COST MEASUREMENT FOR PROVIDER PEER GROUPING

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1 COST MEASUREMENT FOR PROVIDER PEER GROUPING Issue Paper Prepared for the Provider Peer Grouping Advisory Group By Minnesota Department of Health Staff July 10, 2009 The cost of health care is a function of the amount of resources used to provide care, and the amount that was paid for those resources (price). The provider peer grouping system will include analyses of both resource use and price, in total and for specific health conditions. The process of analyzing resource use and cost involves several key steps: Measurement of resource use and price: The first step in the process will be adding up resource use and cost at a patient level, using the multipayer claims database that is being constructed for this purpose. For specific health conditions, this stage of the analysis will need to separate out resource use and cost that is related to the specific condition from other services that were provided to an individual patient over the time period being analyzed. Attribution of patients to providers: Key decisions to be made at this stage of analysis include what criteria should be used in attributing patients to providers for example, whether patients should be attributed to only one provider or potentially to multiple providers. Adjusting for differences in risk across a provider s patient population: This stage of the analysis will involve adjusting for differences in payer mix, patient demographics and comorbidities, severity of illness, and the removal of outliers. This issue paper provides background information related to each of these stages of analysis, identifies key decisions and questions that the Advisory Group will need to address, and presents options and advantages/disadvantages of the options. MEASURING RESOURCE USE AND PRICE In order to compare health care providers on a measure of resource use, it is necessary to have a standardized system of comparing the amount of resources that are needed to provide different types of health care services. For the purpose of provider peer grouping, the encounter claims database will provide information on how much of each particular kind of service was provided by different health care providers, but in order to compare resource use across providers we need a standard way of comparing the intensity of different services (i.e., varying amounts of work, supplies, and other expenses associated with providing a service). For payment purposes, Medicare has created several distinct resource use scales for different types of services. These resource use scales are widely used by commercial health plans as well, sometimes in a modified fashion. 1 For example, Medicare uses the Resource Based Relative 1 Although the relative value scales that are used for Medicare payment purposes are also in wide use by other payers, the systems have been criticized for placing too much weight on technology-intensive services and specialized procedures, and too little weight on primary care services. Critics argue that these distortions have contributed to overuse of some types of services and underuse of others, have contributed to overinvestment in facilities to provide specialized care, and have contributed to shortages of primary care physicians.

2 Value Scale (RBRVS) system to reflect the varying amounts of physician work (time, effort, skill, and stress associated with providing each service), practice expense, and professional liability expenses associated with different types of services. 2 These resource scales allow for direct comparison of different services within a provider type or setting, but not across settings. In other words, using these relative value scales to add up resource use results in an estimate of total resource use for distinct categories of service (e.g., physician services, inpatient, outpatient, drugs, etc.) that cannot be combined into a single measure of resource use. In order to move to a total measure of resource use, it will be necessary to quantify the resource use as a per member per month or per member per year value by multiplying the relative values for each category of service by a standard conversion factor or rate and then summing them up. In Minnesota, HealthPartners has developed its own relative resource measure, called HealthPartners Relative Resource Values (HPRRVs). Applied in combination with unit price measures (fee schedule and member responsibility amounts), the HPRRVs are used to measure the total cost of care. Specifically, HPRRVs quantify the relative resources used for all delivered services, procedures and materials used within an inpatient, outpatient, facility or other provider setting. HPRRV values from different sources of care within the same episode of care can be added together to calculate the total resource utilization amount for that episode or for an all care perspective. While measuring total resource use at the patient level will be a relatively straightforward process using established relative value scales, there are multiple ways of defining and separating out resource use for specific health conditions. Once the Advisory Group has decided which specific conditions to recommend for peer grouping, the next step in the process is to choose a way of defining resource use for these conditions. Typically, this step is done using a standardized definition of an episode of care for the condition. For this step in the process, there are multiple tools available (e.g., multiple commercial software packages, or episode groupers, that define and measure episodes in different ways). 3 Most existing tools have been designed specifically for analysis of data from the commercially insured population, and may perform less well for analyzing resource use for publicly insured populations, which have higher than average resource use and cost often due to multi-morbidity (many conditions); these potential limitations are not well documented. In thinking about which tool is best for Minnesota s provider peer grouping system, the Advisory Group will likely want to consider the following issues: Which tools are currently being used in Minnesota for this purpose? Most health plans in the Minnesota that are analyzing provider resource use for specific conditions 2 Separate scales are used for acute inpatient hospital services (Medicare Severity Diagnosis Related Groups, or MS- DRGs, with 745 separate categories), hospital outpatient services (the Ambulatory Payment Classification system, or APCs), and several other categories of service. The Medicare Payment Advisory Commission (MedPAC) has published a series of issue papers that describe the relative value scales and payment systems that Medicare uses for different types of health care services. These are available at 3 In addition to currently available tools, a variety of other tools are currently in development. For example, a national-level project to design definitions for 20 public domain (i.e., non-proprietary) risk adjusted episodes of care is currently in process. It is unclear whether the results of any of these other efforts will be available in a timely manner for initial implementation of the provider peer grouping system in Minnesota.

