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The Healthcare Imperative: Lowering Costs and Improving Outcomes - Workshop Series Summary 5 Prices That Are Too High INTRODUCTION In a discussion about rapidly rising healthcare costs, inevitable attention turns to the pricing of medical services and products. While current prices may preserve incentives for innovation and reflect investments in research and development (Jayadev and Stiglitz, 2009), these prices may also reflect market asymmetries in information and monopoly power (Dafny, 2009; Pauly and Burns, 2008). The speakers in this session explore how current market practices result from perverse economic and practice incentives, and the opacity of cost, quality, and outcomes, yielding prices that may cost the nation billions of dollars in expenditures unnecessarily. Basic economics teaches that monopolies create high prices and inefficiencies because of the stymied competition. Cory S. Capps of Bates White reasserts this basic economic principle when he examines the impact of hospital consolidations on prices. According to his research, mergers have resulted in higher costs and prices and static or worse patient outcomes. He describes how, until the 1990s, mergers had been blocked because of antitrust legislations. However, a policy change in 1993 has since allowed for the concentration of healthcare providers and relative increase of market inefficiencies. Estimating that current healthcare expenditures are about 0.4 to 0.5 percent higher than they would be absent price increases from hospital consolidations, Capps postulates that “unconcentrating” the market would yield between $10 billion and $12 billion in savings annually. However, he also explains that this analysis considers only broad averages and general trends, and does not indicate that any specific hospital consolidation will (or will not) result in higher or lower prices.
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The Healthcare Imperative: Lowering Costs and Improving Outcomes - Workshop Series Summary Jack Hoadley of Georgetown University discusses how pricing and markets work in relation to pharmaceuticals, explaining that pricing varies substantially by payer and by whether drugs are under patent protection. He also explores how government-sponsored programs, such as the Veterans Administration and Medicaid, price drugs differently than privately insured health plans (including those that deliver the Medicare drug benefit) or than pharmaceutical companies for uninsured purchasers. He additionally reviews research demonstrating that brand-name drugs are twice as expensive in the United States as in other countries while generic drugs are less expensive domestically. Hoadley ultimately concludes that, while a price reduction of even 5 percent in brand-name drug prices could save $9 billion a year, the potential is unclear, partially because pharmaceutical spending is driven not only by prices, but also by physicians’ prescribing decisions and patients’ decisions whether to comply with their prescriptions. While Hoadley cautions that this estimate is only illustrative, as no obvious standard for an optimal drug price is available, he also explains that additional consideration of the impact price alterations could have on research and development and innovation is necessary. According to Thomas J. Hoerger of RTI (Research Triangle Institute) International and Mark E. Wynn of the Centers for Medicare & Medicaid Services, evidence from competitive bidding demonstration projects demonstrates that the market for durable medical equipment (DME) inflates prices by approximately 20 to 25 percent. Care as to the interpretation of the amount of savings achievable is suggested by Hoerger because, while his savings estimate is based on competitive bidding results from the 1999-2002 demonstration projects and the 2008 national program, Medicare fees for DME have since been reduced. Hoerger also discusses how generous insurance coverage and demand created by pressing medical needs can promote higher prices for DME in excess of those that would occur in a perfectively competitive market. Although Medicare has used administered fee schedules in an effort to control these excess prices, Hoerger argues that these schedules may not be responsive to the usual market forces of supply and demand, entry and exit, and technological change. Wynn suggests that well-defined products, such as durable medical equipment, are the best candidates for competitive bidding. Yet, despite the potential for competitive bidding to lower the prices for DME, he urges consideration of the political context, describing how Congress delayed a DME bidding program for 18 months given formidable political backlash. Lastly, Jeffrey C. Lerner of ECRI Institute concludes this session discussing price-setting practices and market practices for medical devices. He examines some of the most common purchasing processes in hospitals and discusses how efficiency can be improved. Building on the premise that the large and artificial asymmetry between information and market power existing between buyers and sellers creates inefficiencies, he suggests that
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The Healthcare Imperative: Lowering Costs and Improving Outcomes - Workshop Series Summary better negotiating processes in hospitals could have yielded close to $5 billion in savings in 2008. He acknowledges that beyond hospitals, data from outpatient medical centers and physician groups would be needed for a more complete analysis. PRICE IMPLICATIONS OF HOSPITAL CONSOLIDATION Cory S. Capps, Ph.D. Bates White, LLC Because Medicare and Medicaid payments are largely determined by administrative fiat, only payments by private parties, primarily insurers, are subject to potential price increases resulting from hospital ownership consolidation. Since 2002, payments to hospitals by private payers have made up 13 to 14 percent of national healthcare expenditures.1 This implies, for example, that if hospital prices increase by 10 percent then total national healthcare expenditures would increase by 1.3 to 1.4 percent. Hospital Consolidation and Spending Growth In the late 1980s and early 1990s, hospital inpatient spending grew rapidly at rates of roughly 4 percent, and total hospital spending grew 8 to 10 percent per year (California HealthCare Foundation, 2009; Claxton et al., 2007; Ginsburg et al., 2006; Strunk et al., 2002) (Figure 5-1). Then, beginning in the early 1990s, two major structural changes in the healthcare industry gathered steam. The first was the dramatic increase in the penetration of managed care (Figure 5-2). The second was a reduction in the length of the average hospital stay and a concomitant increase in outpatient care. In combination, these changes likely explain the marked reduction in the growth rate of spending on hospitals in the early and mid-1990s. Instead of growing at rates in excess of 8 percent, overall hospital expenditures increased 3 to 4 percent annually, while inpatient expenditures actu- 1 Spending on hospital care represents roughly 31 percent of total healthcare spending, and private-sector spending represents about 55 percent of total healthcare spending (2007 National Health Expenditures Tables, at http://www.cms.hhs.gov/NationalHealthExpend Data/02_NationalHealthAccountsHistorical.asp). Multiplying the hospital share by the private-sector share suggests that private-sector payments to hospitals are closer to 17 percent of national healthcare expenditures. NHE Table 4 reports by expenditures source of funds and type of expenditure from 2002-2007 and shows that private-sector payments to hospitals account for 13 to 14 percent of total healthcare expenditures. The discrepancy between the higher figure and the 13 to 14 percent figure is likely the result of lower acuity hospital visits among the privately insured population (i.e., while private-sector spending is 55 percent of total healthcare spending, the private sector accounts for a share of payments to hospitals that is below 55 percent).
