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Strategies for a BEA Satellite Health Care Account: Summary of a Workshop (2009)

Chapter: 3 Price Indexes: Calculating Real Medical Care GDP

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Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
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Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
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Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
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Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
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Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
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Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
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Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
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Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
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Page 37
Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
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Page 38
Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
×
Page 39
Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
×
Page 40
Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
×
Page 41
Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
×
Page 42
Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
×
Page 43
Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
×
Page 44
Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
×
Page 45
Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
×
Page 46
Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
×
Page 47
Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
×
Page 48
Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
×
Page 49
Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
×
Page 50
Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
×
Page 51
Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
×
Page 52
Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
×
Page 53
Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
×
Page 54
Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
×
Page 55
Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
×
Page 56
Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
×
Page 57
Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
×
Page 58
Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
×
Page 59
Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
×
Page 60
Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
×
Page 61
Suggested Citation:"3 Price Indexes: Calculating Real Medical Care GDP." National Research Council. 2009. Strategies for a BEA Satellite Health Care Account: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12494.
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Page 62

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3 Price Indexes: Calculating Real Medical Care GDP Although the term “satellite health care account” features in the title of the workshop, much of what was discussed over the course of the day had to do with medical care price deflation—the use of price indexes to estimate real changes in the levels of inputs and outputs for the sector. Matthew Shapiro, who has done seminal work on the topic, began his comments by noting that a big part of the task for the Bureau of Economic Analysis (BEA) involves parsing out nominal expenditures in a way that is meaningful and conducive to measuring prices. BEA is already in the business of developing price indexes for the purpose of calculating real levels of economic activity, on an industry by industry basis, for the national accounts; this responsibility is particularly demanding for the medical sector in which third-party payments, and the fact that transactions do not occur in textbook competitive markets, confound price measurement. Some aspects of this problem have already been dealt with on the nominal side of the accounts by allocating the actual expenditures back to the consumer, to the gov- ernment, and so on, regardless of who actually pays, which is often an insurance company. Once the nominal flows for the sector have been figured out correctly, which is a daunting problem in itself, the next task in developing the new BEA health care account is to begin estimating the disease-based price indexes. Shapiro endorsed this two-stage strategy, although he noted that, for other purposes, there were different ways—in addition to the disease unit concept—that are also use- ful health care price measurement. For example, hospitals would want to know about prices specifically for its industry. However, to get the price indexes from the consumer standpoint, disease by disease unit pricing seems more appropriate than a traditional industry-based approach or than a global ­pricing of population 30

PRICE INDEXES 31 health. Even if, ultimately, the goal is to measure the price of an incremental gain in health, Shapiro argued that, for economic accounting purposes, one is driven by the logic of the disease by disease approach, which was the focus of much of the workshop. A key question, raised by Steve Heffler (Centers for Medicare and ­Medicaid Services), is how the methods for parsing nominal expenditures by disease (described in Chapter 2) relate to appropriate price measurement. Ana Aizcorbe responded that those working on satellite accounts—the Cutler-Rosen group or BEA—first establish a number of disease “buckets” that make sense to the medi- cal community; these buckets become the unit of observation for which spend- ing and health effects data are collected. The dollar total spent per patient on a particular category—for example, diabetes—becomes the price for the newly defined unit of health service. Then, nominal spending on diabetes is given a weight based on its share of total medical care spending. Likewise, indexes for each disease category are weighted then aggregated. Aizcorbe cautioned that BEA is still in the phase of attempting to figure out the best way to define diseases, and that developing these kinds of indexes is still a ways off. BEA has purchased some databases covering patients who are commercially insured with the intention of experimenting with different types of indexes and different ways of defining diseases. In this context, Aizcorbe described the most important problem with producer price indexes for purposes of the satellite account envisioned by BEA: they do not identify the medical care good or service that is sought by the ­consumer—which most think should be the treatment of a particular disease or condition. She added that health economists have developed the conceptual tools that are needed to remedy the situation, and that putting these approaches into practice is something that BEA would be working on right away. Much of the academic literature has relied on patient claims data to provide a picture of price trends for treating specific conditions. The economic good has been defined as a completed episode. For example, for a heart attack patient, this may involve time and expenditures on a series of initial treatments plus those that take place during the recovery period. At the end of that episode, data are collected to estimate all dollars spent over the entire period; this forms the basis for pricing a completed episode. To develop a comprehensive health care account with this kind of underpin- ning, claims data would be needed for as much of the population as possible; Medicare and Medicaid would provide large portions. However, there are some groups for which claims data will not be available—most obvious are the unin- sured, who do not submit claims—so their spending would have to be measured another way. Patients from some kinds of institutions are also not typically   In a fully evolved price measurement program, tracking nominal dollars spent on treatments would be viewed as only a first step. As discussed in detail in Section 3.3., a fully meaningful price measure must ultimately also consider how the quality of a treatment changes over time.

32 Strategies for a BEA Satellite Health CARE Account included in these sources, so BEA will be investigating ways of obtaining spend- ing data for them as well. Aizcorbe also noted that the timing aspect of the treatment-based unit does not lend itself seamlessly to deflation in the national accounts. For example, for delivery of a baby born in January, most of the dollars are for services provided in the previous nine months. So, when pricing the completed episode, the report- ing takes place in the year following the nominal expenditure outlays. Aizcorbe stated that, ideally, the price index should line up with the time period in which the spending actually occurred. There are other areas of the national income and product accounts (NIPAs) that share this issue (payment, consumption, or return from investment takes place beyond some point); one way to handle it is to think in terms of the price per patient over some predetermined period of time. 3.1. Pricing Treatments to Capture Changing Technologies, Input Substitution, and Population Heterogeneity A disease-specific index must embed a capability to capture the substitution of medical care treatment inputs that takes place over time. Aizcorbe used the example of treatment for depression, which has transitioned from a high to a low reliance on talk therapy as less expensive alternatives—specifically antidepressant drugs—were introduced and proliferated. Tracking patients with this condition over the past few decades would have revealed some portion switching from talk therapy to drug therapy. As this has occurred, the average amount spent on treatment of depression has fallen. However, standard price indexes do not pick up this change because they track the price of talk therapy and of drug therapy independently, and therefore they do not catch the fact that people are switching from one to the other. Even if there is no innovation in prescription drugs and no price change in either approach to treating depression, the per-patient cost falls because this substitution has occurred. If the standard indexes are used to deflate nominal spending, the resulting measure ends up showing a drop in real spend- ing or a drop in the quantity, when in fact the same number of patients are being treated for depression. For its satellite health care account, BEA proposes to take the system-wide spending over some period of time in a treatment (such as for depression), regardless of treatment mode, and divide it by the number of patients treated. The idea is to calculate a unit value that counts all of the spending and allows for substitution across treatment types for each specific condition. Patricia Danzon, who spoke about the pharmaceutical industry, cited the growing prevalence in the market of biologics—biological products made from living organisms whose uses are similar to conventional drugs—as another exam- ple of a switch in technology that BLS will need to confront. At the moment, both a pharmaceutical index and a biotechnology index exist. But, Danzon noted, if the goal is to estimate change in pharmaceutical prices accurately, then the current

PRICE INDEXES 33 movement toward more biologics and fewer chemical-based drugs needs to be captured. To the extent that they are measured in separate industries, a problem arises because the index will not capture the biologics that are dispensed through physicians’ offices or retail pharmacies. They are likely to be picked up by Medi- care as Part B drugs, but probably for many of the other databases, they are just part of physicians’ services. As these biologics become a significant share of total pharmaceutical spending—and they will, especially at the expensive end—it will become increasingly important to make sure that they are correctly allocated to pharmaceuticals (as opposed to physician spending). Jack Triplett described other examples of how spending by traditionally defined medical care industries combine into the price vector describing a specific treatment. Under present BLS procedures for cataracts—a case that was cited several times throughout the day—if the surgeries taking place in a hospital are sampled, then one set of price indexes would be generated for that; if surgeries shift to a clinic, then another set of price indexes would be obtained for that. If people switch from the more expensive hospital surgery to a less expensive clinic surgery, and if quality does not suffer, the ideal price index (from the perspective of the patient) would be capable of capturing the decrease in price. Triplett continued, noting that one reason people have not thought much about substitution across medical care industries is that BEA data are organized at a higher level. BEA industry counts do not go down to the five-digit North American Industry Classification System (NAICS) level of aggregation, and therefore not all of the reallocation effects are readily visible. He offered an analogy between the medical industries and the transportation equipment–producing industries to illustrate the industry-sector problem: the historical case of the automobile industry replacing the buggy industry. If the industry were defined as producing road trans- portation equipment instead of individual cars and buggies (although one might still want to get the prices of those), then in principle these substitution effects could be captured. If only the carriage and automobile industries were tracked, price and productivity measurement would capture only part of the effects that are of interest. When people found that it was cheaper per mile to go by car than by horse and car- riage, and they switched from the latter to the former, the full price and productivity effects would not be completely explained by the indexes for either one. However, this problem arises when the interest is in welfare comparisons, rather than just in output comparisons. There is ­nothing wrong with the auto and buggy measures; rather, it is that aggregating them misses some of the welfare gains to the consumer. Triplett concluded that BEA would need to rework the way the five-digit industries are aggregated into the three-digit industries to do this—it is not only an index num-   NAICS uses a six-digit coding system to identify particular industries and their placement in the hierarchical structure of the classification system. The first two digits of the code designate the s ­ ector, the third digit designates the subsector, the fourth digit designates the industry group, the fifth digit designates the NAICS industry, and the sixth digit designates the national industry (http://www. census.gov/eos/www/naics/).

