Chapter
7

Changes in Health Habits and Medical Practices

Some proposals to modify the health insurance system would include provisions designed to change the behavior of individuals and medical providers. Certain provisions could:

■	Encourage individuals to adopt healthier lifestyles or to get recommended vaccinations and screening tests.

■

Induce changes in the ways that medical providers treat patients and diseases—with the goal of improv­ing that care—by expanding the role of primary care physicians (as part of a "medical home" concept in which care is coordinated across settings); implement­ing programs to help manage care for chronic diseases; funding research on the comparative effectiveness of different treatment options; and making investments in health information technology.

■	Focus on the ways in which patients and medical pro­viders settle disputes about treatment, by modifying the system that determines liability for medical mal­practice.

Each of those initiatives could improve people’s health or the quality of care that they receive. For example, vaccines can prevent the spread of diseases, screening tests may be able to detect illnesses at an earlier and more treatable stage, and improvements in care coordination could ensure that treatments follow evidence-based guidelines and that patients avoid unnecessary or duplicative tests. A question that often arises is whether those initiatives would also reduce health care spending—and thus whether they would affect the budgetary costs of a broader proposal aimed at expanding health insurance coverage. A related question is whether initiatives that reduce certain types of health care spending can yield sufficient savings to offset the costs of the initiatives themselves.

Although such initiatives would, in many cases, result in better health, it is less clear that they would reduce total spending for health care. Many studies that have exam­ined the impact of such initiatives do not indicate net savings, for several reasons. In some cases, the challenge is largely one of identifying and targeting those people whose participation in a health care initiative would result in net savings. Broad programs aimed at preventive medical care and disease management could reduce the need for expensive care for a portion of the population, but they could also provide additional services, and incur added costs, for individuals who would not have needed costly treatments anyway. In order to generate net reductions in spending, the savings such interventions generate for people who would have otherwise needed expensive care must therefore exceed the costs of vaccinating, screening, or coordinating care for much larger populations.

A related issue is that many individuals or health plans might already be taking the steps involved (or will do so in the future) even in the absence of a new requirement or incentive (such as a subsidy). The effect of any pro­posal would have to be measured against that trend, and a large share of any subsidies involved could simply go to people or plans who will take those steps without new requirements or incentives (sometimes referred to as "buying out the base"). Some doctors and hospitals have already adopted electronic medical recordkeeping, for example, and more will do so in the future under current law—so any subsidy payments those providers might receive under a proposal would add to its costs but would not affect its impact.

In other cases, doctors and patients may not have suffi­cient incentives to change their use of health care services. Even though research may indicate that a given treatment is no better clinically than a less expensive alternative—or that the incremental benefits of the more expensive treat­ment do not warrant its added costs—health care practi­tioners may continue to provide the more expensive treat­ment if the payments they receive or the share of costs paid by patients are not adjusted to reflect those findings. Similarly, proposals to establish "medical homes" may have little impact on spending if the primary care provid­ers who would coordinate care do not receive financial incentives to limit their patients’ use of specialists.

Other initiatives that would yield health benefits might not generate substantial savings—at least, not in the near term. Taxes on tobacco or junk food have been shown to be effective at reducing smoking and obesity (particularly among young people). However, the effects of such initia­tives—particularly those that seek to prevent the onset of unhealthy behavior in childhood—on health care spend­ing would probably take years to materialize. In the long term, spending on diseases caused by that unhealthy behavior could decline substantially, but the impact on federal costs would also have to account for people living longer and receiving Social Security and Medicare benefits for more years. Similar issues are raised by other initiatives (such as investments in health information technology) that might require substantial start-up costs; those costs can be difficult to recapture over the typical five- and ten-year budgetary time frames used to evaluate legislative proposals.

Demonstrating savings might also be difficult because of data limitations and other methodological concerns. Although analysis by the Congressional Budget Office found some evidence of links between tort limitations for medical malpractice cases and health care spending, the results are inconsistent and depend on the particular rela­tionships and specifications tested. One reason for the mixed results may be the difficulty of disentangling the effect of certain changes to the medical malpractice sys­tem from other factors affecting medical costs. In other cases, studies that report savings may have methodologi­cal problems that raise questions about their results or whether those findings can be applied to broader popula­tions. For example, many studies of disease management programs lack comparable treatment and control groups, making it difficult to determine whether the results reflect the impact of the programs themselves or differences between the patients who participated and the ones who did not.