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The Healthcare Imperative: Lowering Costs and Improving Outcomes - Workshop Series Summary FIGURE 5-1 Components of national healthcare spending growth. NOTE: CPI = consumer price index. SOURCE: Reprinted with permission from the California HealthCare Foundation, 2010. FIGURE 5-2 Managed care penetration and inpatient spending growth. NOTE: HMO = health maintenance organization; PPO = preferred provider organization; POS = point of sale.
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The Healthcare Imperative: Lowering Costs and Improving Outcomes - Workshop Series Summary ally fell for several years. Then, around 1993, a wave of hospital mergers began (Figure 5-3). The peak occurred in 1996, when there were 108 consolidations among hospitals within metropolitan statistical areas (MSAs). Merger and acquisition activity remained high for several years thereafter (Town and Vistnes, 2001). Thereafter, in the late 1990s and 2000s, hospital spending returned to growth rates in excess of 6 percent overall and 6 to 8 percent for inpatient services only. In both periods of rapid spending growth—the late 1980s and late 1990s to 2007—the rate of increase of hospital spending outpaced the Consumer Price Index (CPI) by approximately 4 percent per year (Figure 5-2). The peak of the 1990s hospital merger wave was followed by an increase in inpatient spending growth (Figure 5-3). Economic literature exploring the relationship between hospital mergers and hospital pricing suggests that a significant portion of the resurgence in hospital spending growth rates was caused by price increases resulting from hospital mergers. Economic Research on Hospital Consolidation This section builds on a comprehensive 2006 survey by health economists Robert Town and William Vogt that was commissioned by the Robert Wood Johnson Foundation (RWJF) (Vogt and Town, 2006). Town and Vogt reviewed 87 papers that analyzed the relationship between hospital FIGURE 5-3 Inpatient spending growth and hospital merger and acquisition activity.
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The Healthcare Imperative: Lowering Costs and Improving Outcomes - Workshop Series Summary consolidation and concentration on the one hand and costs, quality, and pricing on the other. Cost Effects Most studies of the cost effects of hospital consolidation find small effects for most mergers and acquisitions. For example, Dranove and Lindrooth (2003) conclude that there are, at most, modest cost savings from system acquisitions in which hospitals simply combine ownership but do not combine licenses (Dranove and Lindrooth, 2003). They do, however, find that full mergers that involve combined licenses and service integration and consolidation can produce cost savings on the order of 14 percent. However, such full mergers are not the norm and can be difficult to successfully execute.2 Overall, Town and Vogt’s conclusion from their survey of the cost literature is as follows: “[t]he balance of the evidence indicates that hospital consolidation produces some cost savings and that these cost savings can be significant when hospitals consolidate their services more fully.” Quality Effects Hospital consolidation may also affect quality. The majority of studies to date, however, conclude that hospital mergers and acquisitions have either no effect or a modest negative effect on quality, with the former finding being the more common. Town and Vogt (2006) report that “[t]he findings from this literature [on quality effects] run the gamut of possible results. Of the 10 studies reviewed, five find that concentration reduces quality for at least some procedures, four papers find quality increases for at least some procedures, and three studies find no effect.” Price Effects Studies of pricing have yielded more definitive results. There is substantial evidence that hospitals compete within a fairly narrow geographic area, often smaller than a city or an MSA. Mergers within such a narrow area can lead to substantial price increases (Capps and Dranove, 2004; Capps, 2 The 1997 merger of the profitable UCSF and Stanford hospitals resulted in an entity that lost $176 million over 29 months. Don Kazak, “A merger gone bad,” Palo Alto Weekly, May 16, 2001. For a detailed account see: John Kastor, Mergers of Teaching Hospitals in Boston, New York, and Northern California, Ann Arbor: University of Michigan Press, 2003. Similarly, the 1999 merger between Alta Bates and Summit hospitals resulted in a combined firm that, by 2001, faced a $40 million annual deficit. Chris Thompson, “Local hospitals are bleeding money,” East Bay Express, August 1, 2001.