34 Strategies for a BEA Satellite Health CARE Account ber problem. The usual way of modeling “substitution” in price index research does not adequately handle shifts of broadly defined products (like road transportation equipment or curing cataracts) between producing industries. Triplett presumed that there are probably lots of instances of something similar happening, simply because a function is going out of one sector and into another one. Steven Landefeld agreed that these transitions probably do occur with some frequency. He noted that, wherever BEA has used quality adjustment methods, the focus has been almost exclusively been on final goods and services. That is, the agency has typically assigned the adjustment into the industry pro- ducing the final good.  In this case, the goal is to examine how the change in the standard (expenditure) measure for gross domestic product (GDP) resulting from use of a new deflator works through on the industry (input) side. Aizcorbe identified other characteristics of medical care that complicate the calculation of price indexes. For example, insurance plans vary in their payments for a given service, so patients in different plans effectively pay different unit prices. Ignoring bad debt and charity care, the uninsured probably pay the most for treatments and pharmaceuticals. When uninsured individuals turn 65, Medi- care Part D comes into play and the drugs that they buy become cheaper. With aging, if the price that patients were paying before was high and the price that they pay once they join Medicare Part D is comparatively low, then the revenues that pharmacies or manufacturers receive for these drugs will fall. In this stylized case, nominal totals fall but the price indexes do not pick that up because they are tracking prices for, say, someone with Blue Cross/Blue Shield coverage and for someone enrolled in Medicare Part D separately. If this price index is used to deflate spending, a drop in quantity would be shown, even if the same population group—a portion of which has shifted from commercial insurance (or no insur- ance) to Medicare over time—is represented. Aizcorbe suggested that handling this type of heterogeneity for deflation purposes needs to be analogous to the method for handling input substitution for the treatment of diseases. BEA would try to define the price as expenditures on all types of treatments by patients with all types of coverage and divide that by the number of patients. So in the population aging example, as people start spend- ing less on drugs, it would be reflected as a price drop, not as a drop in quantity, which is exactly what is wanted for the national accounts. In summary, the main reasons why BEA feels it needs to construct deflators differently from what is currently being provided by BLS in its Producer Price Index (PPI) program is that they want to be able to think in terms of treatment of a particular disease, not of a specific kind of treatment for the disease. Also, BEA would like the account to be capable of reflecting as a price change—and not as a quantity change—the different prices that patients under different plans pay for treatments as they shift from one plan to another. Finally, BEA would like to control for changing trends in the severity of conditions as well, to the extent that it is possible. The ideal would be to track over time a disease in which the severity of the condition is homogeneous.

PRICE INDEXES 35 3.2. BEA’s Strategy for Coordinating the Industry Input Accounts with the Disease Treatment–Based Expenditure Concept Operating in parallel with the expenditure side of the NIPAs, on which the majority of the workshop discussion focused, is the issue about what to do on the industry side of the accounts. Once BEA begins deflating medical care spend- ing by consumers using a new price index, the industry-side calculations must be revisited, as real spending on inputs in the production of medical care must equal real spending on final medical care goods and services. If the deflators on the spending side are wrong, it must be the case that the industry deflators are also wrong. BEA’s proposed approach to this issue is to reorganize its accounting struc- ture for the medical care industry. The leading idea at the agency is to base this reorganization on a stylized model of health care in which patients are assumed to work through a care gatekeeper. Patients first go to their internist, pediatri- cian, or other primary care physician, who diagnoses problems and then sends them to different providers—the services are outsourced to labs, to professionals performing MRIs, to surgeons, and so on. Thought of in this way, the final good is provided by the primary care physician who orchestrates the medical care; everyone else in the system is simply an intermediate good. Aizcorbe explained that, for national accounting purposes, this means that spending is deflated by the disease episode–based indexes that are allowed to cross NAICS industry lines, and the intermediate goods are deflated by PPIs. The gap between the real dollar amounts on the spending side and the amounts from the intermediate goods is attributed to the value added of primary caregivers. The critical distinction from the current accounting framework is that the specialists must be viewed as providing intermediate goods. BEA’s Plan to Revise the Medical Care Industry Accounts In his presentation, Brain Moyer provided details about BEA’s plan to modify the industry side of the national accounts so that they can remain synchronized with the disease-based organizational structure proposed for the satellite program. He began by explaining that, in addition to the accounts that register the contribu- tion of consumer spending to real GDP, there is the less familiar set of accounts that show detailed inputs and outputs used in the production process by industry, of which health care is one. Here, real measures of value added by industry are established. Because health care accounts for a large and growing portion of the nation’s economic activity—currently 16 percent of GDP—the importance of measuring its impact accurately and in a way that avoids major statistical discrep- ancies is self-evident. If a new measure of consumer spending is considered for the personal consumption expenditures (PCE), then that must be traceable back

36 Strategies for a BEA Satellite Health CARE Account into the industry accounts to see which detailed components are contributing to that change. Moyer began by explaining how BEA currently handles health care in the accounts (details of the BEA methodology can be found in Appendix C). BEA’s input-output accounts show detailed transactions by industry, traditionally defined; so these transactions involve employees (i.e., consumers) who purchase health care services from various health care providers—physicians, hospitals, clinics, and so on. The health insurance industry is also viewed as providing a service to consumers (who often access the plans through employers). The GDP-by-­industry accounts show real value added for the health care industries. In contrast to the proposed satellite structure, these industries produce and sell final outputs to consumers. As noted above, the proposed change to BEA’s framework will involve introducing a new primary caregiving industry. The primary caregiver industry purchases its inputs from the other industries providing health care—hospitals, clinics, laboratories, pharmacies, and so forth—which are viewed as intermedi- ate purchases in the production of medical care. Other inputs, such as specialist physicians, could also be added. The idea is not new; its real-world counterpart is a health maintenance organization (HMO). In his presentation, Moyer detailed how introduction of the primary care- giver category changes BEA’s industry account picture. Employers still make contributions to employee health care plans, and employees still purchase health insurance; however, the new primary caregiving industry sells its output directly to consumers. This framework allows BEA to reconsider the definition of a unit of medical care and to incorporate disease-based price indexes. Most importantly it will allow the industry accounts and the NIPAs to be in balance, both on the nominal side as well as on the inflation adjusted or the real side. Moyer presented a hypothetical example to illustrate how this additional industry reconciles the two sides of the accounts (Box 3.1). He suggested that the new accounting structure for the industry side is not only a mechanism to ensure that the accounts remain in balance but also has a realistic representational element. The real value added from the other providers is unchanged, which is to be expected, whereas there is an increase in the real value added for primary caregivers. This, he said, can be interpreted as resulting from the coordinating efforts that this newly defined primary caregiving industry is providing. In sum- mary, as a result of moving to this new framework for the satellite account, the expectation is that there would be no impact on nominal consumer spending while the measure of real PCE and real GDP would increase. BEA has produced some initial estimates indicating that real consumer spending on medical care, measured in the new way, may be about 1.5 percentage points higher per year, and real GDP would increase by about 0.2 percentage points per year.

PRICE INDEXES 37 Discussion of the BEA Plan During open discussion, Barbara Fraumeni—who, as a recent chief econo- mist at BEA, has considerable experience with these issues—commended BEA for moving in parallel on both the expenditure and the production sides of the accounts, so that the inputs and outputs of the system would be identified. She summarized the key elements of BEA’s plan, which she characterized as preserv- ing the accounting structure while putting together an important disease data set that could provide the flexibility necessary to allow price indexes to potentially capture quality change effects. Fraumeni conceded that the current state of the art for measuring quality change at the disease treatment level does not do this quite yet, but she advised that this should be a high priority for the agency. ­Fraumeni suggested that BEA document its plans and progress through two papers, one that discusses the accounting system and proposed changes to it, and another that describes what can and cannot be done now and what the agency would like to tackle in the future—namely, the quality change issue. Aizcorbe reminded workshop participants that BEA is still very much in the early stages of conceptualizing this industry-side structure and asked the experts assembled in the room to continue the debate on the topic. Extensive discussion followed about how the physician gatekeeper model would be operationalized in the national accounts. Fraumeni described the new model, which reroutes the way that expenditures flow through the national accounts, as essentially involving a “fake billing.” The reason is that the billing does not come entirely from the primary caregiver, and it has no impact in nominal dollar value added or expenditures. Sherry Glied agreed, noting that, even though integrated systems with gate- keepers exist in the real world (e.g., HMOs), medical care often takes place outside these systems. In medical care there are a few general contractors but, in practice, many people serve as their own contractor. From a mechanical standpoint, there needs to be a placeholder there, but it is not clear how it should work. In thinking about how to organize the industry-side inputs, Glied pointed out that sometimes there is a physical representative and sometimes it is a virtual idea. The products of the organizing industry are final episodes of treatment for specific diseases, but, she concluded, it will be a tricky task to figure out what belongs in that category. Joseph Newhouse observed that insurance companies are already working to organize information at the disease level using the ETGs—even though their goals for doing so are somewhat different. They are beginning to actively analyze their businesses into what is called disease management, a new industry that is trying to manage chronic illness better. For the purpose of national accounting, Newhouse noted that the organizer or the gatekeeper is really a residual category to make the accounting entries balance. A mechanism is needed to capture the substitution that takes place, but then double counting has to be avoided, which is difficult when expenditures are fragmented then put back together again. Ralph Bradley added that, if the physician were truly an organizer, a grouper for the