Modifying Health Habits

Many people behave in ways that increase their risk for disease, disabilities, and death. They may smoke, con­sume too much alcohol, overeat, or drive without wearing a seat belt. Modifying those habits or replacing them with other, better habits (such as exercising and following a nutritious diet) could improve their health and extend their life span. Some researchers and policymakers have suggested that reducing the prevalence of risky behav­ior—through public awareness campaigns, financial incentives, or regulations—could also help restrain the growth in health care spending.

Achieving substantial savings in health care spending or federal outlays from such initiatives, however, presents several challenges. First, behavior modification may take years of costly intervention and a combination of approaches to succeed. Second, even if initiatives change people’s behavior, the resulting health benefits may take a long time to emerge—so the immediate impact on health spending may be limited. Third, the long-term savings on health care from reductions in the incidence of illnesses and disabilities may be substantial, but any savings to the federal government could be at least partially offset by additional expenditures as healthier individuals live lon­ger; for example, Medicare costs could rise for the treat­ment of other diseases and conditions during those extra years of life, and expenditures for programs that are not directly related to health (such as Social Security) could also increase as life spans are extended.

Trends in Obesity and Smoking

Among the health habits that are associated with higher morbidity and mortality in the United States, smoking and obesity are the most prevalent.¹ Each is also associ­ated with higher-than-average use of health care services. In a 2008 report, CBO found that health care spending per person among the obese was 34 percent higher than spending by otherwise similar individuals of normal weight.² Among those with especially high rates of obe­sity (who are classified as "morbidly obese"), health care spending was 70 percent higher.³ Another study found that average health care spending per person was 21 per­cent higher among current or past smokers than among people with similar characteristics who never smoked.⁴

Recent trends in obesity and smoking reveal the chal­lenges and opportunities that policymakers will face if they mount new efforts to reduce the prevalence of poor health habits. Obesity rates among adults have more than doubled over the past 40 years (see Figure 7-1).⁵ In addi­tion, the share of children ages 6 to 11 who are over­weight has quadrupled, climbing from about 4 percent to approximately 19 percent.⁶ Overweight children have an increased likelihood of becoming obese as adults, and they are at risk for health conditions that were once con­sidered exclusively adult illnesses, such as Type 2 diabetes, high blood pressure, and high cholesterol.⁷ Conversely, smoking rates have fallen by roughly half over the past 40 years, at least partly as a result of policy initiatives. Even so, approximately one-fifth of adults and one-quarter of high school students continue to smoke.

Figure 7-1. 

Trends in Smoking and Obesity

(Share of adult U.S. population)

Source: Congressional Budget Office based on data from Department of Health and Human Services, National Center for Health Statistics, Health, United States, 2007 (Hyattsville, Md., 2007).

Note: Estimates of the prevalence of smoking and obesity are adjusted for age. Estimates of obesity and overweight children reflect multiyear averages.

Those trends could have far-reaching implications. Although average life expectancy has been steadily increasing in the United States over the past several decades, there has been a growing disparity in life expec­tancy between individuals with high and low income and those with more and less education.⁸ Smoking, obesity, and unhealthy lifestyles may contribute to that disparity. One study estimates that differential trends in smoking-related diseases explain at least 20 percent of the increas­ing gap in life expectancy between groups with different levels of education.⁹ The nationwide increase in obesity began among the less educated and could now explain part of the widening socioeconomic gap in mortality rates.¹⁰

Evidence About the Effects of Policies on Health Habits

Proposals to modify the health care system might seek to encourage healthier lifestyles through public awareness campaigns, financial incentives, or regulations. Much of the evidence about the impact of different approaches for changing people’s behavior comes from policies that the U.S. government adopted to discourage tobacco use. Although single approaches might work for some people, reductions in tobacco use were most likely a result of the combined impact of various interventions.¹¹ Similar con­clusions could be reached about strategies to prevent or reduce obesity. The timing and targeting of the initia­tives—in particular, whether they are aimed at adults or children—could also play a role in determining their impact.

Information Campaigns. Some proposals would try to modify people’s behavior by providing them with more information about the risks of that behavior. Examples of that approach include requirements to place warnings on products known to have adverse effects on health or man­dates for restaurants to provide caloric and nutrition con­tent for entrees on menus. Other proposals would restrict advertising and other promotions of products associated with behavior that increases health risks.

Perhaps the most prominent example of an information campaign involved smoking. The public release of the Surgeon General’s report on smoking in 1964 drew much attention to the causal relationship between smoking and lung cancer, as well as possible links between smoking and other diseases (including emphysema and cardiovas­cular diseases). As a result of that report, the government implemented policies to limit tobacco advertising on tele­vision and radio and require warning labels on cigarette packages. Those policies appear to have measurably affected the population’s beliefs about the risks associated with smoking.