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The Healthcare Imperative: Lowering Costs and Improving Outcomes - Workshop Series Summary Dranove, and Satterthwaite, 2003; Dafny, 2009; Dranove and Ludwick, 1999; Gaynor and Vogt, 2003; Keeler, Melnick, and Zwanziger, 1999; Town and Vistnes, 2001; Vogt and Town, 2006; Vita and Sacher, 2001). Increases are most likely if the consolidation combines hospitals that, from the perspectives of insurers assembling provider networks, are close substitutes. A significant portion of the research focused on the connection between hospital concentration, typically measured by the Herfindahl-Hirschman Index (HHI), and hospital prices. The HHI is calculated by summing the squared market shares of the hospitals in a given market and multiplying the resulting figure by 10,000, with a value of 10,000 corresponding to perfect monopoly.3 Because the HHI is based on market shares, calculation of an HHI requires first defining the market within which to compute shares. Defining the area within which to analyze concentration and compute HHIs has played a crucial role in litigated hospital merger cases. The Federal Trade Commission (FTC) or Department of Justice (DOJ) typically alleges a relatively narrow geographic market, which tends to indicate that market shares and the HHI are high. The merging hospitals typically contend that the relevant geographic market is large and includes many hospitals, yielding low market shares and low HHIs. During the 1990s, as described below, courts overwhelmingly sided with the merging hospitals. Subsequent research has shown that hospitals generally compete locally and that hospital mergers—even those that have very small effects on MSA-level or multicounty HHIs—can lead to large price increases (Capps and Dranove, 2004; Dafny, 2009; FTC, 2005). This indicates that the MSA and other broad regions are unlikely to generally correspond to the relevant antitrust markets in which hospitals compete. However, formal antitrust market definition is a lengthy and fact-intensive process that proceeds on a market-by-market basis. For the purpose of reviewing nationwide consolidation trends and estimating approximate effects on pricing, this is both impractical and unnecessary. Prior studies defining markets based on counties, healthcare referral regions, health service areas, or MSAs have shown the HHI to be a useful predictor of prices. Based on their review of such studies, Town and Vogt (2006) concluded that an 800-point increase in HHI within an MSA led to an aver- 3 For example, in a market in which four firms have equal shares of 25 percent, the HHI will be 2,500 (HHI = 10,000*(0.252 + 0.252 +0.252 +0.252) = 2,500). The HHI ranges from 0 to 10,000, with 0 corresponding to perfect competition and 10,000 corresponding to monopoly.
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The Healthcare Imperative: Lowering Costs and Improving Outcomes - Workshop Series Summary age price increase of roughly 5 percent (Vogt and Town, 2006).4 To put it differently, each 160-point increase in the HHI leads, on average, to prices increases of about 1 percent. The analysis below follows this literature and analyzes hospital concentration at the MSA level. Antitrust Enforcement and Hospital Mergers Given this evidence of price effects resulting from hospital mergers, it is natural to inquire about antitrust policy and enforcement. During the 1980s and through 1993, the DOJ and FTC usually won when they went to court to block a hospital merger.5 That success, however, came to an end during the hospital consolidation wave of the 1990s (Table 5-1). From 1993 through 1998, the FTC and DOJ lost six consecutive hospital merger challenges; in 2001, the State of California lost a seventh. In the decade after the last of these losses, 1998 to 2008, neither the FTC nor DOJ challenged a prospective hospital merger in court.6 Over the 15 years spanning 1993-2008, antitrust policy likely had little restraining effect on hospital mergers over this period. Hospital Consolidation and Likely Price Effects From 1997 to 2006, the average number of hospitals per MSA declined only slightly (American Hospital Association, 1997, 2006). The landscape of hospital ownership, however, changed significantly over this period as a result of consolidation. Primarily as a result of mergers and acquisitions, the average number of independent hospitals per MSA declined by 0.3 percent, from 7.95 to 7.65, while the number of hospitals in multihospital systems in the average MSA increased 0.4 percent, from 3 percent to 3.4 percent (American Hospital Association, 1997, 2006) (Figure 5-4). In terms of capacity (hospital beds), the shift was more pronounced. The share of beds sited at independent hospitals declined from 51 percent to 42.5 percent (American Hospital Association, 1997, 2006) (Figure 5-5). The share of beds controlled by multihospital systems with multiple loca- 4 In a market in which five hospitals had equal shares, a merger between two of them increased the HHI by 800 points and resulted in a 5 percent price increase. An HHI of 2,000 corresponds to five firms with equal shares: HHI = 10,000*(.22 + .22 + .22 + .22 + .22) = 2,000. If two of these hospitals merge, resulting in one firm with 40% and three with 20 percent, then the HHI would increase to 2,800: HH I= 10,000*(.42 + 22 + .22 + .22) = 2,800. 5 The DOJ lost one hospital merger case in the 1980s, in Roanoke, Virginia. 6 In 2004, the FTC challenged a consummated merger between Evanston Northwestern Healthcare (ENH) and Highland Park Hospital, both located in a northern suburb of Chicago, Illinois. The administrative law judge in that case found for the Commission and ordered divestiture. On appeal, however, the Commission instead imposed a conduct remedy that required ENH and Highland Park to bargain separately with insurers. See http://www.ftc.gov/os/adjpro/d9315/index.shtm.
