona, Tucson, Arizona
ABSTRACT This paper addresses information essential to the creation, monitoring, and evaluation of disease management programs within managed-care organizations. Sound procedures for the collection and analysis of data are vital components of any effective disease management program. This presentation argues for a systemwide, rather than an atomistic, approach to data collection and analysis. Because every health system serves different populations, reliable demographic and health resource use data must be collected. Similarly, baseline data on physician prescribing behavior, patient compliance, and treatment costs are necessary to identify areas in need of improvement. Particular care must be taken to ensure that valid statistical models are developed to reflect the realities of the health system. The strengths and weaknesses of various internal and external data sources are 0149-2918/96/$3.50
discussed, with an emphasis on correlating and integrating information to provide comprehensive analyses of treatments and outcomes. The effects of different financial arrangements on data issues are also discussed, particularly in terms of contracting issues at successive stages in the development of a disease management program. This paper examines, in detail, data issues relating to monitoring prescribing behavior, modeling therapy interventions, classifying outcomes, and utilization of resources and treatments. Finally, this presentation makes specific recommendations for designing valid procedures for data collection and analysis. INTRODUCTION When we focus on an individual disease, a disease management (DM) model forces us to examine differences among the principal therapies that we use in treating a disease. It might initially be tempting to 1327
CLINICAL THERAPEUTICS®
limit this examination to pharmaceutical costs, but that view is too narrow. The most accurate analyses of DM programs focus on examining data specific to an individual health system and determining what issues are important in managing a given disease within that health care system. For instance, we know that patient populations may vary. Some health care systems have predominantly elderly patients, others may serve predominantly indigent populations, and still others may include predominantly young, healthy working populations. Therefore, when we analyze the principal treatment options and cost drivers, we may find very different answers when we compare those different health care systems. There can be no "one size fits all" assessments. When we compare therapies, we cannot simply compare drug costs. We must also examine the differences in resources that are consumed or utilized by the health care system, including different therapies, and differences in physician visits, laboratory costs, and other types of costs that may be involved. The other major question about data that we must address is that of outcomes. When we focus on any disease, we need to be able to identify and analyze data that will help us determine the most effective ways to manage patients within a health care system. We must examine how we can improve health care system outcomes and how we can use our resources most effectively in managing specific disease states.
DATA NEEDS
After Cost and Outcomes Data Are Known After we determine estimates of cost and outcome data that are known for a 1328
health care system, the DM model compels us to ask some other probing questions about that disease. First, we must ask how prescribing behavior can potentially be influenced. For instance, we might find that a health care system has four primary therapies for managing a given disease. We might then recognize that two of them are more cost-effective. In a DM program, we would then ask how to direct patients away from the less costeffective treatment pathways and move them toward the more cost-effective pathways. Obviously, there is potential for cost and outcome data to have a significant influence on prescribing behavior, and we must examine ways to ensure that treatment choices truly are cost-effective-with as much emphasis on "effectiveness" as on "cost." Another critical issue is to positively influence patient behavior. Because most DM programs deal with chronic diseases, influencing patient behavior can be extremely important in managing the disease itself, as well as in managing costs. DM programs can potentially influence both providers and patients to adopt behaviors that will result in more cost-effective therapies.
Determining the Data Needed Several important components of DM programs depend on effective data collection and analysis. One of the important issues is modeling different therapy interventions. Our goal should be to create models that let us examine resource use and costs for specific disease states within a health care system perspective. This view means going beyond a simple drugtrial perspective. If we want to know the overall health care system costs involved in comparing different therapies, we will
E.P. ARMSTRONG
obviously need data elements that cover more than just drugs. Another issue typically involved in DM programs is developing treatment guidelines; in many systems, these guidelines are tied to the educational program. In developing such guidelines, we must constantly remain apprised of the relevant literature, so that we are aware of the entire range of recommendations available for managing a disease. We should be aware of whether the Agency for Health Care Policy and Research has information or recommendations available, and we should investigate other treatment guidelines within the state and within the community. When we consider specific drugs, we would seldom want to limit ourselves to the number of prescriptions for a drug or the number of prescriptions per physician. Rather, we would want to link pharmacy data to diagnosis and outcomes. The implications for data collection and analysis can be significant. Health care systems that only have data on their pharmacy output would be unable to make such connections, because they lack the capacity to link the drug and diagnostic data. Finally, educational interventions are a significant component of DM programs. For each disease addressed, we should determine possible educational programs that would be addressed both to providers--physicians and nurse practitioners, for example--and to patients with that disease. Such educational efforts will naturally vary between organizations depending on their patient base and other factors. Some educational programs are stratified by disease severity. For example, there may be general educational information that every patient receives, and then, to address patients with more severe forms of the disease, the education will
become focused on more specialized efforts, sometimes even being tailored to individual patients.
