Hospitals in the U.S. spend an estimated 40% of their annual budgets on supplies, medical devices and equipment alone.[1, 2] According to the American Hospital Association, in 2015 total expenditures by U.S. hospitals was $808 billion.[3] That implies that if the 40% supply and equipment estimate is correct, hospitals are spending more than $320 billion on supplies and equipment annually. That’s a hefty 11% of all health expenditures in the U.S. More importantly, the volume and proportions continue to grow.[4, 5]
Given that most hospital revenue comes in the form of administered pricing– fixed-price fee schedules for episodes of care—the supply, equipment and device budget is a very important focal point for cost control. Historically, this has not always been the case; the “medical arms race” encouraged hospitals to compete against each other in non-price dimensions by acquiring devices and equipment to attract doctors and patients.[6] But all that changed over the past two decades as employers and managed care organizations became less tolerant of persistent annual budget increases in the double digits. Now, at least half of all medical supply, device and equipment purchases are done so with cost reduction as the main goal.[7] In addition to cost control, decision makers are weighing issues regarding “comparative effectiveness research” much more so now than in the past. In a Google survey, 60% of hospital decision makers “look to improve clinical outcomes when deciding on a purchase,” but weigh those decisions against cost considerations.[7] In a peer review survey by Health Care Financial Management regarding hospital purchasing decisions, 63% of respondents strongly agreed that purchasing decisions are driven by whether the product represents “a good value for the cost.”[8]
Hospital supply chain purchasers managed this transition in primarily three ways. First, many have established “Value Analysis Committees” (VACs), also known as Value Analysis Teams (VATs) or Technology Assessment Committees (TACs).[9, 10] The job of the VAC is to evaluate existing evidence in support of the device (for example, from the medical literature or the manufacturer), and to weigh that evidence against a number of other important factors, including costs, costs of ownership, the needs of physicians, and the objectives of the organization. Second, supply chain purchasers have reduced the number of vendors with which they contract, thereby reducing inventory costs and allowing closer scrutiny of product attributes and effectiveness.[1, 5, 8, 9, 11] Third, they have flagged devices known as “physician preference items” (PPIs) and have focused on working closely with physicians to integrate comparative effectiveness research, and the VAC, into assuring that the PPIs are limited to devices that represent good value for money.[4, 12]
The latter issue regarding PPIs is particularly important, as admitting physicians and surgeons are the main drivers of hospital revenue. It’s not always practical to simply ask that surgeons all use, for example, the same implant; surgeons typically learn to work with one product line and stick to it rather than undergo retraining for a new device.[6] At the same time, physicians tend to develop close relationships with specific device vendors, further complicating the ease through which the number of vendors can be reduced.[13] Moreover, physicians typically have little or no idea how much devices cost. One study found that 81% of physicians surveyed could not estimate the costs of medical implants, and those who did hazard a guess were off by an enormous range, from 1.8% to 5,410% of the actual device cost.[14]
One area in which hospital supply chain purchasers and their value analysis committees have experienced challenges is in how to conduct comparative effectiveness research, economic analysis and hospital value analysis on the supplies and devices that cross their desks. The value analysis committees essentially have three pieces of evidence to work with: (1) the testimonials and opinions of their staff physicians; (2) the published literature (which may or may not directly address the device in question); and (3) reports provided directly by the manufacturer. However, each of these have important limitations. Testimonials from hospital physicians may be motivated by their personal connections to the device manufacturer or their own biases, and may not be “evidence based” in an objective sense. Published literature can provide a good foundation for evidence, but the literature can be difficult to digest and summarize, and may not apply directly to the device in question. Finally, evidence provided by the manufacturer may be biased and will likely be based on highly controlled clinical studies, and it’s not clear that the hospitals’ “real world” population will achieve similar outcomes.
There are two ways in which hospital supply chain decision makers and value analysis committees can overcome these evidence hurdles and help transform the evaluative functions of purchasing from ad hoc number crunching and price negotiation to comprehensive value-based purchasing. For the purposes of this discussion, let’s refer to the three types of evidence described above as follows, reordering in terms of generally accepted guidelines on quality of evidence:[15-18] A = peer-reviewed published literature; B = testimonials and opinions of staff physicians; and C = unpublished research sponsored by the manufacturer.
The normal activities of today’s hospital value analysis committee, or similar purchasing committees, does typically make use of all three types of evidence in their decision-making process, in addition to the normal due diligence involving safety, reliability, redundancy and total costs of ownership.[1, 2, 7-11, 19-24] However, where supply chain purchasers have encountered difficulties is in weighing and assessing the quality of effectiveness research, and whether a significant gap is a fatal gap or just a bump in the road. As we emphasized above, Type A evidence has the advantage of having been vetted by peer-review, but may not fit exactly with the device in question or the population to whom the device would be aimed. Type B evidence is potentially clouded by a number of biases, and Type C evidence must be rigorously evaluated in terms of methodology and applicability to the real world. These are not generally tasks that most value analysis committees are equipped to perform, certainly not on a scale necessary for a large health system and given today’s rapid pace of technological innovation.
Consider the diagram below. The VAC must, to some degree, make a determination on the quality of evidence associated with a supply, device or piece of equipment under consideration. If the committee obtains Type B and Type C evidence but observes only weak Type A evidence, then there is insufficient evidence (clinical or economic, or both) to support value-based purchasing. In these cases, we believe that the most useful remedy is to perform some simple calculations, making use of all available data. These calculations take the form of literature synthesis and meta-analysis and conceptual economic models.
Literature syntheses and meta-analyses can be used to fill gaps in clinical evidence. In the absence of directly relevant clinical studies, data can be extrapolated based on reasonable assumptions and it may be possible to conduct meta-analyses including manufacturer’s own study data and published studies.
Economic evaluations can be conceptual or rigorously formal. In some cases, peer-reviewed economic studies may exist, but device manufacturers have historically not been forward thinking in conducting such studies, so it is rare that peer-reviewed economic studies exist at the time of the purchasing decision. In order to be of most use to purchasing, economic studies must be done quickly. One very good option is a logic-based conceptual economic model, which makes use of all relevant and available data but without the intricate structure of a formal economic model. Conceptual models depend on reliable core data combined with reasonable and logical assumptions. These models can be completed quickly, and can provide confidence intervals around key findings. Conceptual economic models can fill the gaps in peer-reviewed literature and can be used as a basis to evaluate unpublished studies conducted by manufacturers.
These supplemental studies can be of great value in supporting hospital supply chain purchasing decisions, and help bridge the gap between existing evidence an informed evidence-based purchasing decisions.
(John Schneider, PhD, Cara Scheibling, and Ivana Stojanovic, MA)
References
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