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The Healthcare Imperative: Lowering Costs and Improving Outcomes - Workshop Series Summary TABLE 5-1 Hospital Merger Casesa Year Merging Party Location Merger Blocked? 1989 Rockford Memorial Hospital Rockford, IL Yes 1994 Ukiah Adventist Hospital Ukiah, CA No 1995 Freeman Hospital Joplin, MO No 1995 Mercy Health Services Dubuque, IA No 1996 Butterworth Health Corp. Grand Rapids, MI No 1997 Long Island Jewish Medical Center New Hyde Park, NY No 1998 Tenet Healthcare Corp. Poplar Bluff, MO No 2000 Sutter Health System Oakland, CA No 2004 Evanston Northwestern Healthcare Evanston, IL N/A 2008 Inova Health System Manassas, VA Yes aUnited States v. Rockford Mem. Hosp., 717 F.Supp. 1251 (N.D. Ill. 1989), aff’d, 898 F.2d 1278 (7th Cir.), cert. denied, 498 US 920 (1990); Ukiah Adventist Hospital v. FTC, No. 93-70387 (9th Cir. May 18, 1994); FTC v. Freeman Hospital, 911 F.Supp. 1213 (W.D. MO. 1995), aff’d 69 F.3d 260 (8th Cir. 1995); United States v. Mercy Health Services, 902 F.Supp. 968 (N.D. Iowa 1995), vacated as moot, 107 F.3d 632 (8th Cir. 1997); FTC v. Butterworth Health Corp., 946 F.Supp. 1285 (W.D. Mich. 1996), aff’d per curiam, No. 96-2440 (6th Cir. July 8, 1997) (unpublished); United States v Long Island Jewish Medical Center, 983 F.Supp. 121 (E.D.N.Y. 1997); FTC v. Tenet Healthcare Corp., 17 F.Supp. 2d 937, 943 (E.D. Mo. 1998), rev’d 186 F.3d 1045 (8th Cir. 1999); California v. Sutter Health Sys., 84 F. Supp. 2d 1057 (N.D. Cal.), aff’d mem., 2000-1 Trade Cas. (CCH) U 87,665 (9th Cir. 2000), revised, 130 F. Supp. 2d 1109 (N.D. Cal. 2001); Final Order, In re Evanston Northwestern Healthcare Corp., No. 9315 (Federal Trade Commission Apr. 24, 2008), http://ftc.gov/os/adjpro/d9315/080424finalorder.pdf; and Complaint, In re Inova Health Sys. Found., No. 9326 (Federal Trade Commission May 8, 2008), http://www.ftc.gov/os/adjpro/d9326/080509admincomplaint.pdf. tions within an MSA increased from 21 percent to 27 percent of beds (American Hospital Association, 1997, 2006). By the standards outlined by the DOJ and FTC in the Horizontal Merger Guidelines, most MSAs were already highly concentrated by 1997, when the simple average HHI within an MSA was over 4,000.7 By 2006, the average HHI rose an additional 299 points. Weighting MSAs by admissions, the average 1997 HHI was still over 2,000 and rose by 253 points by 2006 (Figure 5-6). Based on the Town and Vogt (2006) conclusion that prices increase by 1 percent per 160-point increase in HHI, hospital consolidation between 1997 and 2006 likely resulted in a 1.9 percent increase in hospital prices across MSAs and an average 1.6 percent price increase across patients.8 7 The antitrust agencies define markets with HHIs above 1,800 as “highly concentrated.” U.S. Department of Justice and Federal Trade Commission, Horizontal Merger Guidelines, http://www.usdoj.gov/atr/public/guidelines/horiz_book/15.html. 8 An increase in concentration in a larger MSA will affect more patients than a similar increase in a smaller MSA. The effect for the average patient, across MSAs, is computed by
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The Healthcare Imperative: Lowering Costs and Improving Outcomes - Workshop Series Summary FIGURE 5-4 The extent of hospital consolidation. NOTE: MSA = metropolitan statistical area. FIGURE 5-5 The share of beds owned by independent hospitals and multihospital systems. NOTE: MSA = metropolitan statistical area.
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The Healthcare Imperative: Lowering Costs and Improving Outcomes - Workshop Series Summary That estimate accounts only for price changes driven by merger activity from 1997-2006, and thus does not capture the effects of the pre-1997 hospital mergers. A simple counterfactual scenario provides a conservative estimate of the approximate magnitude of the cumulative effects of hospital consolidation on prices. In particular, suppose that all MSAs that could be “unconcentrated” in 2006 were in fact unconcentrated.9 This exercise effectively “unconsolidates” the MSAs that saw substantial consolidation and then estimates the resulting change in price.10 This counterfactual scenario indicates that, in an unconsolidated world, hospital prices (to private payers) would be about 8 percent lower on average in these MSAs. The hospital prices faced by the average patient, computed by weighting by MSA admissions, would be about 6 percent lower. Hospital Consolidation and Healthcare Expenditures Within the set of 94 MSAs for which (1) hospital ownership is concentrated, and (2) the population is large enough to support multiple independent hospitals (i.e., the MSA could in principle be unconcentrated), privately insured patients and their insurers pay about 6 percent more than they otherwise would. These 94 concentrated MSAs account for 60 percent of admissions among all MSAs, and about 85 percent of all admissions are to hospitals in an MSA. Thus, roughly half (0.85*0.60) of privately insured patients are paying 6 percent more than they would absent hospital consolidation. This indicates that nationwide payments to hospitals on behalf of the privately insured are about 3 percent higher than they would be absent hospital consolidation. Payments to hospitals by private insurers represent about 13 to 14 percent of total U.S. expenditures on health care. In combination, these statistics indicate that total national healthcare expenditures are roughly 0.4 to 0.5 percent higher ($10 billion to $12 billion in annual expenditures) than they would be absent the price increases resulting from hospital consolidation. taking the admission-weighted average of HHIs. That the weighted average change is smaller than the unweighted average change indicates that concentration increased somewhat more in smaller MSAs. 9 An MSA is defined as “relatively unconcentrated” if the HHI equals 2,000 (this is a conservative estimate; as in most contexts, an HHI of 2,000 indicates high concentration). An MSA is defined as large enough that it “could be relatively unconcentrated” if there are sufficient admissions in the MSA to support five or more hospitals (this requires 45,000 or more admissions per year). Ninety-four MSAs have an HHI over 2,000 and are large enough to support five or more hospitals. These 94 MSAs account for about 60 percent of admissions among the 336 MSAs. 10 That is, compute the predicted price effect of reducing the HHI in these 94 MSAs from the observed level in 2006 to 2,000.