Information Requirements I would argue that for DM programs to operate appropriately, they must be very data intensive, because good data are needed to make better decisions. Information is power, and in a competitive marketplace, the health care systems that have the best information (on their costs, outcomes, and other components) will be the most competitive. Concepts from DM models are vital to the competitive power of health care systems because these concepts help organizations determine the costs and outcomes in managing populations of patients. The health care system's own internal databases are clearly among the critical foundations for effective DM programs. Ideally, we would want to integrate the information from assorted databases that health care systems may have structured in various ways. In integrating them, we would create a relational database that incorporates information on medical claims, pharmacy claims, laboratory claims, urgent care visits, and hospitalizations--all the different components that we need to put together to assess a specific disease state. We can then mobilize powerful quantitative information in analyzing how the health care system uses resources to manage diseases. I must point out, however, that databases do not necessarily have everything that we may need for a DM program. Most significantly, they do not usually measure outcomes other than resource use.* To move beyond resource use data, we must collect additional information. Therefore, DM pro1329
CLINICAL THERAPEUTICS*
grams must consider various external data sources beyond their own databases. We can begin by looking at diseasespecific outcomes in particular patient populations. Although it would be inefficient to evaluate all 24,000 asthma patients in a health care system, we might take a random sample of patients and focus specifically on their outcomes. We can draw logical generalizations about the system's patients and then follow up patients longitudinally. In addition to clinical outcomes, we can also evaluate humanistic outcomes, such as quality of life and patient satisfaction. Some of these evaluations are relatively clear, but others may be more controversial. For instance, in a hypertension DM program, blood pressure is obviously one of the main indicators we would want to observe. We would want to monitor a population of patients and determine how successfully we maintain appropriate blood pressure control for these patients. On the other hand, a disease such as asthma can be more controversial. Some suggest that we should determine the number of symptom-free days for asthma patients within a population. Others would argue for peak flow rates as the main criterion and would want to examine peak flow meter readings to measure the clinical outcome. Obviously, there can be legitimate questions about which outcomes measurement should be used for a disease.
*The Department of Veterans Affairs (VA) database often includes laboratory results, which is unique because most health care systems do not have those results within their database. But the VA also has a disadvantage when tracking outpatient visits. The VA database frequently does not have diagnostic data for each clinic visit. 1330
FINANCIAL ARRANGEMENTS As already noted, DM programs have generated some unique financial relationships that have attracted considerable attention. Most DM programs involve some kind of sharing in cost savings. However, as a counterbalance, there is also risk involved. The DM program can invest considerable resources, but if clinical and economic targets are not reached, then there are no savings to be shared. It is important to define both partners' goals and responsibilities. Obviously, this is less of an issue when a health care system institutes a DM program on its own. Although it is still necessary to meet targets and achieve savings, the risks are borne solely by the health care system itself. Cost savings occur when outcome targets and cost targets are achieved. If we can achieve a particular clinical outcome within a population, within given cost targets, then the contract should define some means for determining what the savings are and for sharing that savings between partners. It is critical to define appropriate targets for any partnership for managing patients' health. Not only must we define the clinical targets that we will aim for, we also must be aware of the economic targets. As noted, we must expand our view beyond drug costs and look at overall health care system costs involved in managing specific diseases. It is much more complicated than simply determining the cost of drugs per member per month.
Issues Cria'cal to Financial Arrangements For partners to enter into successful financial relationships, the contract has to allow for a win-win arrangement; both
E.R ARMSTRONG
partners need incentives to work together, and these incentives should be clearly laid out in their contract. Issues concerning information collection and use must be considered up front in the process of developing contracts. We must identify and agree on claims coding processes, and specify International Clas-
sification of Diseases, 9th Revision, Clinical Modification codes that will be used for program analysis. What process will be used for manipulating or cleaning the data, and how will the data be assessed? What are the Physicians' Current Procedural Terminology (CPT) codes that will be part of the contract? We need to define the costs of particular resources clearly, so that everyone involved in assessing the data will know exactly what the different resource units include. Because of the complexity of these issues, we must specify procedures for data processing and management from the beginning of the contracting process. This way, both parties can clearly determine whether targets have been reached and ensure that everybody is in agreement with the results. We must be able to know, for example, whether the clinical and economic targets have been reached. To know that, we must first agree on the methods to be used in assessing what "reaching the targets" means. We also need to define in the contract what steps will be involved in the processing of data, so that everyone understands how the program will be analyzed.