38 Strategies for a BEA Satellite Health CARE Account BOX 3.1 Hypothetical Reconciliation On the spending side of the NIPAs, suppose nominal spending on medical care changes by $100. In the table below, the first column lists how the expendi- ture would be treated in the current framework; the next column represents the proposed framework. Currently, in the measures of personal consumption expen- ditures, the PPI—in this case, 1.07—is used to calculate real consumer spending. Since producer prices increased by 7 percent, the result is a $93 real change in consumer spending. Treatment of a Hypothetical Expenditure, Conventional and Satellite Structures Current Proposed Medical care: Change in nominal spending $100 $100 Price index 1.07 1.05 Change in real spending $93 = ($100/1.07) $95 = ($100/1.05) Industry accounts: Change in nominal value added $100 $100 Primary caregiver $20 $20 Other providers $80 $80 Change in real value added $93 $95 Primary caregiver $19 = $20/1.07 $21 = ($100/1.05) –($80/1.07) Other providers $74 = $80/1.07 $74 = $80/1.07 NOTE: Real value added computed through “double deflation.” In the proposed framework, BEA would use the disease-based price index, which, in this example, increases by a slower growth rate relative to the PPI, which is consistent with some of the initial work done by BEA. This leads to a higher calculated growth rate in real consumer spending. To show how this flows through to the industry accounts in this example, the intermediate inputs of other providers are assumed to be zero. In the industry ac- claims database system would not be needed, because physicians would be select- ing the pathways of all the treatments and then reporting them. He also pointed out that, when reading a claims database now, there are claims for prescriptions and other medical services that are not assigned a diagnosis. This shows that the grouper fails to assign a medical purchase to a disease for each case. The session concluded with discussion of Moyer’s point that, if the deflator for medical care on the spending side of the accounts is changed, then the rate of GDP growth changes and a gap would open between that rate of real GDP growth

PRICE INDEXES 39 counts, the goal is to derive a measure of real value added. To do that, the outputs and inputs of an industry must be deflated. In nominal terms, the value added is equal to the output of an industry minus its inputs. Output is deflated with an ap- propriate price index, as are the inputs, and real intermediate inputs are subtracted from real gross output, which provides the estimates of real value added. This is the typical way of computing real value added in a national accounting framework. Turning to the industry accounts side, under the current structure, the example shows a $100 change in nominal value added—nominal value added equals the change in health care GDP. For this example, $20 of that is attributed to the primary caregiver, $80 to other providers. The assumptions have not changed under the new structure; in nominal terms, everything adds up. Moving to the real side, under the existing structure, outputs and inputs are also deflated for the primary caregiver industry. Since a value of zero is assumed for intermediate inputs, the $20 for the industry is divided by the PPI, which gives a change in real value added of $19. Following the same procedure for the other providers produces a nominal value added of $80 and a real value added of $74. The sum totals $93 again, which matches the figure for real consumer spending on the other side of the account—the two sides balance. Moving to the satellite framework, characterized by the addition of the new primary caregiving industry, the other providers’ value added is still $80; divided by the PPI, a real value added of $74 is obtained. Changing the structure does not change the value added of these other providers. The output of the primary caregiver industry is now the value added, or $20, plus the intermediate pur- chases it is making from the other providers, $80. Dividing this total by the new disease-based price index, hypothetically set here at 1.05, then subtracting the real value of intermediate inputs (the nominal, $80, divided by the relevant PPI, 1.07) gives $21. This total, plus $74, equals $95—the two sides of the account are in balance. SOURCE: Workshop presentation by Brian Moyer. and the measure of change in the industry accounts (if left unchanged). This gap has to be entered as a line item somewhere; the issue is then how to interpret it. Landefeld reminded participants that this idea of making an adjustment in a top- level category on the industry side of the account, to make it balance with the expenditure side, is not unprecedented. For computers, this is how any gains in productivity are categorized. The residual is attributed to the top-level industry— computer manufacturers, not the component manufacturers. Triplett also framed the industry side accounting issue in the context of pro-

40 Strategies for a BEA Satellite Health CARE Account ductivity, which, like the national accounts, offers a logical construct for organiz- ing data. If indeed the primary care physician industry is to be where all excess multifactor productivity will be categorized, he urged BEA to be explicit about the method for treating the residual difference between the expenditure-side and the industry accounts under the old and the new frameworks. This level of detail underlies the accounts, but the numbers published by BEA are for the health care industry, which is the aggregation of the subindustries—the hospitals, the doctors’ offices, and the rest—for which data are supplied by the Census Bureau and BLS. Triplett also suggested that BEA will have the problem that price indexes for the subindustries will not equal those for the aggregate level—there will need to be a reallocation term. Based on the Moyer presentation, BEA now has a story to tell about the source of economic activity driving the inequality, which, in some other contexts, is called a statistical discrepancy. In discussions of the options about how the inequality could be handled in the satellite account (other than the gatekeeper productivity mechanism), David Cutler made the point that one possibility would be to simply create a line in the accounts called “total factor productivity” and not specify which industry gets it. In addition, it is sometimes important to know how productivity is affected in overlapping disease areas below the medical care industry level. As an example, if the medical profession gets better at treating heart attacks, it might suggest that diabetes treatment has improved, since one of the things these patients die of is heart disease. Cutler observed that in one sense this is right, but in another it is not. It is right in the sense of the broad medical care industry, because people with diabetes are now living longer and their quality of life is better. In other ways, it is wrong, because it is not the treatment specifically for diabetes that has led to the improvements. So, for various purposes, productivity gains need to estimated at different levels of aggregation. The key is to make sure that the pieces add up to the total; otherwise, the person who receives treatment for heart disease may be double counted in the estimated productivity improvement from that, as well as from treatments of dia- betes, high cholesterol, and hypertension. Only with an accounting structure can one ensure that entries do not appear in multiple places or, if they do, that they are parsed to add up correctly. This is done by looking not only at the expenditures on and productivity of the treatment of the disease, but also at the productivity of each particular input; so it is not just the productivity of treating the heart attack, but also of the hospital and of the physician and of the pharmaceutical company. In response to Cutler, Landefeld made the point that, to allocate the adjust- ment across industries or subindustries begins to call for a lot more work and, of course, all that has to be transparent. He noted the similarity here with the discus- sion that has gone on for years about the statistical discrepancy in the accounts.   Multifactor productivity is defined as output generated per unit of combined inputs; inputs may include capital, labor, energy, materials, purchased business services, and so forth.

PRICE INDEXES 41 Many users would prefer that the accounts categorize the residual in one place, because once it is allocated, people want to know explicitly where it went and how that flows through to multifactor productivity. He continued, saying there may be some value in just leaving the residual in one place until it can be figured out where it properly belongs. In other words, final expenditures is what is in PCE, and that needs to be made clear. With the Moyer presentation, BEA laid out what that implies for the industry accounts. 3.3. Tracking Quality Change of Medical Goods and Services In a world of ideal measurement, BEA’s satellite health care account would be deflated using quality-adjusted price indexes, as is already done for the most methodologically evolved components of the NIPAs. This is the logical final step in the program, but work is currently too embryonic for BEA to go that far now—things are very much in the research stage. Matthew Shapiro laid out the (long-term) agenda and identified hurdles that would be met along the way. He began by noting that, in constructing a con- stant quality price index intended for use in measuring real change in economic a ­ ctivity, the idea is to hold the mix and character of goods and services constant. For the health sector, price inflation is important, but there are also huge changes in how medical services are performed. The only constant is that the treatments are intended to improve the health of people with a given condition. Shapiro drew from the example of a treatment he has worked on, cataract sur- gery, to illustrate the sometimes subtle components of a price change. If a doctor or a facility charges more for a specific treatment, that should clearly be counted as a price change—that is the easiest case. But, he continued, the price change that accompanies a facility shift should also sometimes count. If a procedure previously performed in a hospital is now done in an ambulatory care facility or an office and the price is different, the price index should capture that. Here, Shapiro noted, is where some differences in method emerge across the agencies. BEA’s preferred approach is treatment oriented; whether a patient has a cataract procedure done at a hospital or at an ambulatory care facility, if the outcome is the same, any observed price change should be treated as such. The BLS indexing procedures have typically looked only within each type of ­facility, and the aggregation is done in a way that does not capture this kind of price change. Similar situations arise elsewhere. Consider cases in which a health plan reimburses only a fraction of the price change, or for which Medicare decides to change the amount that it pays doctors. Shapiro argued that, as long as the doctor is performing the same service, the change in what gets reimbursed should be reflected as a price reduction. In addition, situations arise in which the way a procedure is actually done changes. For example, staying with the cataract case, modern treatment has gone