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The Healthcare Imperative: Lowering Costs and Improving Outcomes - Workshop Series Summary projects compared with the often nonmarket and excessive payments made under administrative pricing schedules. Second, policy makers must decide whether to allow for multiple bidding winners, thus facilitating participation by smaller suppliers, versus the lower prices and ease of administration if only a single or small number of suppliers is chosen. Choosing multiple winners also allows for competition between suppliers on the basis of quality, thus providing the consumers with greater ability to obtain high-quality goods and services. Third, the bidding process requires years of elapsed time in a public program that must publish formal regulations on the processes and policies to be used for the bidding program. Administrative pricing may be operated more quickly, depending on the process chosen to determine the amounts to be paid by Medicare. Finally, the choice must be made between the relatively “pure” market price discovery that is possible using bidding in each market, versus proxies of the market through other methods of determining prices to be paid, even those that are based on attempts to obtain market prices through other means. The Negative Consequences There are several caveats that should be noted if Medicare started reducing payments for DMEPOS to market prices, no matter what the process. Any significant reduction in payments would affect suppliers, reducing profit margins, and potentially leading to consolidation in the industry. Also, in a competitive bidding environment, nonselected suppliers would lose their Medicare business, at least for those categories of supplies that they were not chosen to provide, which would lead to a large reduction in business since Medicare makes up roughly half of the business of many suppliers. Suppliers will be quick to note that Medicare imposes costs that are not reflected in Internet prices, including requirements for beneficiary education, billing, maintenance, and new requirements for accreditation and surety bonds. Conclusion The narrative of difficulties in applying competitive bidding to purchase DMEPOS and other items and services in the Medicare program is instructive about the difficulty of achieving healthcare payment reform in general. In this case, it appeared that all of the stars were aligned for payment reform. Medicare transparently pays more than market prices for DMEPOS items. The DMEPOS bidding demonstration showed an ability to reduce payment levels while maintaining access for high-quality items. The initial stage of the Medicare DMEPOS bidding program in 10 geographic areas yielded average reductions in payments of 26 percent. Even so, the politi-
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The Healthcare Imperative: Lowering Costs and Improving Outcomes - Workshop Series Summary cal backlash proved a formidable challenge to the widespread adoption of competitive bidding as a price-stabilizing option. While well organized, the DMEPOS industry has far less political influence than many other health industry members such as physicians and hospitals. Echoing some problems in the bidding that were cited by industry representatives, Congress subsequently delayed the DMEPOS bidding program for 18 months. Larger-scale reforms, even with an adapted version of competitive bidding, will face difficult political obstacles, and those costs must be evaluated in addition to the administrative and other considerations of these efforts. MEDICAL DEVICE PRICES Jeffrey C. Lerner, Ph.D. ECRI Institute The market for medical devices (including capital equipment and supplies) in the United States in 2008 was approximately $153 billion.13 In this paper, we estimate that hospitals, the primary purchasers of devices, would have saved approximately 3.1 percent or $4.73 billion in 2008 had they negotiated with manufacturers to achieve average savings for every device they bought. Financial waste in the medical device market is likely driven by both pricing practices and overutilization. While reducing overutilization might produce much greater savings, it would be complicated and uncertain. Therefore, this paper does not take clinical appropriateness into account. In this paper, we concentrate on medical device prices alone, looking at how prices are set and how market practices could be improved. The Medical Device Market Let us step back to examine some characteristics of the medical device market, aspects of it that function differently and therefore affect the means for reducing costs, and the changing dynamics that threaten the savings that are now achievable. First, we must acknowledge that data for the medical device industry is extremely difficult to gather in meaningful ways. Information on the categories of medical devices we wish to examine is not gathered or compiled consistently. Furthermore, this market has not been 13 This calculation was arrived at by taking a figure for 2006 of $131.6 billion and inflating it by 7.7 percent annually over the next 2 years (Donahoe and King, 2009). “Estimates of Medical Device Spending in the United States.” Retrieved June 17, 2009, from http://18.104.22.168/search?q=cache:Gxmo1jaF4qQJ:www.amsa.org/business/King%2520Paper%2520Medical%2520Device%2520Spending.pdf+Donahoe+G,+King+G.+Estimates+of+medical+device+spending+in+the+United+States.&cd=1&hl=en&ct=clnk&gl=us.