Re-Contracting Many people tend to overlook the question of re-contracting in monitoring DM programs. At the beginning of a DM program, we start with baseline data that tell us what is happening within the health
care system and how we are managing the disease. Once the program starts, we see some aggressive efforts at shifting physician prescribing behavior, as well as efforts to improve patient education, so that everyone concerned will adopt more health-conscious and cost-conscious behavior. After this intense effort, we should see changes beginning to occur in costs and outcomes. However, we should also know that we will reach a plateau where the initial gains start leveling off, as both physicians and patients adopt healthier, more cost-effective practices. The biggest savings will almost always occur in the short term. We need to consider how such plateaus will affect the partners in DM when the time for re-contracting arrives. We need to give more thought to planning for the conclusion of this initial stage and for the eventual conversion of the DM program to a maintenance stage. Some programs have attempted to shift payment options between the two phases of DM---during the initial stage, when savings are likely to be most significant, they might use a risk-sharing approach, and then during the maintenance phase, they might shift to a capitated arrangement. It is not always this simple, of course, because legal issues surrounding partnering agreements may vary from state to state. At this point, no one can reliably predict exactly when to make the transition from the initial intensive stage to the maintenance stage of a DM program in a health care system.
MONITORING PRESCRIBING BEHAVIOR DM programs often depend on changing prescribing behavior. This dependence is somewhat similar to some drug use evaluation programs in that when we merge 1331
CLINICAL THERAPEUTICS ®
member data with pharmacy claims and medical data to create a relational database, we can then examine prescriber compliance in different diagnostic categories. Such databases allow us to look at first- and second-line agents and to assess therapies by diagnosis and by physician. This method is far more powerful than reports using only pharmacy claims. We can go beyond just looking at a drug cost per member per month or how much of a drug a specific physician is using. Instead, this approach allows us to see, by diagnosis and by physician, what is being used as first-line and second-line agents. We can easily compare physicians' actions with treatment guidelines, and we can more easily see when medications are being used outside of treatment guidelines. If we are using an expensive second-line agent, we can determine whether it is being used appropriately for a disease when another agent failed, or whether it is being used inappropriately, when a first-line agent would be a more appropriate therapy. In addition, we can adjust our educational efforts accordingly, depending on the analysis. Assessments of prescribing behavior can then be used for more effective marketing of the treatment program guidelines. If we find physicians who are not following treatment guidelines, we can then focus our educational efforts on them. T H E R A P Y INTERVENTION MODELING I want to briefly discuss modeling within the framework of the information requirements necessary for DM programs. As I have noted already, what is most useful to decision makers is not just a drug analysis but taking a health care system's perspective--a "macro" perspective---in an1332
alyzing how therapies are being used to manage specific diseases. We must examine the impact of therapy interventions and analyze cost-outcomes relationships in the context of modeling equilibrium processes; that is, we need to determine the range of therapies before and after we make some kind of change, such as instituting a treatment guideline. Such analysis is very powerful because when we look at a health care system, we can determine the primary therapies (or principal therapy options) being used in that health care system for a given disease. We can also identify the principal cost drivers for that disease. We can examine the equilibritun issues and determine how much it costs the health care system to manage that disease. Many health care systems do not actually know how much they spend to manage a disease. A health care system will know, to the penny, what they pay for physician visits and hospitalizations, but when we think in terms of DM, and ask how much it costs to manage a certain disease, most health care systems in the United States are unable to determine total costs. They simply have never looked at the data from a DM perspective. The DM approach is causing health care systems to reevaluate some of these issues. At the risk of taxing an overused term, I would suggest that we are experiencing a major "paradigm shift" that may require us to reconstruct our attitudes. The challenge we face is to adapt our procedures of data collection and evaluation appropriately to this new approach.
