Innovation Policy and the Economy

Overview

The economic importance of innovation brings with it an active debate on the impact public policy has on the innovation process. This annual series, sponsored by the National Bureau of Economic Research, brings the work of leading economic researchers to the broader policy community. Issues covered in Volume 11 include an exploration of innovation challenges in the health care and cleantech industries and the implications for public policy; a reconsideration of static antitrust analysis on innovation incentives, ...

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Overview

The economic importance of innovation brings with it an active debate on the impact public policy has on the innovation process. This annual series, sponsored by the National Bureau of Economic Research, brings the work of leading economic researchers to the broader policy community. Issues covered in Volume 11 include an exploration of innovation challenges in the health care and cleantech industries and the implications for public policy; a reconsideration of static antitrust analysis on innovation incentives, an examination of innovations in governance that encourage investment and growth, and the impact of the dynamic nature of scientific research and technological innovation on science policy.

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Product Details

  • ISBN-13: 9780226473383
  • Publisher: University of Chicago Press
  • Publication date: 2/28/2011
  • Pages: 131
  • Product dimensions: 6.64 (w) x 9.05 (h) x 0.33 (d)

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Innovation Policy and the Economy 11


The University of Chicago Press

Copyright © 2010 National Bureau of Economic Research
All right reserved.

ISBN: 978-0-226-47338-3


Chapter One

Where Are the Health Care Entrepreneurs? The Failure of Organizational Innovation in Health Care

David M. Cutler, Harvard University and NBER

Executive Summary

Medical care is characterized by enormous inefficiency. Costs are higher and outcomes worse than almost all analyses of the industry suggest should occur. In other industries characterized by inefficiency, efficient firms expand to take over the market, or new firms enter to eliminate inefficiencies. This has not happened in medical care, however. This paper explores the reasons for this failure of innovation. I identify two factors as being particularly important in organizational stagnation: public insurance programs that are oriented to volume of care and not value and inadequate information about quality of care. Recent reforms have aspects that bear on these problems.

Designing policies to lower medical spending was central to the recent health care debate in the United States. Rising health care costs are the leading contributor to projected federal deficits over the next few decades (Congressional Budget Office 2009) and make health insurance coverage expansions difficult to afford. In the private sector, high medical costs crowd out private coverage (Chernew, Cutler, and Keenan 2005) and lead to reduced employment of low-wage and secondary earners (Sood, Ghosh, and Escarce 2009).

Of course, not all medical spending increases are problematic. A good share of rising costs is attributable to the development and diffusion of new technologies (Newhouse 1992), which bring significant value (Cutler and McClellan 2001; Cutler 2004). In an efficient industry, spending more on a good is not a cause for concern.

But alongside valuable innovation is an enormous amount of inefficiency. Evidence based on cross-sectional comparisons—both across countries and within the United States—suggests that one-third or more of medical resources are not buying improved health (Institute of Medicine 2001; Cutler 2002; Fisher et al. 2003a, 2003b). In a $2.5 trillion medical care economy, this amounts to over $700 billion of excess spending annually. Understanding the causes of this inefficiency, and why it has not been eliminated, is the central goal of this paper.

One explanation for inefficient spending, common in the economics literature, is "flat of the curve" medicine (Fuchs 1974). Low patient cost sharing combined with generous provider reimbursement means that neither patients nor providers have incentives to limit care. Thus, too much is done. Flat of the curve medicine is indeed common (see below). But it is not the whole story. There are two other explanations for excessive spending that are important as well.

The second explanation is inadequate coordination of care. Many acute conditions that could be prevented are not, leading to poor health outcomes and higher spending. For example, about 20% of Medicare patients discharged from a hospital are rehospitalized within 30 days ( Jencks, Williams, and Coleman 2009), often without seeing a doctor or nurse in between; in the best systems, the rate is as low as 6%. Similarly, patients with chronic disease—hypertension, high cholesterol, diabetes, and depression, for example—are not helped adequately to control their condition. Rates of chronic disease control are no better than two in five on average. Inadequate care coordination costs lives, and likely dollars.

The third explanation for inefficiency is poorly designed production processes. Medical care providers are far less efficient than they should be. Doctors and nurses spend significant time on routine administrative tasks or clinical services that could be provided by less trained personnel. Hospitals are slow to adopt efficiency savings in surgical suites, despite evidence that they save money and improve outcomes. And mistakes are common and costly.

Medical care is complex, and it is natural that there will be inefficiencies in complex settings. Indeed, in any industry in which human action is important, there are bound to be mistakes.

The failure of medical care is not so much that mistakes are made, but rather that the system has not evolved mechanisms to minimize those mistakes. For many years, Toyota was famous for its attention to error reduction; Wal-Mart is equally known for its supply chain management. In health care, in contrast, doctors will often redo a test because the prior test results are not available or would require too much effort to obtain.