42 Strategies for a BEA Satellite Health CARE Account from a sutured to a sutureless procedure—there has been a technological change in how the surgery is performed. The fact that patients do not have to pay for the suture under the new procedure should be considered a cost reduction, assum- ing that it is just as effective. In the language of price index construction, this is a debate over what gets linked and what does not. BEA is hoping to develop a method in which such factors as changes in the mix of inputs, in who pays, and in the location of the service are not relevant to pricing the unit of measurement for cases in which these things have not affected quality. Shapiro views this as correct if the goal is to measure real prices for purposes of deflation of the national accounts. Next, Shapiro asked how this type of price index construction might be conducted on a larger scale than has been done to date in studies by health economists, which have typically focused on treatment of one specific disease. He suggested that it will inevitably require compiling data from bills on costs of episodes in a detailed, broad-based, and systematic way. The Consumer Price Index program for measuring health costs already involves sampling bills and repricing them from period to period. For the cataract surgery case, BLS would draw a random bill and note the portion going to the doctor, to the facility charge, to materials, to nursing, to anesthesia, and so on. Price change is measured asking the facility to reprice that randomly selected bill in each period for a ­hypothetical patient (with the same payer) who had the same mix of treatments, applying recently observed component prices. Over time, new bills are periodically drawn that may reflect changed processes or input mixes, but this only has an effect on the index going forward. In the cataract example, the fact that the suture disap- peared may get missed in the price system. The BLS-developed method of pricing bills for specific, well-defined kinds of treatments marked a significant improvement over indexing methodologies that simply priced care components, such as an hour of a doctor’s time or a day in a hospital. Shapiro pointed out that, in fact, what BLS has done for the past decade or so sounds a lot like what BEA is proposing with its large medical care database. However, the BEA idea is more ambitious, in that it would attempt to account for the fact that the suture in the cataract has disappeared, or that the service has moved from a higher cost category of provider—a hospital—to a lower cost one, such as an ambulatory care facility. Shapiro embraced the BEA proposal, which he described as fundamentally following the treatment of diseases, not a set bundle of inputs. He added that moving to this type of framework—really developed by Newhouse, Cutler, and others on a case by case (or disease by disease) basis—en masse in a systematic way would represent a major move in the right direction. BLS participants also described their agency’s progress on this kind of price measurement. Bonnie Murphy noted that the PPI program can handle substitu- tion within providers. So, if a cataract surgery was changed from a sutured to a sutureless procedure, and it was performed by the same kind of provider—say, in

PRICE INDEXES 43 the hospital—that substitution could be captured. If the nonsuture cataract surgery was performed in a physician’s office, that would not, as BLS current index sam- pling procedures cannot accommodate substitution across providers. In the examples cited at the workshop, the advantage of moving to a struc- ture organized by major disease categories rather than by provider became clear. Murphy agreed, stating that, theoretically, BLS would want to be able to measure price change associated with these kinds of substitutions. She added that, in their experimental indexing work, described in detail in Section 3.4., they plan to allow substitution across provider types. Furthermore, if suitable outcomes information becomes available, BLS would look into methods to do a quality adjustment; without that, an effort would be made to do a direct price comparison, which would show the price change taking place between the two periods. Shapiro recommended that BLS document their work, both to show the extent to which the current CPI and PPI programs are equipped to handle input and provider type substitutions, as well as the plans for alternative indexes designed to move further toward a disease-based unit of measurement. He noted that the past CPI literature includes articles that have helped spur important changes in price indexing methodology and practice. He added that both the CPI and PPI should produce papers prior to the workshop, his impression had been (perhaps mistakenly) that not much was happening on this front. Shapiro concluded that, at some point, it will be time to move on to the qual- ity change issue; following the cataract example further, it is important to ask whether losing the suture was a good or a bad development. In reality, it probably improved treatment outcomes by reducing the likelihood of mistakes and compli- cations from having to put in a suture. Treating complications adds to cost, which should be picked up in the accounts. That these costs are eliminated should count as a productivity improvement, which would have to enter the accounts through an adjustment to the price index. However, there may be instances for which los- ing something from a bill might not be a positive in terms of patient outcomes— disappearing inputs do not necessarily always constitute a price reduction. This is precisely why medical experts are needed to evaluate the constant quality caveat, even if no explicit quality adjustment is taking place. BEA has, for the moment, put quality adjustment aside, which Shapiro said was reasonable. Presumably, down the road, indicators of mortality and morbidity will become increasingly available and, perhaps, progress can be made to fold this information into quality measures on a case by case basis. But those, Shapiro agreed, are two steps that could be worked out in sequence, as BEA has proposed. In her comments, Barbara Fraumeni suggested that BEA also ramp up docu- mentation of plans for its satellite health care account, specifically about possible q ­ uality adjustment to their price and quantity estimates, and a defense of their view that the task is separable from the disease-based expenditure allocation work. She worried that, if BEA defers this effort, they may find out after spending a huge amount of time working with these very large databases that, had certain fields

44 Strategies for a BEA Satellite Health CARE Account (e.g., perhaps those related to product quality review) been saved, it would have been much easier to come back and do some sort of quality adjustment. Fraumeni said that BEA must recognize that quality adjustment is important and that one way to do it is through outcomes assessment. She advised BEA to take the “house-to-house combat” approach with respect to quality adjustments; there may be some diseases for which it is fairly well known that there have been significant changes in the types of treatments and the efficacy of those treatments—mental health might be one of those. Work could gradually proceed on a case by case basis to begin making gradual adjustments in prices and quanti- ties of these treatments in a way comparable to what happened when BEA began dealing with quality adjustment for computers. On behalf of BEA, Aizcorbe welcomed these suggestions. She was particu- larly interested in getting a sense of the extent to which the staged strategy—first tackling the quantities and expenditures, then quality adjustment—would fare in the end. She assured Fraumeni and others that the agency was keenly aware of the quality adjustment issue but that, at the moment, they simply do not have a systematic way of dealing with it. Fraumeni responded that perhaps there were some cases on which BEA could get started sooner rather than later, but that they would need to be selective. In summarizing what he heard during the session, Landefeld conveyed the view that work in progress at BLS would ultimately help capture differences and changes in the quality of care relative to some best practice across regions or types of hospitals, but that they would tend to miss changes in best practices that take place over time. He said that how to begin measuring quality improvements, whether they occur in large discontinuous jumps or incrementally, was the key question for which his agency would need help from the assembled expertise at the workshop. Landefeld then asked about the effect of including quality adjust- ment, going from a conventional price index to the proposed version needed for the satellite account. His view was that BEA could in fact move significantly toward where it wants to be by getting the cost (nominal expenditure) component correct first, and then appealing to external guidance for suggestions on how to go forward on quality adjustment—not cross-sectionally, but with respect to those large discontinuous changes in technology. 3.4. The Role of the BLS Price Indexes Michael Horrigan, associate commissioner in the Office of Prices and ­Living Conditions at BLS, provided introductory comments for presenters from his a ­ gency’s CPI and PPI programs. The presentations focused, in part, on the kinds of price changes described by Shapiro and the extent to which they could be captured by BLS field procedures in a timely fashion. In introducing his col- leagues, ­Horrigan said the purpose of the presentations was to provide a sense of

PRICE INDEXES 45 the agency’s plans; he acknowledged that whether or not the PPIs or CPIs would meet BEA’s needs to proceed with its satellite account was an open question. One important consideration is the frequency of the reporting requirement to the public, which has an impact on the range of methodological options. For much of its work, BLS has to think in the context of a monthly production pro- cess. Horrigan pointed out that the Cutler-Rosen work measuring per-person costs of completed episodes can take advantage of less frequent reporting requirements. The varying timeliness constraints may lead to different decisions regarding, for example, whether an episode-based approach or an encounter-based approach is appropriate. Likewise, some of BEA’s objectives with its satellite account allow for less timely periodic analysis. The fact that some version of the national health accounts could be issued on an annual or quarterly basis creates extra possibilities that should be exploited. CPI Medical Care Price Indexes The workshop sessions covering initiatives to advance medical care price indexes informed questions about how and in what ways BEA will be able to draw from BLS sources to deflate nominal expenditures for estimating real GDP for the sector. Ralph Bradley from the CPI program opened the discussion presenting pre- liminary results produced by experimental work on a medical care index organized by disease. He stated that the motivation behind the initiative was to improve the medical care CPI by trying to capture protocol changes, such as those exempli- fied by the cataract or mental disorder cases discussed by workshop participants. Bradley also emphasized the importance of determining the ­feasibility of real-time production and publication of disease-based price indexes. Bradley began with a description of data requirements. He stated that BLS would not be in a position to initiate a new survey in the near future, so research investigating the feasibility of disease-based indexes must rely on existing data sources. MEPS, because it captures medical expenditures and also measures medical utilization, is the obvious candidate to use in the CPI. Its level of detail facilitates price index work that encompasses many of the needs; for example, substitutions toward less costly inputs for a given treatment should be reflected in the data. MEPS is analogous to the Consumer Expenditure Survey used to generate weights for many CPI components in aggregating to the all-items index. Annual data from MEPS would be used in much the same way to generate weights for the disease-based indexes. Bradley presented preliminary results emerging from initial analysis of the MEPS data. These results are summarized in the tables in Appendix A. For this research, Bradley and colleagues merged the MEPS conditions file, which lists each diagnosis an individual receives, or the diseases reported on the house- hold file, with the MEPS event file. The events file includes various treatments received, office visits, emergency room visits, outpatients encounters, pharma-