OBJECTIVES OF THERAPY INTERVENTION MODELING Therapy intervention modeling can provide the basis for an assessment of the ef-
E.R ARMSTRONG
fectiveness and efficiency of health care delivery. With such models, we should remember to include some of the "messy realities" of real life, where we must deal with comorbidities and compliance problems in patient populations. Modeling gives us a systemwide framework, enabling us to compare different treatment options. It also can be useful when evaluating new therapies. When a new drug comes onto the market or there is an agent that we are considering adding to the formulary, we can build it into the model and examine the potential impacts on the main therapies, costs, and outcomes within that specific disease area. Finally, modeling can be a powerful tool for evaluating the impact of modifying prescribing behavior. When we have a model in a specific health care system and we know what the costs and outcomes are, we can estimate the likely impact of shifts in prescribing behavior on future costs and outcomes. E S T A B L I S H I N G DATABASE PARAMETERS I would like to briefly discuss some important database parameters that must be considered in a DM program. To begin with, we must look at disease classification and defining particular CPT codes that will be used within the DM contract. Further, we must have some agreed-on definitions of resources as they are used within a health care system. There are few standardized
definitions of resources and their dollar values that hold true for all health care systems. Such elements must be defined up front (eg, capitated primary care physician visits, fee-for-service specialty visits, laboratory costs, drug costs, and rebates). We must also define how drug types will be classified, and we must establish a clear time frame of analysis. If, for instance, a study covers 1 or 2 years, we need to determine if there are seasonality issues that need to be considered for certain diseases. Finally, there is the complicated matter of defining "disease event" profiles. How do we determine what all the costs incurred are, from the time patients enter the health care system until they are cured of their acute disease? CONCLUSIONS In conclusion, I would like to reiterate the importance of taking a health care system's perspective, beyond just a drug's perspective. To do that, we must draw on internal sources of data from a health care system's database and use external data to assess clinical and humanistic outcomes. I think it is important to evaluate the principal treatment options for diseases and to determine the cost-outcome relationships for those specific primary therapies. We can then examine DM agreements and determine whether a health care system should develop them internally or whether they would be better served by working with a parmer.
1333
Monitoring and Evaluating Disease Management: Information Requirements Edward P. Armstrong, PharmD Center for Pharmaceutical Economics and Department of Pharmacy Practice and Science, College of Pharmacy, The University of Arizona, Tucson, Arizona
ABSTRACT This paper addresses information essential to the creation, monitoring, and evaluation of disease management programs within managed-care organizations. Sound procedures for the collection and analysis of data are vital components of any effective disease management program. This presentation argues for a systemwide, rather than an atomistic, approach to data collection and analysis. Because every health system serves different populations, reliable demographic and health resource use data must be collected. Similarly, baseline data on physician prescribing behavior, patient compliance, and treatment costs are necessary to identify areas in need of improvement. Particular care must be taken to ensure that valid statistical models are developed to reflect the realities of the health system. The strengths and weaknesses of various internal and external data sources are 0149-2918/96/$3.50
discussed, with an emphasis on correlating and integrating information to provide comprehensive analyses of treatments and outcomes. The effects of different financial arrangements on data issues are also discussed, particularly in terms of contracting issues at successive stages in the development of a disease management program. This paper examines, in detail, data issues relating to monitoring prescribing behavior, modeling therapy interventions, classifying outcomes, and utilization of resources and treatments. Finally, this presentation makes specific recommendations for designing valid procedures for data collection and analysis. INTRODUCTION When we focus on an individual disease, a disease management (DM) model forces us to examine differences among the principal therapies that we use in treating a disease. It might initially be tempting to 1327
CLINICAL THERAPEUTICS®
limit this examination to pharmaceutical costs, but that view is too narrow. The most accurate analyses of DM programs focus on examining data specific to an individual health system and determining what issues are important in managing a given disease within that health care system. For instance, we know that patient populations may vary. Some health care systems have predominantly elderly patients, others may serve predominantly indigent populations, and still others may include predominantly young, healthy working populations. Therefore, when we analyze the principal treatment options and cost drivers, we may find very different answers when we compare those different health care systems. There can be no "one size fits all" assessments. When we compare therapies, we cannot simply compare drug costs. We must also examine the differences in resources that are consumed or utilized by the health care system, including different therapies, and differences in physician visits, laboratory costs, and other types of costs that may be involved. The other major question about data that we must address is that of outcomes. When we focus on any disease, we need to be able to identify and analyze data that will help us determine the most effective ways to manage patients within a health care system. We must examine how we can improve health care system outcomes and how we can use our resources most effectively in managing specific disease states.