The problem in health care is not a lack of possible market organizers. Primary care physicians, for example, could coordinate care for patients with chronic disease. Similarly, multispecialty groups of physicians might combine into care organizations to make sure patients do not fall through the cracks. Alternatively, payers for medical care—insurers or the employers they contract with—could push for coordination. Even farther removed, a firm from outside medical care could enter health care and organize the care experience, as Amazon.com did with book sales and Expedia did with airline tickets. A few firms have tried in health care, but none has made more than a minor dent. The question is why.

I argue that there are two fundamental barriers to organizational innovation in health care. The first is the lack of good information on quality. Within a market, it is difficult to tell which providers are high quality and which are low quality. As a result, most consumers still rely on reputation to judge providers. Difficulty measuring quality also makes expansion of high-quality firms more difficult. The quality of a Wal-Mart store in Kansas is virtually identical to that of a Wal-Mart in Oklahoma. Thus, a firm with a general reputation for high quality can expand nationally with relative ease. Knowing that an insurer has high quality in California, however, tells consumers very little about its quality in other markets. Thus, the gains from economies of scale are limited.

The second barrier is the stagnant compensation system of public insurance plans. In most industries, higher quality is associated with higher prices. That is not true in medical care, however, largely because of the public sector. Medicare accounts for 25% of physician and hospital services, and Medicaid accounts for another 13%. Since the 1960s, Medicare has paid providers on a fee-for-service basis, without reference to the quality of care delivered. Medicaid reimbursements are more flexible, but they are so low that many providers view Medicaid patients as effectively uninsured. As a result, about 40% of the market transmits incentives to provide more care but not more efficient care (Medicare) or to avoid patients who are sick (Medicaid). With so much of compensation pegged to volume, not value, inefficient care is the natural outcome.

If inadequate information and misaligned compensation systems are the problems, one set of solutions is in that arena as well. I discuss the potential efficiency improvements from a significantly increased commitment to information collection and analysis and changing the compensation arrangements in public insurance. I show that such efforts could lower medical spending by significant amounts. I discuss briefly how the recent reform legislation dealt with these issues. I also discuss how issues such as capital constraints or the uncertain role of the consumer factor into this analysis.

The paper is organized as follows. Sections I and II provide evidence on the production inefficiencies in health care and the potential for improved outcomes. Section III lays out the puzzle of missing innovation. Sections IV and V examine the barriers to innovation for providers and payers. Section VI notes the features of the recent reform legislation that affects these areas, and Section VII presents conclusions.

I. Productivity in Health Care

Inefficient spending is an example of low productivity; more is spent than is needed to get the outcomes we get (or, equivalently, less output is produced than is possible given the inputs employed). One way to gauge the relative efficiency of health care over time is thus to compare productivity growth in health care to that in other industries.

Productivity growth is notoriously difficult to measure in health care (Berndt et al. 2000). Accurate productivity assessment requires a good output measure. Health is difficult to measure and even harder to decompose into medical and nonmedical factors. As a result, official data are much better on productivity outside of health care than they are in health care. Still, I start with the official data as they are.

Overall productivity growth in the United States as a whole was low from the mid-1970s to the mid-1990s (the "productivity slowdown"). Since the mid-1990s, however, productivity growth has increased rapidly. Productivity growth in private industry, for example, was 1.25% annually from 1987 to 1995 and 2.4% between 1995 and 2005 (Oliner, Sichel, and Stiroh 2007). Oliner et al. attribute the resurgence of productivity growth largely to greater use of information technology. Industries that use information technology above average experienced productivity growth approximately 1.5 percentage points higher than industries that did not.

The relative performance of productivity in different industries in the post-1995 era is shown in figure 1. The most productive industries were durable goods manufacturing (6.9% growth annually) and information technology (5.7% growth annually). These industries are fairly different from health care. There are some industries with high productivity growth that are more similar to health care, however. Retail trade, for example, used to be a cottage industry like health care. In the last decade and a half, however, productivity growth in retail trade averaged 4.3% annually. Professional and business services had productivity growth of 1.2% annually, another industry that is close in production to medical care.

Productivity growth in health care (along with education and social assistance) is estimated to be -0.2% annually in the official data. As noted above, this is almost surely an underestimate. But even still, the negative value is striking.

Other studies have looked more closely at health care costs and output and can be used to assess the productivity of medical care over time. Figure 2 shows the cost per additional year of life attributable to medical care between 1960 and 2000, as estimated by Cutler, Rosen, and Vijan (2006). The lower line is for newborns, with higher lines reflecting people at older ages. The highest line is for people aged 65.

The value of a year of life is generally taken to be about $100,000 (Cutler 2004). Thus, costs per year of life below this amount are generally considered to be good value, and costs above this amount are considered to be poor value. Most of the estimates of cost per year of life are below $100,000. Thus, medical care on average is giving good value for the dollar. But the trend is adverse. Cost per year of additional life was lower in the 1960s and 1970s than in the 1980s and 1990s. For the elderly, recent estimates suggest that we are spending too much to extend life, though these estimates do not account for quality of life.