46 Strategies for a BEA Satellite Health CARE Account ceuticals, etc. The MEPS data are annual, so a monthly inflation number had to be created. For this, weighted CPI data for existing categories—office visits, hospitals, pharmaceuticals, etc.—were used to generate monthly price growth estimates. The weights in MEPS are updated annually for all inputs used in each disease treatment. If substitution away from a more expensive input to a less expensive input occurs, the updated weight will reflect that. Substitution among medical protocols is one aspect of the analysis. Another, reported Bradley, has to do with the fact that the population is getting older and, with that, many disease treatments are increasing in intensity. Table 3.1 shows trends in average utilization per person (not per disease) for a set of services, as reflected by data from the MEPS consolidated household file. Each year, the representative individual is getting a little older, by roughly 0.2 year. Although there is little trend in hospital utilization, there is an increase in utilization for the other areas, particularly for pharmaceutical. The rapid growth in emergency room utilization could be, in part, a function of the increased fraction of sampled indi- viduals who are uninsured and who therefore rely more heavily on that option. Bradley next reviewed methods for handling comorbidities. The research group looked first at the mean number of diseases treated per office visit, linking the conditions file with the events information files on office visits. They found that the mean number of diseases treated per visit increased from about 1.5 in 1996 to over 2 for 2004. One approach considered by the group for treating these additional comorbidities involved calculating a proportional distribution. The idea here is that if, in one year, a patient with diabetes has a physician visit, that entire encounter is allocated to diabetes. If, in the next year, the patient now has diabetes and a heart condition, that will be prorated—half of the visit will be allocated to diabetes and the other half to heart disease. If a prorating scheme is used across the treated diseases, increasing comorbidities will increase service productivity. Doing no prorating, however, may bias the index upward, because a service is double counted. So far, BLS has generated only indexes without pro rating, but they plan to produce additional indexes with prorating. Bradley also described item weighting procedures. For purposes of the CPI TABLE 3.1  Average Service Utilizations, 1998-2004 Hospital Inpatient Physician Outpatient Emergency Number of Filled Year Nights Discharges Visits Visits Room Visits Prescriptions 1998 0.582 0.098 4.569 0.461 0.160 7.202 1999 0.513 0.100 4.412 0.428 0.157 7.480 2000 0.592 0.102 4.390 0.470 0.169 7.783 2001 0.572 0.106 4.765 0.526 0.194 8.773 2002 0.562 0.102 5.110 0.558 0.196 9.345 2003 0.557 0.101 5.144 0.544 0.192 9.640 2004 0.567 0.103 5.219 0.513 0.193 10.003 SOURCE: Workshop presentation by Ralph Bradley.

PRICE INDEXES 47 program, the methodology is driven by the intent to price the goods and services financed by the out-of-pocket payments of consumers. Relative to, say, total sys- tem expenditures, this produces a different set of weights on hospital services, physician services, and outpatient services. If a shift occurs from hospital to outpatient services, there may be very little savings in terms of consumer out- of-pocket expenditures; most of it may accrue to third-party payers. This will of course affect the relative performance of the different kinds of indexes. For the experimental medical CPI, BLS organizes disease treatments into the following categories:   1 - Infectious and parasitic diseases   2 - Neoplasms   3 - Endocrine, nutritional, and metabolic diseases and immunity disorders   4 - Diseases of the blood and blood-forming organs   5 - Mental disorders   6 - Diseases of the nervous system and sense organs   7 - Diseases of the circulatory system   8 - Diseases of the respiratory system   9 - Diseases of the digestive system 10 - Diseases of the genitourinary system 11 - Complications of pregnancy, childbirth 12 - Diseases of the skin and subcutaneous tissue 13 - Diseases of the musculoskeletal system and connective tissue 14 - Congenital anomalies 15 - Certain conditions originating in the perinatal period 16 - Injury and poisoning 17 - Other conditions 18 - Preventive services without diagnosis Preliminary results for all of the total expenditure and out-of-pocket expenditure disease-based indexes are presented in Appendix Table A.5. Bradley illustrated how the index works using mental disorders as an example. For this kind of service, the mean number of office and outpatient department visits has dropped dramatically over the study period, but the mean number of prescriptions has increased. Using the current methodology, in which each individual service is tracked separately, these shifts go unaccounted for and a growth rate of 37 percent emerges for the price index for treatment of mental disorders over the 1999- 2004 period. When the annual quantity updates are performed, the price index growth declines to 7 percent. Bradley noted that mental disorders is an extreme example—a case in which the substitution effect is pronounced. It does provide an example, though, of the kind of impact that can occur when changes in proto- col are folded into the broader based indexing method. Overall for medical care, when a total expenditure concept is used, a price

48 Strategies for a BEA Satellite Health CARE Account index that uses annually updated protocol changes grows less rapidly—by about 3 percent—than does an index that uses only fixed quantities. But, for the out-of-pocket scope index, the reverse happens; the fixed quantities index grows less rapidly than does the quantity updated index. There are several reasons for this. Sources of financing play a very important role. As Appendix Table A.4 indicates, the out-of-pocket share of inpatient hospital expenses is comparatively small. Bradley reported that it is common to see situations in which going from inpatient to outpatient status produces a 50 percent drop in the total cost of a medical procedure. But, simultaneously, patient copays may rise, and there could actually be an increase in terms of out-of-pocket pay- ments going from inpatient to outpatient. Also, hospital services is the stratum for which prices are rising most rapidly. So, if a total expenditure approach is used, more weight is being put on the hospital index relative to an out-of- pocket index. Next, Bradley described the BLS plans for continued research, specifically to develop treatment-based indexes that use a CPI scope. The CPI includes out-of-pocket payments made by individuals or families (including premiums for employer-provided health insurance paid by employees), plus the insurance payment financed from the employee’s Medicare Part B payment. Jack Triplett commented that, although it is a little hard to conceptualize exactly what a price index for out-of-pocket expenses will look like, it is the relevant concept for the CPI. He made the point that it is odd that, in the past, the CPI program has spent time trying to figure out how to price the cost of insurance when the CPI weights reflect only the out-of-pocket expense portion. The out-of-pocket scope index is a little more difficult estimate, relative to the total expenditure scope index, because a crosswalk has to be created between the Consumer Expenditure Survey data and MEPS data, which include the third-party payments. Finally, BLS will looking for ways to better differentiate medical services in its indexes. For example, currently, the unit of measurement for office visits is the visit itself; however, it should be possible to take advantage of data fields in MEPS indicating in more detail the types of services that are provided, such as an MRI, an X-ray or other things, and this may lead to better measurement of quantity and possibly quality. PPI Research Plans for Medical Care Price Indexes Bonnie Murphy presented the BLS plans to develop a “multi-industry price index structured by disease.” At the moment, these plans are very much still in the research phase. She emphasized that the PPI program will not change the way it measures prices—it will not be pricing an entire disease treatment episode. Since the alternative structure uses PPI data that are already collected (there will be no new surveys), it is a low-cost experiment. This first new product is scheduled to appear in 2010 at the earliest, mainly because of restrictions on the

PRICE INDEXES 49 data that BLS can get to implement this project. The PPI program also has plans for a second project to push forward on ways to quality adjust for hospitals. This research ­project is in the public comment phase right now; it is possible that, if the feedback on the concept is positive, a new method of quality adjustment for the hospital index could be implemented very soon. The medical price indexes in the PPI have to be flexible to meet a range of user needs; for example, the industry or provider-based indexes have been needed by BEA for industry-based deflators. Indexes organized according to payer meet the needs of health insurance companies and other public and users (such as CMS). Also, BLS receives many requests from health insurance com- panies asking about price trends for Medicare and Medicaid; timely (monthly) and comprehensive price indexes generally meet these needs. In part because of these clients, the current industry indexes will not change. Workshop participants referenced data sets that they have used in accounting exercises that are available only with a 2- to 4-year lag; with these, it would be very difficult to come up with a product that is very timely and comprehensive. In addition, Murphy stated that BLS would not publish anything that did not cover the entire population. Murphy began by acknowledging that a major motivation for working on the disease-based indexes is to help meet a research or national health account need. This alternative index she hoped would fill a gap by providing timely price data on a path or course of treatment for any given diagnosis across all providers (industries). Ideally, it would be capable of capturing substitutions of treatment protocols within and across treatment providers—for example, the cataract sur- gery that has changed from inpatient to outpatient (discussed in Section 3.1.), or the ulcer treatment that has changed from hospital treatment to drug treatment. Murphy then described the current structure of medical service PPI com- ponents. Currently, PPI publishes indexes for the set of medical care industries shown in Table 3.2. Murphy reported that the PPI for hospitals has a relatively robust sample, which can support publication of data for Medicare and Medicaid patients, as well as 23 additional indexes by major diagnostic category for non-Medicare and non-Medicaid patients, so BLS already publishes this part of its health care indexes by disease. The pricing unit itself consists of expenditures on the specific procedure. For example, for the patient who had an appendectomy surgery in the hospital, the price would be the reimbursement that the hospital receives from any payer, whether Medicare or Medicaid, private insurance, the patient, or any combination of these payers. The PPI captures the total reimbursement (from admission to discharge) for this appendectomy. Specifically, the PPI captures the reimbursement for all of the services that are included on the bill. If the physi- cian who performed the surgery bills the patient separately, then those revenues would not be a part of the hospital bill and would be given a chance of selection in the physician industry. It is important to clarify that, if the patient goes to a physician’s office for