DATA NEEDS
After Cost and Outcomes Data Are Known After we determine estimates of cost and outcome data that are known for a 1328
health care system, the DM model compels us to ask some other probing questions about that disease. First, we must ask how prescribing behavior can potentially be influenced. For instance, we might find that a health care system has four primary therapies for managing a given disease. We might then recognize that two of them are more cost-effective. In a DM program, we would then ask how to direct patients away from the less costeffective treatment pathways and move them toward the more cost-effective pathways. Obviously, there is potential for cost and outcome data to have a significant influence on prescribing behavior, and we must examine ways to ensure that treatment choices truly are cost-effective-with as much emphasis on "effectiveness" as on "cost." Another critical issue is to positively influence patient behavior. Because most DM programs deal with chronic diseases, influencing patient behavior can be extremely important in managing the disease itself, as well as in managing costs. DM programs can potentially influence both providers and patients to adopt behaviors that will result in more cost-effective therapies.
Determining the Data Needed Several important components of DM programs depend on effective data collection and analysis. One of the important issues is modeling different therapy interventions. Our goal should be to create models that let us examine resource use and costs for specific disease states within a health care system perspective. This view means going beyond a simple drugtrial perspective. If we want to know the overall health care system costs involved in comparing different therapies, we will
E.P. ARMSTRONG
obviously need data elements that cover more than just drugs. Another issue typically involved in DM programs is developing treatment guidelines; in many systems, these guidelines are tied to the educational program. In developing such guidelines, we must constantly remain apprised of the relevant literature, so that we are aware of the entire range of recommendations available for managing a disease. We should be aware of whether the Agency for Health Care Policy and Research has information or recommendations available, and we should investigate other treatment guidelines within the state and within the community. When we consider specific drugs, we would seldom want to limit ourselves to the number of prescriptions for a drug or the number of prescriptions per physician. Rather, we would want to link pharmacy data to diagnosis and outcomes. The implications for data collection and analysis can be significant. Health care systems that only have data on their pharmacy output would be unable to make such connections, because they lack the capacity to link the drug and diagnostic data. Finally, educational interventions are a significant component of DM programs. For each disease addressed, we should determine possible educational programs that would be addressed both to providers--physicians and nurse practitioners, for example--and to patients with that disease. Such educational efforts will naturally vary between organizations depending on their patient base and other factors. Some educational programs are stratified by disease severity. For example, there may be general educational information that every patient receives, and then, to address patients with more severe forms of the disease, the education will
become focused on more specialized efforts, sometimes even being tailored to individual patients.
Information Requirements I would argue that for DM programs to operate appropriately, they must be very data intensive, because good data are needed to make better decisions. Information is power, and in a competitive marketplace, the health care systems that have the best information (on their costs, outcomes, and other components) will be the most competitive. Concepts from DM models are vital to the competitive power of health care systems because these concepts help organizations determine the costs and outcomes in managing populations of patients. The health care system's own internal databases are clearly among the critical foundations for effective DM programs. Ideally, we would want to integrate the information from assorted databases that health care systems may have structured in various ways. In integrating them, we would create a relational database that incorporates information on medical claims, pharmacy claims, laboratory claims, urgent care visits, and hospitalizations--all the different components that we need to put together to assess a specific disease state. We can then mobilize powerful quantitative information in analyzing how the health care system uses resources to manage diseases. I must point out, however, that databases do not necessarily have everything that we may need for a DM program. Most significantly, they do not usually measure outcomes other than resource use.* To move beyond resource use data, we must collect additional information. Therefore, DM pro1329
CLINICAL THERAPEUTICS*
grams must consider various external data sources beyond their own databases. We can begin by looking at diseasespecific outcomes in particular patient populations. Although it would be inefficient to evaluate all 24,000 asthma patients in a health care system, we might take a random sample of patients and focus specifically on their outcomes. We can draw logical generalizations about the system's patients and then follow up patients longitudinally. In addition to clinical outcomes, we can also evaluate humanistic outcomes, such as quality of life and patient satisfaction. Some of these evaluations are relatively clear, but others may be more controversial. For instance, in a hypertension DM program, blood pressure is obviously one of the main indicators we would want to observe. We would want to monitor a population of patients and determine how successfully we maintain appropriate blood pressure control for these patients. On the other hand, a disease such as asthma can be more controversial. Some suggest that we should determine the number of symptom-free days for asthma patients within a population. Others would argue for peak flow rates as the main criterion and would want to examine peak flow meter readings to measure the clinical outcome. Obviously, there can be legitimate questions about which outcomes measurement should be used for a disease.