3 are currently using the Episode Treatment Group (ETG) software from Ingenix Symmetry. What are the advantages and disadvantages of specific episode groupers relative to each other? In addition to the ETG system, other widely used tools include Thomson Reuters Medical Episode Group (MEG), and the Cave Consulting Group s Marketbasket System. The Medicare Payment Advisory Commission (MedPAC) performed a comparative analysis of the ETG and MEG tools for specific health conditions, and found both to be adequate and reliable tools (although they generate different results). 4 The Technical Panel will consider this question and report back to the Advisory Group on a recommendation for which of the available tools should be used for the peer grouping system. After analysis related to resource use is completed, the next step will be to calculate unit prices. For each provider, a price per unit of resource use (total amount paid divided by total resource use) can be calculated. This can be done in total and separately by payer, which will also facilitate payer mix adjustments at a later stage of the analysis. ATTRIBUTION Another key issue for the Advisory Group to consider is how to attribute patients, procedures, costs, and episodes of care to groups of physicians, hospitals, or care networks. If a patient sees only his or her primary care physician or is admitted to the hospital just once in a given year, the attribution of cost responsibility for that care is reasonably simple. However, if a patient sees more than one physician in the year, or is hospitalized more than once, perhaps in more than one location, then the method employed to attribute responsibility for the cost of health services becomes more complex. Attribution to Physicians Methods or decision rules for assigning physician responsibility for the services provided to patients vary considerably and no single method works perfectly for every physician specialty or population. Attribution for cost and resource utilization may be made to one or more physicians. Single attribution methods are designed to identify an individual physician as the decision maker accountable for all care rendered to a patient; multiple attribution methods recognize the lead physician has incomplete control over treatment by specialists and other physicians. The Advisory Group will need to consider whether the peer grouping system should use one of these two approaches or some combination of the two. Within single attribution methods, patients may be assigned to physicians on the basis of the largest share of their office and other outpatient evaluation and management visits; a percentage of the allowed charges determined by a health plan; or a percentage/count of relative value units of service. Among some commercial health plan products offered in the state, physician responsibility is attributed on the basis of which physician provided the most patient evaluation and management visits (with a minimum threshold of 25% to 30%) for each covered life. In 4 Medicare Payment Advisory Commission, Report to the Congress: Increasing the Value of Medicare, June 2006, chapter 1.

4 contrast, State Employee Group Insurance Program (SEGIP), attributes all care and related cost to the primary care clinic selected by the covered employee at time of open enrollment each year. By accounting for the largest share of patients evaluation and management services, this type of decision rule assumes physician groups or networks can be held responsible for coordinating specific patients care and the cost of the services provided. There are also rules based on classifying physicians into two or three types based on which physician was controlling the initiation of care process and which physician was doing followup. These rules are applied most often to episodes with a clear referral specialist involved. Another potential methodology is to attribute responsibility for an episode to the first provider that a patient saw for the episode. Other considerations include whether, or under what circumstances, resource use and cost should be attributed to multiple providers. If the Advisory Group recommends attributing resource use and cost to multiple providers, it will also need to consider whether and how it would be apportioned among those providers, or whether they would all be held accountable for the entire amount. Selection of a decision rule for attribution involves tradeoffs. Research indicates that attribution to physicians based upon who provided a large percentage of the care tends to leave more patients unattributed and holds physicians responsible for a smaller percentage of the total care they deliver. While a higher threshold in a single attribution approach may be considered more desirable for accuracy in determining provider responsibility for a patient s care, it will also result in an analysis that is less complete. Conversely, the Medicare Payment Advisory Commission found that attributing costs to multiple providers increases the number of physicians included in the analysis. Attribution to multiple providers, however, does not establish as clear a line of accountability with respect to coordinating patient care. In addition, numerous factors may impact the application of any single decision rule, especially when using episode of care defining algorithms, or in establishing attribution rules for diverse populations. It may also be necessary to employ more than one decision rule in order to enhance the accuracy of provider comparisons. Attribution to Hospitals Alternatively, cost attribution rules to hospitals are relatively simple. A base assumption is that the entire hospital s medical staff is responsible for the cost of care provided to a patient. Attribution rules will need to address situations in which patients are transferred from one hospital to another and when patients are discharged from a hospital and subsequently admitted to a second hospital within a specified timeframe. The Advisory Group will need to consider whether or under what circumstances it is appropriate to attribute costs that occur after transfer or readmission to a second hospital to the first hospital that saw the patient.