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The Healthcare Imperative: Lowering Costs and Improving Outcomes - Workshop Series Summary subject to a great deal of study, despite its size and importance in modern medicine.14 Exacerbating these limitations, few purchasers pay list prices in this market. The price of a device is often bundled with a range of services, and providing rebates is common. The size and characteristics of the market for medical devices are further complicated by the sheer number of products and the rates at which manufacturers introduce technical changes in their products. For example, ECRI Institute categorizes a half-million supply items bought by hospitals into 2,278 categories in the Institute’s Universal Medical Device Nomenclature System (UMDNS). ECRI Institute currently captures information on 4,983 models of capital equipment, classified into 962 UMDNS technology categories in 2009. Each type of supply and capital equipment that hospitals buy is purchased differently, and within each type or category, the processes vary. It is important to understand this because our premise for this paper is that “financial waste” is the amount of money paid by U.S. hospitals above the average amount for the same equipment. Focusing on Medical Supplies The best data we have found on prices paid is for medical devices that are classified as supplies. We categorize supplies into two types. First, there are medical/surgical supplies, such as syringes, catheters, tongue depressors, etc. According to a study published in April 2009 (Schneller, 2009), hospitals in a large survey purchased 72.8 percent of their goods through group purchasing organizations (GPOs) and had average savings of about 18.7 percent. Most of these goods are medical/surgical supplies. Since hospitals are already achieving these savings, they are not included in our estimate of the additional 3.1 percent. The category of supplies also includes sophisticated devices, such as hip and knee replacements, implantable defibrillators and pacemakers, artificial spinal discs, and a range of other implants, collectively known as physician preference items (PPIs). Despite some variation in what some parties consider PPIs (e.g., surgical thread may or may not be defined as a PPI), the costs of these supplies are significant. This, along with many other factors, complicates the categories we are discussing. Just how much of the category of supplies are PPIs varies among hospitals, and it is consequential for the arguments made in this paper. In letters sent to Senators Grassley and Specter in 2007, one large hospital system stated “medical and implantable devices make up 40 percent to 55 percent of a hospital’s total supply 14 Donahoe and King could find no empirical studies on “systemic spending on all types of medical devices” as of January 31, 2006. Burns notes the lack of comprehensive studies of the medical device purchasing/supply chain.
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The Healthcare Imperative: Lowering Costs and Improving Outcomes - Workshop Series Summary expense; in our case, implantable devices cost approximately $65 million annually” (Siegfried, 2007). A much smaller rural regional medical center reported that “medical device spending [i.e., PPI] here comprises approximately 40 percent of our total medical supply expense and is nearly $3 million annually” (Nelson, 2007). Complications to Market Pricing for Physician Preference Items Looking at how PPIs are bought and sold sheds light on some of the market fragmentation that may be driving significant excess costs to hospitals. Some manufacturers of PPIs insert “confidentiality clauses” into their contracts and other purchase documents with hospitals that prohibit these hospitals from disclosing prices paid to third parties. This practice can derail the negotiation of fair prices by precluding the hospital from disclosing prices to implanting physicians, other hospitals, consultants who help them purchase equipment, benchmarking pricing services, patients, and insurers. Some manufacturers have aggressively sought to reinforce and spread the use of these price-secrecy clauses, including the claim that prices are protected as “trade secrets.” These arguments have been the subject of recent articles, most notably in the health policy journal Health Affairs and in legal writings (Bridy, 2009; Lerner et al., 2008). Physicians have long been insensitive to the prices their hospitals pay. A PPI, as the name indicates, is specified by physicians, but it is the hospital that purchases the supplies. One explanation for the perpetuation of this divide between the decision makers and purchasers is that hospital administrators are reluctant to disrupt the relationships with manufacturers of products preferred by their major revenue-generating physicians. However, reform may come. Senators Grassley and Specter have introduced the Transparency in Medical Device Pricing Act of 2007 (S. 2221), which would require manufacturers to report their median and mean prices for PPIs quarterly to the Centers for Medicare & Medicaid Services. Other options to keep manufacturers from making prices opaque, such as banning the signing of secrecy clauses by hospitals doing business with the Medicare program, have also been proposed. With some 60 percent of the expenditures on medical devices potentially subject to secrecy clauses, this issue looms large in the ability to achieve the 3.1 percent average savings upon which we based our estimate of waste (Lerner et al., 2008).15 15 Senator Specter said, in introducing his legislation, S. 2221 Specter, A. (2007, October 23, 2007). “Arlen Specter Speaks on the Senate Floor Regarding the Transparency in Medical Devices Act.” Retrieved June 26, 2009, from http://specter.senate.gov/public/index.cfm?FuseAction=NewsRoom.ArlenSpecterSpeaks&ContentRecord_id=cf655dfb-1321-0e36-bab2-05c5b6002908.