*The Department of Veterans Affairs (VA) database often includes laboratory results, which is unique because most health care systems do not have those results within their database. But the VA also has a disadvantage when tracking outpatient visits. The VA database frequently does not have diagnostic data for each clinic visit. 1330
FINANCIAL ARRANGEMENTS As already noted, DM programs have generated some unique financial relationships that have attracted considerable attention. Most DM programs involve some kind of sharing in cost savings. However, as a counterbalance, there is also risk involved. The DM program can invest considerable resources, but if clinical and economic targets are not reached, then there are no savings to be shared. It is important to define both partners' goals and responsibilities. Obviously, this is less of an issue when a health care system institutes a DM program on its own. Although it is still necessary to meet targets and achieve savings, the risks are borne solely by the health care system itself. Cost savings occur when outcome targets and cost targets are achieved. If we can achieve a particular clinical outcome within a population, within given cost targets, then the contract should define some means for determining what the savings are and for sharing that savings between partners. It is critical to define appropriate targets for any partnership for managing patients' health. Not only must we define the clinical targets that we will aim for, we also must be aware of the economic targets. As noted, we must expand our view beyond drug costs and look at overall health care system costs involved in managing specific diseases. It is much more complicated than simply determining the cost of drugs per member per month.
Issues Cria'cal to Financial Arrangements For partners to enter into successful financial relationships, the contract has to allow for a win-win arrangement; both
E.R ARMSTRONG
partners need incentives to work together, and these incentives should be clearly laid out in their contract. Issues concerning information collection and use must be considered up front in the process of developing contracts. We must identify and agree on claims coding processes, and specify International Clas-
sification of Diseases, 9th Revision, Clinical Modification codes that will be used for program analysis. What process will be used for manipulating or cleaning the data, and how will the data be assessed? What are the Physicians' Current Procedural Terminology (CPT) codes that will be part of the contract? We need to define the costs of particular resources clearly, so that everyone involved in assessing the data will know exactly what the different resource units include. Because of the complexity of these issues, we must specify procedures for data processing and management from the beginning of the contracting process. This way, both parties can clearly determine whether targets have been reached and ensure that everybody is in agreement with the results. We must be able to know, for example, whether the clinical and economic targets have been reached. To know that, we must first agree on the methods to be used in assessing what "reaching the targets" means. We also need to define in the contract what steps will be involved in the processing of data, so that everyone understands how the program will be analyzed.
Re-Contracting Many people tend to overlook the question of re-contracting in monitoring DM programs. At the beginning of a DM program, we start with baseline data that tell us what is happening within the health
care system and how we are managing the disease. Once the program starts, we see some aggressive efforts at shifting physician prescribing behavior, as well as efforts to improve patient education, so that everyone concerned will adopt more health-conscious and cost-conscious behavior. After this intense effort, we should see changes beginning to occur in costs and outcomes. However, we should also know that we will reach a plateau where the initial gains start leveling off, as both physicians and patients adopt healthier, more cost-effective practices. The biggest savings will almost always occur in the short term. We need to consider how such plateaus will affect the partners in DM when the time for re-contracting arrives. We need to give more thought to planning for the conclusion of this initial stage and for the eventual conversion of the DM program to a maintenance stage. Some programs have attempted to shift payment options between the two phases of DM---during the initial stage, when savings are likely to be most significant, they might use a risk-sharing approach, and then during the maintenance phase, they might shift to a capitated arrangement. It is not always this simple, of course, because legal issues surrounding partnering agreements may vary from state to state. At this point, no one can reliably predict exactly when to make the transition from the initial intensive stage to the maintenance stage of a DM program in a health care system.