5 RISK ADJUSTING THE PRICE OF CARE AND RESOURCE UTILIZATION The Institute of Medicine defines risk adjustment as a process that modifies the analysis of performance measurement results by those elements of the patient population that affect results, are out of the control of providers, and are likely to be common and not randomly distributed 5. These elements of the patient population, otherwise known as risk factors, include the following: Demographic characteristics such as patient age, gender, geographic region, socioeconomic status, or race/culture; Severity of illness and other health status differences including diagnostic history, comorbid conditions and risk of mortality; and Insurance benefit variation focused mostly on payer mix adjustments for patients with public vs. private insurance. Of the three categories of risk factors noted above, adjustments made to compensate for payer mix are considered the clearest to understand and the most simple to make. Payer Mix Adjustments In the peer grouping analysis, payer mix adjustment will help mitigate variances in unit prices that are largely a function of the source of insurance for patients. Payer mix adjustments when measuring the cost of care in dollars typically follow one of two tracks: Measurement and comparison of the cost of care separately for patients with commercial insurance, Medicare, or public assistance programs; or Development of conversion factors designed to adjust public program recipient payment levels upwards to the level more typical of the commercial market payment for the same set of services. Development of these conversion factors has evolved over time and allows entire populations to be compared irrespective of insurance source. The methodology would include a standard conversion factor for each type of service calculated as an average of the plan/provider specific relative unit prices for each type of service. This stage of the analysis is also the place to consider whether and how to make adjustments for data that is missing from the information available for peer grouping. Specifically, the Advisory Group should consider whether to recommend the inclusion of some type of adjustment for uncompensated care. Reliable data on uncompensated care that could be used for such an adjustment are available for hospitals, but not for other types of providers. Demographic and Health Status Risk Adjustments 5 Institute of Medicine, Performance Measurement: Accelerating Improvement p 131, 2006.

6 Diagnosis based risk adjustment has been broadly used by the industry for more than a decade. Risk adjustment related to these factors uses a conceptual model to assign a risk level to patients based on available information on their risk factors. These risk scores can be used to adjust per patient per time period costs/resource utilization levels or can be aggregated to the provider level to assess overall patient case mix differences across providers. Expected cost of care can then be adjusted upwards for providers with patients of a higher risk case mix or downward for providers with patients with lower risk case mix so providers won t appear to be more or less costly just because their patients are sicker or healthier than the overall population. Adjusting for demographic and health status risk adjustments requires having data on these patient characteristics. Some of these patient demographic characteristics will be captured in Minnesota s multi-payer encounter claims database, but other key variables that may be of interest will not. For example, the data will include patient-level information related to age, gender and five-digit zip code, but will not include information on race/ethnicity. One issue the Advisory Group will need to consider is the desired scope of risk adjustment for the peer grouping analysis in both the short- and long-term. The benefit of adopting a more sophisticated risk adjustment tool is that it may be able to more accurately adjust for a range of demographic and health status characteristics. An increasing level of complexity of the risk adjustment tool, however, may make analytical results less transparent and less understandable to providers and consumers. The Advisory Group should be aware that adjustments related to some types of demographic characteristics are less well developed. In order to more robustly adjust for socioeconomic factors, for example, it would be necessary to develop and test additional risk adjustment methodologies. The Advisory Group will also need to consider how practical it may be to collect various kinds of demographic and health status data. We have asked the Technical Panel to consider the range of existing risk adjustment tools available in the market and to provide the Advisory Group with a recommendation about which tool may be most appropriate for peer grouping purposes. Outliers Another important consideration is to determine how outliers cases that are highly untypical should be managed in the peer grouping analysis. There can be both a statistical approach to outliers and a rule based approach in which certain types of cases are eliminated from the analysis. An example of a statistical approach would be a decision to exclude any case that is more than three standard deviations above or below the mean; an example of a rule-based approach would be a decision to exclude patients or episodes with costs over a certain threshold (e.g., exclude costs in excess of $200,000 per year, as used in the total cost of care analysis in the state employee group). It is common for high cost outliers to have costs in excess of a defined threshold largely excluded from the analysis and low cost outliers to be eliminated because the very low cost outliers likely represents coding error or missing data. Another example of a rulebased approach would be to exclude some specific types of care, such as trauma care, from analyses of provider performance. While it is often argued that catastrophic cases should be removed from the analysis due lack of provider control, it is also true that excellent care management can help to avoid some of these costs.

7 KEY QUESTIONS As noted earlier in this paper, we will ask the Technical Panel for specific advice and recommendations related to defining episodes of care and risk adjustment methods and report these recommendations back to the Advisory Group. Policy-level questions for consideration by the Advisory Group include the following: What basic approach should be taken with respect to attributing care to physicians and hospitals? Should the peer grouping system analyze results separately by payer type (commercial, Medicare, and public programs) in addition to an aggregated result? What is the desired scope of risk adjustment for the peer grouping analysis over the short and longer-term? How should outliers be managed in the peer grouping analysis?