50 Strategies for a BEA Satellite Health CARE Account TABLE 3.2  Publication Dates for Medical Service PPI Components Industry Publication Start Date Pharmaceutical preparation manufacturing July 1981 General hospitals Jan 1993 Psychiatric and substance abuse hospitals Jan 1993 Other specialty hospitals Jan 1993 Offices of physicians Jan 1994 Diagnostic imaging centers July 1994 Medical laboratories July 1994 Nursing care facilities Jan 1995 Home health care Jan 1997 Retail pharmacies and drug stores July 2000 Health and medical insurance carriers Jan 2003 Residential mental retardation facilities Jan 2004 Blood and organ banks Jan 2007 SOURCE: Workshop presentation by Bonnie Murphy. diagnosis of the appendicitis, that office visit would be included in the PPI physi- cian index, which is separate from the hospital index that captures the price of the surgery (recall the example above for how the PPI handles reimbursements). The PPI segregates by provider, however, physicians who operate out of hospi- tals, HMO medical centers, or similar facilities, and bill separately are included in the physician PPI. The immediate plans for the PPI, for July 2008, call for the general hospitals index to be published according to major diagnosis category (MDC) without payer detail. PPI will also publish an alternative index by payer type only—that is, by Medicare, Medicaid, and “all other payers,” again without MDC detail. Future plans call for aggregating the indexes by the Census Bureau ­industry weights, which is what is typically done in the PPI. A much more dramatic improvement, which may be made possible by the 2007 Census Bureau implemen- tation of the North American Product Classification System structure, may allow the PPI (by around 2010) to publish alternative indexes that cross seven health care industries: pharmaceutical manufacturing; all hospitals (general, psychiatric, and specialty); physicians; medical laboratories; and diagnostic imaging centers. This plan would allow the indexes to capture some cross-industry medical service input substitution. Murphy provided an example to illustrate how the new price index would work. She returned to the example of an eye surgery that moves from an in-hospital setting to a physician’s office, and that is now performed at a lower cost. The new (in office) treatment for this eye disease enters the market in year two, and it does not initially represent a large portion of the market. The current PPI would not show price change in the physicians index when it enters the market; the same is true for the hospital index. The old treatment is still occur-

PRICE INDEXES 51 ring in hospitals, so PPI would still price it, and it will continue to be included in the hospital indexes for a number of years. Under the alternative indexing methodology, both treatments (the traditional hospital-based one and the new office-based one) would be concurrently priced until some threshold is reached, defined by a specific level of market penetra- tion for the new treatment. In the alternative index, the hospital treatment would be eliminated altogether from the sampling at some point; BLS is currently in the research phase of determining exactly when this would be—and what the threshold would be. The index would show the price change between the hospital treatment and the physician treatment at the threshold time period. Although Murphy expressed optimism in the proposed design for the experi- mental index, she was also careful to list limitations to this alternative structure: • The PPI covers only 13 health care industries; there is no coverage, for example, of ambulance services or of outpatient surgical centers, which tend to be important in substitutions between hospitals and physicians. • Disease-based structures based on the North American Product Classifica- tion System are unavailable for some providers (e.g., home health care). The PPI will be limited as to the number of industries it can include in the alternative structure, so something will have to be done about expen- ditures that don’t fall into these categories. • The PPI will publish only disease categories with sufficient item data cov- erage. The PPI cannot show price change for items in the alternative price index at least until additional economic census data become available. The experimental PPI would still assume that the outcomes were the same before and after the change in protocol—for example, from hospital outpatient to physician outpatient. Of course, this will not always be the case, and ideally one would want some quality or outcome assessment. Murphy stated that a high pri­ ority is to get a quality adjustment assessment in the index as soon as possible. At the moment, the PPI does not have a systematic method for quality adjust- ment, although Murphy was optimistic that, at some point, the CMS Hospital Compare data set could possibly be used to quality adjust the hospital index. There are a handful of conditions included in the Hospital Compare data set—for example, heart failure and shock, pneumonia—for which data are collected from hospitals on a quarterly basis. The program has established some quality indica- tors developed by clinicians that are likely to be better than anything that could be developed by BLS which does not house clinical expertise. The underlying methodology needs more research, but if it is determined to provide an acceptable basis, the capability to quality adjust will grow along with the Hospital Compare data set. The current PPI plan is to quality adjust all treatments, on an annual basis, for which data are collected at CMS. Murphy cautioned that this was a small step in quality adjustment—for example, it applies only to hospitals. But

52 Strategies for a BEA Satellite Health CARE Account because the CMS data are longitudinal and clinician approved, they are worth considering for the program. Open Discussion of BLS Plans Triplett commented that, in 1992, the PPI released its then-new hospital price indexes, which were a great advance over what had been done historically. As noted above, for many years, the unit of measurement for BLS price indexes were things like the cost of a day in a hospital. The PPI’s advance was to move toward the episodes-of-treatment concept, in which a diagnosis for in-hospital treatments would be priced out initially and then followed. It is a synthetic price that is esti- mated by asking the hospital what it charged for a diagnosis that has the same characteristics, the same demographics, and other conditions. The improvement resulted in a price index that grew less rapidly than the older index. Now appears to be time for the next major improvements to BLS price index programs. Triplett summarized three dimensions to the upgrade, noting the encouraging development that BLS is proposing work along all these lines. First is the need to adjust for improved (or deteriorated) medical treatments. Everybody, including BLS, agreed that it would be much better if quality adjust- ments were made to reflect these improved treatments. The second upgrade to the system is to extend the general approach of pricing episodes of diseases to nonhospital indexes. The third is to follow and perhaps adjust indexes when a treatment moves across facilities—or industries, as the structure is set up now. Currently, the hospital is an industry, the doctor’s office is an industry, and clin- ics are an industry. Next, Triplett spoke in more detail about BLS quality adjustment plans. The agency’s proposal follows the usual PPI method for making quality adjustments. The PPI is a constant input, fixed technology price index, just as the cost-of- living index is a constant utility, fixed preference function index. The CPI uses consumer preferences as a way to value a change in medical treatments, but the PPI theory on this is based on production costs. So the theoretical ideal is to use the difference in production costs between the old treatment and the new treat- ment to make a quality adjustment in the index. Triplett expressed skepticism about these PPI plans working because this theory of the output index embodies a problematic conceptual approach. Because the PPI is in theory a fixed input, fixed technology index, it is con- sistent if the quality change does not involve a change in the underlying produc- tion technology; Triplett pointed out that a lot of quality changes fit this model. For example, computers have over some periods used the same technologies, but there have been improvements that make machines faster. In the medical care context, many of the trends of interest involve new technologies. The cataract surgery that moved to a sutureless procedure is a good example. It is an innova-

PRICE INDEXES 53 tion, not a constant technology process. This innovation not only improved the treatment, but actually reduced its cost. One could take the old technology and ask what would it cost to produce the characteristics of the new treatment in the old technology. The problem, Triplett continued, is that the outcome could not be produced using the old technology. On this topic, Triplett concluded, the procedure described by Murphy might work well for some of the limited purposes for which it has been proposed, but it will not get at the major changes that concern most people working on productivity change in medical care. Those are the big changes in medical technology for which there is not a consistent cost estimate for the new and the old technologies. Again, Triplett praised the presenters for trying to do something about this, but cautioned that there were these limitations in terms of the ability of the methods to pick up the new technology-driven quality changes. Next, Triplett commented on differences between the grouper-based and PPI approaches for handling major changes in medical treatment that involve new technologies. The BLS method of handling the case in which outcomes before and after the change were not equal would involve a linking procedure. The alternatives discussed during the workshop are unit value indexes, in which a direct comparison is implicitly made between two different treatments, and indexes using linking methods, wherein prices of the two treatments are linked and not compared directly. In the case of the direct comparisons, changes to the good or service are ignored. A generic drug is treated as equal to its branded equivalent, and its price is compared directly (the current CPI procedure). The measured price drop is too large for the typical case in which some people do not switch to the generic version. The error arising from the direct comparison depends on the magnitude and direction of the quality change since all of it has been incorrectly been called a price change. The linking method is more complicated because the price change is implic- itly assigned from the things that changed (it is not true that the linking method implies the exact opposite of the direct comparison method—that is, that all quality change is ignored). The error occurs, roughly speaking, when prices are rising or falling, and some of the price change is attributed to the product quality change. So, Triplett raised the question, how are we to know what is the right way to do it? Comparing the generic and the branded drug directly might be better than ignoring the price change that occurred when the generic was introduced, even if direct comparison contained an error; but, we should strive for better methods. He suggested that the right way is to avoid constraining oneself to either making a direct comparison (with the implication that there is no quality differ- ence) or to using a linking technique (implying little price change). Triplett argued that an explicit adjustment is the preferable method for adjust- ing price indexes to account for changing treatment quality. The explicit quality adjustment in the case of medical care requires information on medical outcomes,

54 Strategies for a BEA Satellite Health CARE Account which throws the problem back to the fact that little is known about them. A small number of studies are cited over and over because that is all that has been explored; and, even in those studies, none used explicit outcomes measures. One can only conclude that a lot more work will have to be done before this kind of approach can be implemented in a broad based way throughout a ­statistical pro- gram. Yet, Triplett concluded, it is the right course for future work. Triplett’s final point was about the issue of following a treatment across industries, providers, or facilities. This could be done under the BLS method of collecting data from providers. If outcome measures were available to allow quality adjustment, that could also be done across provider classes. Returning to the cataract example, if BLS knew that outcomes from surgery in the outpatient and inpatient treatments were the same, they could make this direct comparison. But there are so many cases for which this is not known. Without the research, one cannot be sure that reducing the number of days after a normal birth delivery yields an equivalent outcome or whether it is just an attempt to reduce costs. This implies a major research agenda; figuring out how to do the quality adjustment requires a lot of scientific and medical information. The Illustrative Case of Pharmaceuticals Patricia Danzon commented on the BLS presentations, drawing insights from one of her areas of expertise. In the case of pharmaceuticals—as with standard cross-national comparisons that have been done for other services, such as hospi- tal days and physician visits—the practice has been to simply divide expenditures by number of units to infer difference in prices. The expenditures are hugely different, the quantities are similar, and the inference has been that it is therefore the prices that are much higher in the United States. It is these misleading results, Danzon stated, that make pharmaceuticals a good example of why accounting for quality differences is important in medical care price indexes. The issue, then, is to determine how much of the price difference observed in these statistics is really the services and the quality. Because pharmaceuti- cals are precisely defined—they are measured at the level of the mechanism of action, the strength, the pack, the manufacturer, etc.—Danzon has been able to calculate accurate comparisons of utilization and price differences across countries. She and her colleagues have found that a significant portion of the expenditure ­ difference across countries is explained by variation in the drugs being used—the formulations that may have quality dimensions to them. Clearly, simply dividing expenditures by number of prescriptions can vastly overstate price differences. Danzon raised the issue of international comparison as it relates to the dis- cussions by Triplett and Shapiro. For pharmaceuticals, the tendency to use num- ber of prescriptions is essentially imputing all the expenditure change to a price change (the direct comparison), whereas in reality much of it is in fact attributable