MONITORING PRESCRIBING BEHAVIOR DM programs often depend on changing prescribing behavior. This dependence is somewhat similar to some drug use evaluation programs in that when we merge 1331
CLINICAL THERAPEUTICS ®
member data with pharmacy claims and medical data to create a relational database, we can then examine prescriber compliance in different diagnostic categories. Such databases allow us to look at first- and second-line agents and to assess therapies by diagnosis and by physician. This method is far more powerful than reports using only pharmacy claims. We can go beyond just looking at a drug cost per member per month or how much of a drug a specific physician is using. Instead, this approach allows us to see, by diagnosis and by physician, what is being used as first-line and second-line agents. We can easily compare physicians' actions with treatment guidelines, and we can more easily see when medications are being used outside of treatment guidelines. If we are using an expensive second-line agent, we can determine whether it is being used appropriately for a disease when another agent failed, or whether it is being used inappropriately, when a first-line agent would be a more appropriate therapy. In addition, we can adjust our educational efforts accordingly, depending on the analysis. Assessments of prescribing behavior can then be used for more effective marketing of the treatment program guidelines. If we find physicians who are not following treatment guidelines, we can then focus our educational efforts on them. T H E R A P Y INTERVENTION MODELING I want to briefly discuss modeling within the framework of the information requirements necessary for DM programs. As I have noted already, what is most useful to decision makers is not just a drug analysis but taking a health care system's perspective--a "macro" perspective---in an1332
alyzing how therapies are being used to manage specific diseases. We must examine the impact of therapy interventions and analyze cost-outcomes relationships in the context of modeling equilibrium processes; that is, we need to determine the range of therapies before and after we make some kind of change, such as instituting a treatment guideline. Such analysis is very powerful because when we look at a health care system, we can determine the primary therapies (or principal therapy options) being used in that health care system for a given disease. We can also identify the principal cost drivers for that disease. We can examine the equilibritun issues and determine how much it costs the health care system to manage that disease. Many health care systems do not actually know how much they spend to manage a disease. A health care system will know, to the penny, what they pay for physician visits and hospitalizations, but when we think in terms of DM, and ask how much it costs to manage a certain disease, most health care systems in the United States are unable to determine total costs. They simply have never looked at the data from a DM perspective. The DM approach is causing health care systems to reevaluate some of these issues. At the risk of taxing an overused term, I would suggest that we are experiencing a major "paradigm shift" that may require us to reconstruct our attitudes. The challenge we face is to adapt our procedures of data collection and evaluation appropriately to this new approach.
OBJECTIVES OF THERAPY INTERVENTION MODELING Therapy intervention modeling can provide the basis for an assessment of the ef-
E.R ARMSTRONG
fectiveness and efficiency of health care delivery. With such models, we should remember to include some of the "messy realities" of real life, where we must deal with comorbidities and compliance problems in patient populations. Modeling gives us a systemwide framework, enabling us to compare different treatment options. It also can be useful when evaluating new therapies. When a new drug comes onto the market or there is an agent that we are considering adding to the formulary, we can build it into the model and examine the potential impacts on the main therapies, costs, and outcomes within that specific disease area. Finally, modeling can be a powerful tool for evaluating the impact of modifying prescribing behavior. When we have a model in a specific health care system and we know what the costs and outcomes are, we can estimate the likely impact of shifts in prescribing behavior on future costs and outcomes. E S T A B L I S H I N G DATABASE PARAMETERS I would like to briefly discuss some important database parameters that must be considered in a DM program. To begin with, we must look at disease classification and defining particular CPT codes that will be used within the DM contract. Further, we must have some agreed-on definitions of resources as they are used within a health care system. There are few standardized
definitions of resources and their dollar values that hold true for all health care systems. Such elements must be defined up front (eg, capitated primary care physician visits, fee-for-service specialty visits, laboratory costs, drug costs, and rebates). We must also define how drug types will be classified, and we must establish a clear time frame of analysis. If, for instance, a study covers 1 or 2 years, we need to determine if there are seasonality issues that need to be considered for certain diseases. Finally, there is the complicated matter of defining "disease event" profiles. How do we determine what all the costs incurred are, from the time patients enter the health care system until they are cured of their acute disease? CONCLUSIONS In conclusion, I would like to reiterate the importance of taking a health care system's perspective, beyond just a drug's perspective. To do that, we must draw on internal sources of data from a health care system's database and use external data to assess clinical and humanistic outcomes. I think it is important to evaluate the principal treatment options for diseases and to determine the cost-outcome relationships for those specific primary therapies. We can then examine DM agreements and determine whether a health care system should develop them internally or whether they would be better served by working with a parmer.
1333