At one level, the pattern of increasing cost per year of life is not entirely surprising. It may be that the most beneficial treatments were developed first, and we are simply moving down the marginal product of innovation curve. Looked at a different way, however, the finding is quite surprising. In other industries, the common denominator has not been new goods but better ways of organizing production, distribution, and sales. This organizational change has led to expanded output per dollar. In health care, however, there has been very little innovation in the organization of the system.

II. Categories of Inefficiency in Medical Care

The inefficiency of medical care production can be understood in three dimensions: flat of the curve medicine, poor coordination, and inefficient production processes.

A. Flat of the Curve Medicine

Significant evidence shows that many people receive more medical care than is appropriate for their condition, especially in acute settings. Consider the treatment of localized prostate cancer (Perlroth, Goldman, and Garber 2010). Almost all elderly men have cancer of the prostate. In many cases, however, the cancer grows slowly, and the person will die of something else before the cancer becomes fatal—or even clinically meaningful. Thus, "watchful waiting" is a common strategy. In some cases, the cancer will grow rapidly and should be treated. However, it is not always clear whether a patient has a rapidly growing cancer or not.

There are a variety of different treatments for prostate cancer. In addition to watchful waiting, men may receive radical prostatectomy (removal of the prostate), brachytherapy (radioactive implants in the prostate), external beam radiation therapy, and intensity-modulated radiation therapy. Costs increase with the intensity of care. Costs in the 2 years after diagnosis average about $50,000 for watchful waiting and radical prostatectomy, about $68,000 for brachytherapy, about $78,000 for external beam radiation therapy, and about $96,000 for intensity-modulated radiation therapy.

Some clinical evidence has examined the effectiveness of these different strategies. The results suggest that the therapies are approximately equally efficacious in men aged 65 and older, the most common group diagnosed with localized prostate cancer. In particular, there is no evidence that the newer and very expensive radiation therapies have better outcomes. There is some evidence of adverse side effects with surgery—impotence and incontinence are common outcomes—making watchful waiting even more appropriate for many men.

Still, rates of invasive treatment remain high. Only 42% of elderly men with prostate cancer receive watchful waiting. One-third receive a radical prostatectomy, 15% receive brachytherapy, 1% receive external beam radiation therapy, and 5% receive intensity-modulated radiation therapy. A final 4% of patients receive a combination of intensive treatment, which has not even been explored in the literature. Perlroth et al. (2010) conclude that savings of $1.7–$3.0 billion annually would be realized by having allMedicare patients receive guideline-concordant care.

Patient preferences are not a major part of the variation in treatment. Sommers et al. (2008) show that patients differ in their preferences for side effects and risks of metastatis, but these preferences do not predict the therapy a patient receives. Rather, patients get referred to a particular type of specialist, and this specialist then recommends the therapy that he or she judges best. Thus, patients who see only a urologist most frequently undergo a radical prostatectomy, whereas patients seen by a radiation oncologist undergo some form of radiation.

The standard economic framework rationalizing this outcome is shown in figure 3 (see Fuchs 1974). Potential output is shown by the concave production possibility frontier. The marginal value of life is shown by the straight line, assumed constant over this interval. The optimal point for society is for patients to receive care until the marginal value of care is equal to the marginal cost, shown by point A.

Most patients are insured, however, and physicians are often paid above marginal cost. Each of these factors provides incentives for additional care above what is optimal. This is shown by point B in the figure. If the production function is sufficiently flat, outcome differences between points A and B would be difficult to detect, even at very different treatment rates. Point B is allocatively inefficient. The care that is provided is technically correct but is not appropriate.

Other countries appear to have less overused care than the United States. Because of the tighter restrictions on overall supply, the number of procedures performed is lower elsewhere (Cutler 2002). Thus, it is natural to think of lower-spending countries as occupying a point like A in comparison to the United States, perhaps at point B.

The overall amount of money spent on allocatively inefficient care has been a subject of some debate. Comparing different regions of the United States, Fisher et al. (2003a, 2003b) estimated that about 30% of medical care utilization in the Medicare population is associated with care that is not contributing to improved health. Other studies suggest that the number may be smaller (Elmendorf 2009) or larger (other countries spend about half the U.S. amount). Table 1 shows the estimate of 30% possible savings.

The flat of the curve model is undoubtedly part of the explanation for high medical spending, but it is unlikely to be the only important factor.

(Continues...)



Excerpted from Innovation Policy and the Economy 11 Copyright © 2010 by National Bureau of Economic Research. Excerpted by permission of The University of Chicago Press. All rights reserved. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher.
Excerpts are provided by Dial-A-Book Inc. solely for the personal use of visitors to this web site.

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Table of Contents

Introduction Josh Lerner Scott Stern xi

1 Where Are the Health Care Entrepreneurs? The Failure of Organizational Innovation in Health Care David M. Cutler 1

2 Cap-and-Trade, Emissions Taxes, and Innovation Suzanne Scotchmer 29

3 When Is Static Analysis a Sufficient Proxy for Dynamic Considerations? Reconsidering Antitrust and Innovation Joshua S. Gans 55

4 Innovations in Governance Raymond Fisman Eric Werker 79

5 As Science Evolves, How Can Science Policy? Benjamin F. Jones 103

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