The evidence is mixed, however, regarding the degree to which individuals change their behavior in response to the dissemination of information about health risks. A 2007 study found that individuals’ beliefs regarding the health risks associated with tobacco affect their decisions to begin or quit smoking; in fact, smokers appear to over­estimate, rather than underestimate, the health and mor­tality risks from smoking.¹² Another study conducted in the early 1990s, however, found that a comprehensive community-based information campaign had no measur­able impact on the overall prevalence of smoking or the share of heavy smokers who quit smoking.¹³ Additional research from social psychology and behavioral econom­ics on social norms—which generally finds that people’s actions are based in part on their perceptions of how others might behave in similar circumstances—may pro­vide insight into designing more effective information campaigns.

Financial Incentives. Some proposals would penalize people for behavior that is associated with health risks or reward people who adopt healthier lifestyles. Excise taxes imposed by the federal government (and many states as well) on tobacco and alcohol products could be increased, for example, or new taxes could be applied to items linked to potential health problems (such as sugar-sweetened beverages). Alternatively, eligibility for subsidies—through tax benefits or other means—could be expanded to include the costs of counseling and pharmaceutical therapies for smoking cessation or other clinical interventions that promote healthy behavior.¹⁴

Significant evidence shows that cigarette taxes reduce smoking. Studies have found that a 10 percent increase in the price of cigarettes decreases consumption among adults by between 4 percent and 6 percent (the effect may be larger for teenagers). Other studies have found that significant increases in taxes on unhealthy foods can diminish the consumption of those foods, lessen the prev­alence of obesity, and reduce mortality rates.¹⁵ Studies have also found that counseling and clinical interventions —including those with financial incentives—can be somewhat effective in changing people’s behavior in the short term.¹⁶ Most studies, however, do not show sus­tained changes in behavior, either because they do not test for it or because the interventions are not effective over longer periods.

Regulation. Some proposals would expand the regulatory functions of the government to restrict access to unhealthy products or to expand the availability of counseling or other clinical interventions aimed at encouraging healthy behavior. The federal government, for example, requires that schools serve meals that meet nutrition guidelines in return for receiving lunch subsidies. At the state and local level, governments have banned the sales of soft drinks in schools and the use of trans fats in restaurants and bakeries. Research shows that bans on smoking in the workplace are effective. A study from the 1990s found that such bans decrease smoking prevalence by 5 percentage points and average daily consumption of cigarettes by 10 percent.¹⁷ As part of a broader legislative package, proposals that required health insurers to provide mandated benefits might also include clinical counseling and pharmaceutical therapies among those benefits.

Research from the field of behavioral economics may offer new insights about how to develop more effective regulatory strategies to reduce obesity. A recent review of the literature on behavioral economics and social psychol­ogy concluded that findings from those fields could help policymakers and analysts better understand how people make food choices.¹⁸ For example, making healthy foods the default option in federal nutrition programs would, according to the study’s authors, raise the perceived value of those foods. Many people have problems of self-control when choosing food, possibly because they shop for food when they are hungry or they place great value on immediate gratification. Allowing people to make food choices in advance—by letting students preselect menu options in the federal nutrition programs, for instance, or giving recipients of the Supplemental Nutri­tion Assistance Program (SNAP, formerly known as the Food Stamp program) the option to preorder groceries by telephone—might spur them to make healthier choices. Also, paying SNAP benefits more often than once a month might prevent food hoarding and bingeing among recipients who place a greater value on consumption today than on deferred gratification. The authors did not estimate the potential savings for those interventions and concluded that more research is needed to evaluate the costs and benefits of those types of strategies for reducing obesity.

Timing and Targeting of Interventions. Another factor that may affect the success of proposals to change people’s health habits is the timing and targeting of interventions. Some experts suggest that policies that focus on prevent­ing the onset of risky behavior might offer greater gains than those that attempt to change long-established hab­its. One justification for a focus on early intervention is the possibility that certain critical developmental stages during childhood play a disproportionate role in determining whether people become obese later in life.¹⁹Another justification for focusing on prevention is the relative lack of success with weight loss interventions among adults. Few successful interventions have been found for a general population over an extended time despite significant commercial investment in those weight loss programs.²⁰ Most participants in weight loss programs either quit the program or regain the weight afterward. At the same time—and notwithstanding the arguments for earlier intervention—research on attempts to reduce obesity among children generally has not pro­duced promising results either.²¹

Impact of Proposals on Health Care Spending

The impact that proposals to modify people’s behavior would have on health care spending depends on several factors in addition to their effects on health habits. Even if the proposals were effective in causing people to adopt healthier lifestyles, the effects on health and spending might not materialize for many years. Furthermore, proposals that focus on changing people’s behavior might have a limited effect on their health. Although individu­als who discontinued harmful habits would generally become healthier, they would probably not be as healthy as people who had never engaged in that behavior. In addition, proposals that seek to prevent the onset of unhealthy behavior in childhood, while having the great­est potential impact on health over the long term, would be very unlikely to have a substantial effect on health care spending over the five- and ten-year time frames typically used in budgetary analysis.