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The Healthcare Imperative: Lowering Costs and Improving Outcomes - Workshop Series Summary The Cost Savings Opportunity Data kept by government agencies and, to our knowledge, the private sector, fail to segregate the supplies market into medical/surgical supplies and PPIs in ways that are useful for calculating waste. Even so, cost savings can be estimated if both supply types are combined. For the purpose of this paper, ECRI Institute evaluated datasets of supplies from 123 hospitals that provided their complete “item masters” of purchases recorded from January 1, 2009 to May 1, 2009. Table 5-4 illustrates the findings from an analysis of actual prices paid, demonstrating that these hospitals collectively could achieve an average 3.1 percent savings if they negotiate to the average price paid for every supply item.16 We derive our estimate of total potential national savings in 2008 by multiplying the size of the market for medical devices, $152.65 billion, by 3.1 percent to arrive at $4.73 billion. We use supplies from this study, most of which are medical devices, as a surrogate for all medical devices. In Table 5-4, the sample of data from the 123 hospitals is arrayed so that the three hospitals with the largest total amounts spent on supplies are at the top and the three with the lowest are at the bottom. It indicates that even hospitals that negotiate well can still capture additional savings. Hospital #1 could achieve 1.72 percent in additional savings (or $2.85 million) if it negotiated to the average price paid for every item. This table also illustrates that were Hospital #1 able to negotiate the lowest prices from among the 123 hospitals, it would achieve an 8.13 percent savings. However, for the purpose of our calculation of 3.1 percent in “financial waste,” we assume that only the current average matters, even though it may be very possible to develop strategies that would create greater average savings among all hospitals. In fact, policy makers are considering options such as bundling payments to physicians and hospitals, which they believe will create greater incentives for these parties to work together to lower prices. Were this to happen and were secrecy clauses limited or banned, it would be possible to imagine savings in excess of 3.1 percent.17 To illustrate this possibility, we summarized the prices hospitals paid for a small sample of PPIs (Table 5-5). Different-sized hospitals buying “Since national sales of implantable devices are approximately $65 billion annually, with an expected growth in utilization of close to 20 percent, the potential of adding 8 to 15 percent annual price increases to the expenditures clearly demands attention.” The years on which he based his data are unknown. If PPI prices and/or utilization expand at a greater rate than other technologies, and if new, more expensive models continue to proliferate at a rapid rate, then the importance of being able to negotiate prices most effectively will increase as well. 16 No hospital in the study currently negotiates to the average price for every item purchased. 17 Additional strategies are described in Lerner et al. (2008) and Burns (2002).
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The Healthcare Imperative: Lowering Costs and Improving Outcomes - Workshop Series Summary TABLE 5-4 Medical/Surgical Supplies and Implants: Total Spending and Potential Savings from a Sampling of Hospitals Facility Total Spending Potential Savings If Lowest Price Achieved Percentage Savings If Lowest Price Achieved Potential Savings If Average Price Achieved Percentage Savings If Average Price Achieved Hospital #1 $165,287,541 $13,452,180 8.1 $2,854,653 1.7 Hospital #2 $132,869,401 $13,380,529 10.1 $2,467,469 1.9 Hospital #3 $128,241,519 $16,382,166 12.8 $4,310,728 3.4 . . . . . . . . . . . . . . . . . . Hospital #121 $1,213,521 $152,161 12.5 $50,228 4.1 Hospital #122 $1,179,089 $28,629 2.4 $6,594 0.6 Hospital #123 $1,112,824 $67,479 6.1 $13,997 1.3 Average 12.3 3.1 SOURCE: Reprinted with permission from ECRI Institute. different volumes of the same brand and model of pacemaker paid different prices. While that might seem intuitive, the pattern illustrated is not. Essentially, there is no pattern. For example, a 1,900-bed hospital/health system buying 25 pacemakers paid $287 more per pacemaker than a 200-bed hospital buying only 9 pacemakers. Because hospitals are ignorant of the prices they pay relative to other consumers, they may simply accept statements by manufacturer sales representatives that the hospital is getting the best price. Manufacturers have a great deal more aggregated information on prices offered to customers than do individual hospital customers. Enhancing the transparency in the market allows the purchaser to verify claims and to negotiate prices more effectively. TABLE 5-5 Volume and Price Paid per Unit for the Same Brand of Model of Pacemakers by a Variety of Hospitals Number of Beds* Volume Purchased Price Paid per Unit 600 6 $4,400 1,100 17 $4,500 200 9 $4,513 600 33 $4,650 300 15 $4,700 1,900 25 $4,800 500 20 $4,837 100 38 $5,000 *Number of beds rounded to the nearest 100 beds. SOURCE: Reprinted with permission from ECRI Institute.
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The Healthcare Imperative: Lowering Costs and Improving Outcomes - Workshop Series Summary An analysis of a subset of data consisting only of PPIs from the 123 hospitals studied showed an average potential savings of 27 percent as opposed to 3.1 percent for all technologies combined. Capital Equipment Purchases The final category of medical devices we need to consider is capital equipment (e.g., computed tomography scanners, anesthesia units, linear accelerators, electric beds, laboratory analyzers). These technologies are purchased less frequently than supplies, but they represent very large expenditures. Unfortunately, we do not have a satisfying estimate of the proportion of the $153 billion device market is made up of capital equipment purchases (Burns, 2002).18 We do, however, have evidence of great variation in prices offered to hospitals. Table 5-6 shows data from 1,500 hospitals and health systems and prices for five types of capital equipment studied between May 1, 2008, and May 1, 2009. This table indicates that hospitals are quoted prices that are on average 29.6 percent lower than the list price. Hospitals in this study did not report the actual prices paid after their negotiations. In this way, it is different from the price-paid supplies data that was cited previously. Consequently, we assumed only that an additional 3.1 percent of the capital portion of the expenditure could be saved (i.e., the same percentage we used for the supplies). Notably, as with PPI purchases, small hospitals buying the exact same equipment may pay less for it than large hospitals. For example, based on ECRI Institute study data, ACME Imaging offered a community hospital a cardiac ultrasound system at a 43.7 percent discount while offering only a 33 percent discount on the same brand and model system to a larger hospital. It might come as something of a shock to the executive teams in large hospitals that, despite their beliefs, they are not always offered the best discounts. Conclusion The above analysis shows substantial savings but perhaps less than some policy makers might believe possible. These policy makers might note that the United States spends far more per capita on medical devices than the second largest purchaser of medical devices in the world, Japan, or the third largest purchaser, Germany (Table 5-7). Analyses conducted by the McKinsey Global Institute that were discussed as part of this workshop demonstrate that the United States spent 18 Definitions of what comprises capital equipment vary. They may be merged with capital expenditures on buildings and they may also include durable medical equipment.