PRICE INDEXES 55 to new drugs or new formulations and of course the generics. If a CPI tracks vol- ume purely based on the number of prescriptions, it will be upward biased when there is change in technology that is quality improving. She noted that, to her knowledge, BLS was handling generics appropriately now, in that they are being treated as equivalent to the branded products that preceded them. An important gray area is the changes in drug formulation that occur around patent expiration that involve strategies by the branded manufacturers to extend their patent life; for example, the firms may delay the release of new formulations and, instead, introduce single isomer versions of the original drug, or combina- tion products. In BLS’s current procedure for drugs, Danzon suspects, these new formulations or combinations are treated as new products, which may not always be appropriate and may lead to a bias in the price indices. A common manufacturer strategy is to raise the price on the old formulation that is going off patent relative to the new formulation, in order to encourage people to switch to the newer products. Thus, if a price index is being used that tracks the older standard dosage form but does not pick up the delayed-release version that is in fact becoming the norm in the market, it will overestimate the rate of price inflation, since it includes the formulation that is no longer being used very much in the market. When the market baskets are updated, this will be picked up but, in some cases, the delays are significant. Danzon reported that, in these cases, her own research indeed found a much more rapid price increase for the formulations when they were going off patent and being replaced by versions that still had some exclusivity. Danzon also raised the issue of pharmaceutical invoice rebates. When prices are sampled at the hospital level or at the pharmacy level—or, for the PPI, at the manufacturer level—electronic rebates that go directly to payers will be missed for the outpatient pharmaceuticals. These rebates reduce the price to consumers and, in turn, the revenue that the manufacturers get. This is important for the branded products (for the generics, manufacturer revenues will be correct). What may be misreported is the amount that consumers pay, because the discounts go to pharmacies. How much that rebate to the pharmacies then gets passed on to consumers in the form of price reductions of other goods and services is unknown. She commented that, it is mostly an article of faith among economists that much of the rebate that comes from the manufacturer to pharmacy benefit managers is passed back as part of the cost of the drug benefit to the employer and there- fore ultimately to the consumer. The evidence that she has seen (a study by the Congressional Budget Office) is that something like 80 percent of these drug- specific rebates were passed back to the employers. If they are not passed back, it would be picked up more in the cost of health insurance—it will not show up in the pharmaceutical component. Danzon noted that these rebates are not trivial amounts. For the generics, the average rebates are on the order of 30 percent; for the branded pharmaceuticals, her best estimate is around 12 percent.

56 Strategies for a BEA Satellite Health CARE Account Similarly, mail order is an increasingly important distribution channel. It now accounts for about 18 percent of total pharmaceutical sales. The prescription is usually for a 3-month supply, whereas from a pharmacy it is a 1-month supply. So, again, if one simply counts the number of prescriptions to estimate the price per prescription, either the mail order users will be missed, or the wrong number will be calculated, because the content of every prescription is dramatically dif- ferent, by a factor of three in this case. Following up on Danzon’s example, Cutler added that, for these cases in which different strengths in dosages and formulations exist and substitutions across them occur, the more aggregated index should perform better than the more detailed version, provided quality adjustment is done properly. The aggre- gated index will take the total growth in pharmaceutical spending at the condition level and then ask how much of that growth is the result of new drugs and what is their net value. In this respect, moving to the larger pricing buckets is actually the right way to go and immensely important. The question then becomes: Is there any way to link across those buckets? Cutler added that, no matter which way the index is constructed, the quality adjustment is needed. It may be simpler for a more aggregated index, because each exact formulation does not have to be dealt with individually. Anything that is truly a new good will raise a different problem that will be missed either way; that is a big remaining issue. 3.5. Outcomes and Quality Change At several points during the workshop, participants made the point that, to monitor quality change in medical care for purposes of price measurement, accu- rate data on outcomes for treatments—defined in parallel with the expenditure categories—would be needed. Mark McClellan, of the Brookings Institution and formerly of CMS and the Food and Drug Administration, spoke about measuring treatment outcome in this context. Among participants, there seemed to be com- plete agreement that quality adjustment of price indexes for the satellite health care accounts is extremely important and also that it is very hard to do. McClellan began by noting that many of the measurement problems faced in the construction of price indexes for health accounts are increasingly impor- tant elsewhere for people—payers, purchasers, consumer groups, providers, and policy makers—concerned with where the health care system is going. The work that is going on in parallel creates an opportunity for collaboration on doing a better job of measuring outcomes and on putting more of a focus on value in the processes of health care decision making and policy. During open discussion, Linda Bilheimer (National Center for Health S ­ tatistics) asked what policy makers are looking for in terms of measures of out- comes. McClellan responded that it depends on whom and when you ask. If it is a briefing before the Joint Economic Committee about where health care should

PRICE INDEXES 57 be headed in the next 5 or 10 years, then topics related to value and to account- ing for productivity changes arise. If it is a Congressional Budget Committee meeting on how to get Medicare physician payment problems addressed for next year, then the topics are about the price and the policy changes that could affect nominal budget spending. As work moves forward in this area, one of the side benefits will be a better awareness among the general public and policy makers about the distinction in these kinds of questions. When people are asked today about what is wrong with health care, their response is increasing costs, and they equate that with prices going up—the premiums that they pay on their insurance plan and so forth. Even though they are individually quite satisfied with the care they are getting and perhaps reluctant to see major changes in health care policies that could directly affect their care, there is less recognition of the broader questions of how poli- cies are affecting value in health care and what people are getting for what they spend. Among the many challenges with measuring outcomes and accounting for them in indexes is sorting out the impact of health care on health, which can be hard to isolate, especially at the patient level; so many factors influence health. Also, there are few standard quality or outcome measures established for many aspects of health care. The trend has been to start with narrow pieces of the picture—such as a look at a specific disease—and try to expand that over time as data and technical expertise get better. McClellan reminded the audience that there is still a long way to go. Next, McClellan reiterated the point made throughout the workshop about the measurement problems created by the existence of multiple chronic condi- tions. He noted that it is getting more and more difficult to isolate diseases that coexist in individuals. The vast majority of Medicare spending now is on people who have multiple chronic conditions. For these patients, health professionals are increasingly realizing that focusing on one particular disease and its treat- ment leads to real missed opportunities to improve the coordination and results of care. Accordingly, efforts are being made to cut across disease areas with prescriptions for behavioral changes and medications, compliance systems, and the like that are not easy to attribute in patients with multiple conditions or any particular disease. Even when this kind of effort is made, however, McClellan agreed that it was hard to sort out how much of a given health status effect is due to the medical treatment and how much to other factors. Health is clearly improving over time, although at different rates for different kinds of disease treatments, and these trends reflect changes not only in medical technology, but also in bio­medical knowledge that affects behavior, as well as nonmedical factors that are not mea- sured as medical care in the economy. Wellness expenditures are a growing indus- try, and food improvement, education, socioeconomic status, and environmental exposures certainly affect health and are important determinants as well.

58 Strategies for a BEA Satellite Health CARE Account For all of these reasons, McClellan pointed out, there has not been a lot of practical application of outcome measures to ongoing health care policy aimed at improving the value of what society is getting for its spending. Instead, much of the focus has tended to be on process-of-care measures, for which it is easier to conclude with a reasonable level of confidence, from clinical studies and expert opinion, that using certain kinds of treatment for particular conditions or combi- nations of conditions leads to better results. As an aside, he noted that the results have not been particularly encouraging about whether the health care system is reliably delivering quality care. Nor have the these methods been very useful as surrogates for outcome measures; they have tended to focus on specific aspects of care and do not capture most of the things that consumers or even providers need to know in their decisions about health care. On the positive side, McClellan reported that many efforts are under way to change the way information about the health care system is processed. This is where McClellan sees some parallels and some opportunities for collaboration between the kinds of people who attended the workshop and those who are work- ing in such areas as quality improvement, payment reform, benefit redesign, and the like. Among the interested parties are provider groups that have been strug- gling with the traditional ways of paying for programs like Medicare based on volume and intensity, in which the final common pathway to address rising spend- ing is to squeeze down prices; this, McClellan stated, is not working very well in terms of promoting quality and value or even long-term cost savings, whether it is health plans or the employers who use them, who want more accountability for what they are getting for their spending. There are also interested consumer groups, like Consumers Union, that are now engaged in initiatives to make health care much more like choosing appliances and cars; they want to see information about providers and health plans, just like those in Consumer Reports for these other areas. CMS’s Hospital Compare database is another example of how this work on quality measurement—and not only processes of care, but also outcomes and satisfaction measures—is progressing. The Hospital Compare site was imple- mented several years ago; since then it has expanded and now includes several outcome measures. There are now CMS reports on Hospital Compare for 30-day mortality from acute myocardial infarction and 30-day mortality from heart fail- ure. McClellan expressed the hope that more will be coming soon in the area of surgical outcome measures and a range of other survival measures. McClellan reported that a final area for which CMS is beginning to expand measures available on Hospital Compare—very much related to outcomes— involves standardized patient satisfaction measures. He said that surveyed patients generally respond favorably when asked whether they received satisfactory care. However, he continued, more detailed data providing a deeper understanding of relationships with providers and doctors and nurses, of how information about the condition was communicated, and of how patients felt about particular aspects of