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The Healthcare Imperative: Lowering Costs and Improving Outcomes - Workshop Series Summary TABLE 5-6 Prices and Discounts Obtained for Capital Equipment Device Average Percentage Discount, % (Range) Average List Price Number of Hospitals Reporting Data Scanning systems, computed tomography 35.8 (12.0-58.1) $1,582,591 76 Anesthesia units 24.2 (10.0-45.8) $59,378 171 Radiotherapy systems, linear accelerator 46.6 (13.5-66.2) $4,467,482 34 Beds, electric 30.1 (7.2-45.0) $16,658 162 Analyzers, laboratory, hematology, cell counting, automated 35.1 (1.0-72.0) $157,138 93 SOURCE: Reprinted with permission from ECRI Institute. TABLE 5-7 Comparison of Medical Device Expenditures Across Countries MD Expenditures as a Percentage of THE THE as a Percentage of GDP MD Expenditure per Capita (€)* United States 5.1 13.9 278 Japan 5.1 7.6 158 Germany 8.6 10.7 230 France 5.8 8.6 107 United Kingdom 4.8 7.6 97 NOTE: GDP = gross domestic product; MD = medical devices; THE = total health expenditures. *Prices are expressed in Euros. SOURCE: Adapted from CERM, 2005. $18 billion above the Estimated Spending According to Wealth (ESAW) on medical devices. “The U.S. spends 54 percent above its ESAW on the top 5 inpatient devices—defibrillators, pacemakers, coronary stents, hip implants, and knee implants—when compared with Europe and Japan” (Angrisano et al., 2007). The report goes on to say that the wealth-adjusted cost of a knee implant is 32 percent higher and hip implants 60 percent higher than the average of those in France, Germany, Italy, and the United Kingdom (Angrisano et al., 2007). Whether due to the higher prices, greater utilization, or additional factors such as “upselling,” it is complex to alter the current organizational structure and condition of health care in the United States.19 Even when we restrict our analysis of financial waste to prices alone, there are many caveats. In addition to those we have already mentioned, 19 Upselling takes place when manufacturer representatives present in the operating room suggest using more expensive devices to surgeons.
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The Healthcare Imperative: Lowering Costs and Improving Outcomes - Workshop Series Summary our reliance on information for supplies as a proxy for all U.S. device expenditures and potential savings is a limitation. Hospitals are the major purchasers of medical devices, but they are not the only purchasers. Outpatient clinics and physician groups buy devices, but we were not able to study the prices they pay. The figure of $152.65 billion dollars in 2008 also includes, for example, durable medical equipment bought by home health agencies and patients. But since no figures are available to perform a reliable analysis, we have based our analysis on what we do know. In summary, we believe that close to $5 billion (3.1 percent of total national expenditures for medical equipment) could have been saved in 2008, and similar savings would accrue in the future, if better negotiating processes were deployed in hospitals. The latter includes using benchmarking data to the fullest extent possible to achieve average prices. Even current savings will decline if aggressive efforts to make prices opaque are not remediated. REFERENCES American Hospital Association. 1997. AHA Annual Survey Database. Chicago: American Hospital Association. American Hospital Association. 2006. AHA Annual Survey Database. Chicago: American Hospital Association. Angrisano, C., D. Farrell, B. Kocher, M. Laboissier, and S. Parker. 2007. Accounting for the Cost of Health Care in the United States. Washington, DC: McKinsey Global Institute. Bridy, A. 2009. Trade secret prices and high-tech devices: How medical device manufacturers are seeking to sustain profits by propertizing prices. Texas Intellectual Property Law Journal Winter 17(2):187-222. Burns, L. R. 2002. The health care value chain: Producers, purchasers, and providers. San Francisco: Jossey-Bass. California HealthCare Foundation. 2009. Health Care Costs 101. http://www.chcf.org/topics/healthinsurance/index.cfm?itemID=133630 (accessed April, 2009). Capps, C., and D. Dranove. 2004. Hospital consolidation and negotiated PPO prices. Health Affairs (Millwood) 23(2):175-181. Capps, C., D. Dranove, and M. Satterthwaite. 2003. Competition and market power in option demand markets. RAND Journal of Economics 34(4):737-763. CMS (Centers for Medicare & Medicaid Services). 2007. Improper Medicare fee-for-service payments report-May 2007 report. http://www.cms.hhs.gov/apps/er_report/preview_er_report.asp?from=public&which=long&reportID=6 (accessed June 12, 2009). ——. 2008. Medicare to save average of 26% for some durable medical equipment, prosthetics, orthotics, and supplies in selected areas; beneficiaries to join in savings. http://www.cms.hhs.gov/apps/media/press/release.asp?Counter=2996&intNumPerPage=10&checkDate=&checkKey=&srchType=1&numDays=0&srchOpt=0&srchData=&keywordType=All&chkNewsType=1%2C+2%2C+3%2C+4%2C+5&intPage=&showAll=1&pYear=1&year=2008&desc=false&cboOrder=date (accessed June 2009). ——. 2009a. Category definitions: National health expenditures. http://www.cms.hhs.gov/NationalHealthExpendData/downloads/quickref.pdf (accessed June 11, 2009).
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