PRICE INDEXES 59 the treatments they received can all be very helpful. This turns out to be especially true for patients with chronic diseases, who often have a good idea of what is working to keep their conditions under control. Gail Wilensky commented that it is very helpful that people are being pushed to understand better, with tools like the Hospital Compare data or other data that are becoming available, that there are different ways to measure outcomes. She also noted that questions have to be framed very carefully. For example, when individuals are asked what is important to them about health care costs or health care prices, they typically think only in terms of what they are paying out-of-pocket—that is their working definition. McClellan also mentioned that other parallel efforts are under way for physi- cian care, nursing home care, and pharmacy care, but they are not as far along as the Hospital Compare data. They all follow a similar general model, starting with some process-of-care measures, then push toward looking at outcomes, patient satisfaction, and other aspects related to outcomes. With this increasing emphasis focusing on value—and not just volume and intensity and prices in health care reform—there have been a number of efforts bringing together provider groups, payers, purchasers, and consumer groups of health care. One purpose has been to get consensus behind methods to measure the quality of outcomes and costs, and to do it more at the episode or patient level, rather than just in a particular silo (such as hospitals, physicians’ offices, etc.) of care. McClellan described the typical process as involving a number of organizations that become involved in developing the technical details of what a quality measure, whether it is process or outcome or satisfaction, might look like. Next, a process is coordinated by the National Quality Forum, a congressionally recognized nonprofit organization, to try to get a consensus endorsement behind particular measures. These processes by themselves do not do anything to get the quality mea- sures into use in practice. According to McClellan, there have been a number of collaborative activities developed over the last few years to do that. Most of them are in the form of quality alliances or hospital quality alliances; these are instrumental in creating a consensus behind the measures of care that are used in such systems as Hospital Compare. One of these is a group called AQA, formerly Ambulatory Care Quality Alliance, which is concerned mainly about physician and ambulatory care quality measurement and is behind some of the efforts by Medicare and private payers to put more emphasis on quality and payment report- ing. On the ambulatory side, an organization called Pharm Quality Alliance is working toward some similar goals. A group called the Quality Alliance Steering Committee has been charged with trying to help these groups work together, to collaborate in this effort to get more consistent and common metrics out of the nation’s pluralistic health care system. The focus of the steering committee is to ensure that measures being developed in each of these silos, as McClellan described the various areas, are not only harmonized, but also on a track focusing on overall pictures of quality and cost at the patient level, or at least at the episode level, and on ways to get

60 Strategies for a BEA Satellite Health CARE Account synergistic benefits from using data from multiple sources together. One of the challenges in developing and using these measures is that any health plan, even Medicare, does not get a complete picture of care quality at the level of providers and their treatment of diseases or other conditions, or even at the patient level. Much of the focus on these collaborative efforts has been on trying to harmonize the different measures that various health plans, Medicare, or employers are using that would facilitate an aggregated approach that provides a more complete pic- ture of quality and cost of care. 3.6. Data Needs for Price Measurement, Tracking Outcomes, and Quality Adjustment Mark McClellan’s presentation also touched on some overarching data issues for measuring quality change; many of these parallel points were made during the discussions on medical care expenditures. McClellan stated that the aggregated data—compiled at the level of the provider, the health plan, or the treatment of similar patients—are what matters, not the fact that a specific patient was treated by a specific doctor with certain results. If participation processes and data collec- tion for the health care system were carried out with some consistency, it would be possible to perform complex analyses (e.g., multivariate regressions) and to produce relatively sophisticated measures in a distributed data system. Further- more, he pointed out that if a truly electronic health care system were created, it would have much more analytic value—not necessarily in terms of data volume but through development of consistent rules and standards being applied that would enable researchers to use it much more effectively. Danzon commented that there is a huge amount of data that are already collected by the pharmaceutical companies on comparative effectiveness of new technologies versus old technologies or new drugs versus old drugs that they have to collect in order to make their case for reimbursement in many foreign countries and increasingly with health plans in the United States. Many of the data are col- lected as part of clinical trials. With that comes limitations, but the data would provide some evidence about new technology versus old. In an efficient system, BLS and BEA would be able to take advantage of the millions of dollars spent collecting these outcomes data as part of this exercise. Newhouse pointed out that McClellan actually did a paper 10 years ago or so on heart attacks in which clinical trial data were used to break down the components of improvements and outcomes and attributes them to changes in specific aspects of treatment. McClellan pointed out that data could continue to be used in that way; however, in terms of actual health care delivery, there is a big gap between how well the technologies could be used and how they are used in practice. In most cases, even when there is a big medical breakthrough, it does not appear in the data from one year to the next. He cited the example of beta blockers used in heart attack patients—a treat-

PRICE INDEXES 61 ment that, beginning in the late 1970s, was used to substantially improve survival of patients after acute myocardial infarction. In the 1980s it started to be tracked in a few limited settings, and in the 1990s it became part of a routine quality measure that was part of Hospital Compare for a while. But it took three decades to get from the time when the studies were done to when there was complete use and practice of the procedure. So these kinds of data, in conjunction with trend information on the use of different technologies, can certainly be useful. But they are not going to provide a complete picture. The only other caution he added is that the quality adjustments could be significant; the measures on outcomes are going to get better over time, but they are almost certainly going to be different from year to year—it is very hard to maintain consistency over longer time periods. Triplett pointed out that there have still been only a small number of disease- based studies in the economics literature—the heart attack study (Cutler et al., 1998), the cataract study (Shapiro et al., 2001), the mental health study (Berndt et al., 2001), and a couple of others. However, outcome measures were not used in any of these. The heart attack study used mortality, but this was not a full outcome measure—it is a lower bound, because it does not pick up morbidity effects. For the cataract and mental health studies, there were no direct outcome measures either (for cataracts, the researchers contended that they had estimated a lower bound). Triplett concluded that there is not that much low-hanging fruit from the literature to pick up. Despite agreement in the past couple of decades that outcome measures are needed to conduct cost-effectiveness studies—which everyone agreed are important, as they are being used for health care planning as well as in research—the medical literature on the topic is still not extensive, and that limits what economists can do. McClellan responded that the technical ability to do something about this, to come up with some more reasonable and more complete outcome measures, though still very incomplete in terms of everything people might care about, has gotten a lot better. There is currently clearer policy agreement that just focusing on volume and price restrictions is not going to be enough. That said, McClellan agreed that there is still a long way to go. But, as more work gets done in this area, one of the side benefits is going to be a better awareness among the general public and policy makers about the distinction between cost and price and productivity questions. Some of the movement in the direction of quality measurement is also being driven by policy and legal pressures. McClellan cited a recent settlement in which the New York attorney general required transparency and the use of nationally recognized quality standards from major health insurers who have been trying to use measures of quality and costs of care as either conditions in their contracting with providers or as factors that influence the structure of their benefits (e.g., set- ting lower copays and perhaps paying the physicians and hospitals according to performance based on whatever measure each health plan or each employer came up with). The point here is that these metrics need to be based on both quality and cost, and they need to have a more comprehensive and consistent picture

62 Strategies for a BEA Satellite Health CARE Account than a health plan is likely to be able to get on its own with the limited number of patients that it covers. There has been an added push for these efforts to do network or aggregated approaches to quality and cost measurement. The practical relevance of this to the work that is being done in health accounts and price indexes is still a way off. However, McClellan pointed out that some of these broader measures are in the early stages of being constructed and made available. There is a broad national public-private roadmap planning effort to move from data that are based just on claims to data that include what might be called clinically enriched electronic information, like lab results and increasingly sophisticated information from electronic records or personal health records. There is a parallel between the kind of work that is going on here and the kind of work that is going on in the health accounts area. McClellan expressed his concern that the initiatives he is involved with, as well as the overlapping health accounting programs, move forward as effectively as possible from a policy reform standpoint; this will require efficient use of data. Ideally, a virtuous cycle could be created in which, with more and better informa- tion available on outcomes and costs of care and therefore on the value of care, there will be a movement toward payment and benefit system designs that reward and support better value and clearer evidence about what actually works. McClellan concluded his remarks by suggesting that some ongoing involve- ment of the BEA and the National Academies on work that is happening in these quality measurement, value measurement, and quality improvement efforts would make a lot of sense. The measurement goals across the various interests are similar; the only difference is that BEA has to focus on the national accounts as opposed to the actual impact on delivery of care.

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In March 2008, the Committee on National Statistics of the National Academies held a workshop to assist the Bureau of Economic Analysis (BEA) with next steps as it develops plans to produce a satellite health care account. This account, designed to improve its measurement of economic activity in the medical care sector, will benefit health care policy.

The purpose of the workshop, summarized in this volume, was to elicit expert guidance on strategies to implement the objectives of the BEA program. The ultimate objectives of the program are to:

  • compile medical care spending information by type of disease-a system more directly useful for measuring health care inputs, outputs, and productivity than current estimates of spending by type of provider;
  • produce a comprehensive set of accounts for health care-sector income, expenditure, and product;
  • develop medical care price and real output measures that will help analysts to break out changes in the delivery of health care from changes in the prices of that care;
  • and coordinate BEA and Centers for Medicare and Medicaid Services (CMS) health expenditure statistics.
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