To the extent that the proposals successfully reduced risky behavior, however, long-term savings might be offset by other expenditures or budgetary effects. Reductions in smoking would decrease federal and state revenues from excise taxes on cigarettes, for instance. Many of the approaches to altering behavior—such as offering financial rewards to people who undergo weight loss or smoking cessation counseling—would have short-term direct costs that must be balanced against any savings from future health improvements. Behavioral interven­tions that are successful in improving health may extend people’s life span and the quality of their life, but that impact could cause federal expenditures for retirement benefits and Medicare to increase in the longer term.

Notwithstanding those important caveats, CBO would consider persuasive evidence of budgetary savings in ana­lyzing the effects of various proposals. For example, CBO has estimated that proposed regulation of tobacco prod­ucts by the Food and Drug Administration would lessen the number of women on Medicaid who smoke during pregnancy; in turn, that outcome would yield modest program savings from reduced complications during pregnancy and a smaller likelihood of having babies with low birth weights. (Children with low birth weights have higher medical costs, particularly at birth, but also later in life.) The substantial difference in health care spending for obese and normal-weight individuals also suggests a potential for savings; the primary challenge appears to be identifying strategies that can effectively reduce rates of obesity.

Expanding the Use of Clinical Preventive Services

Clinical preventive services are delivered to patients in a medical setting—that is, by a doctor or other health care practitioner. Those services include immunizations and other interventions that prevent diseases from arising (known as primary prevention) and screening tests that can determine the presence of a disease before symptoms appear (known as secondary prevention).²²

To the extent that clinical preventive services avert diseases or lead to their early treatment, they have the potential to reduce health care costs as well as improve the quality and length of patients’ lives. For that reason, some proposals would seek to expand the federal govern­ment’s role in encouraging the use of preventive services. However, the impact of specific preventive services on health care spending varies, depending on the disease being targeted and the population that receives the ser­vice. A preventive service can be clinically effective (that is, improve health) and cost-effective (meaning that the costs of the service are low relative to its health benefits) but not result in net savings to the health care system.²³ Targeting clinical preventive services to people who would benefit from them the most would increase the chances of obtaining long-term net savings, but—for many preventive services—more research is needed to evaluate who in the population would be best served by such treatments.

Coverage and Use of Preventive Services

Federal and state governments use a combination of sub­sidies, mandates, and outreach campaigns to encourage the use of certain preventive services. When the Medicare program was implemented in the 1960s, it did not include coverage for preventive services, but coverage has since been added for a number of specific services, such as cholesterol screenings, mammograms, and colonoscopies. For most children enrolled in the Medicaid program, the federal government requires that preventive services be covered through the Early and Periodic Screening, Diag­nostic, and Treatment program. In addition, the Vaccines for Children program provides free vaccines to doctors for children who are uninsured, underinsured, or eligible for Medicaid.

Many states also require private health insurance plans to cover certain preventive services. In 2008, all states except Utah mandated coverage for breast cancer screening, and more than half required coverage for cervical cancer screening. Although private health insurance coverage provided by large employers is generally exempt from state mandates, most private insurers appear to cover immunizations and various screening tests, and almost all plans that require enrollees to pay a deductible exclude at least some preventive services from it.²⁴

The share of the population that receives recommended preventive services varies widely, depending on the pre­ventive service and the age group. In general, adherence to the recommended guidelines for childhood vaccina­tions is quite high—in 2006, the percentage of children between 19 and 35 months old who had received the rec­ommended schedule of vaccinations was close to or above 90 percent. The share of adults receiving recommended vaccinations and screenings is much lower. For example, mammography is recommended every one to two years for all older women and is covered by Medicare without being subject to the normal deductible. According to claims data, however, only about half of the women enrolled in Medicare actually received a screening mammography in 2004 or 2005.²⁵ Similarly, an influ­enza vaccination is recommended each flu season for all older individuals, but fewer than half of Medicare benefi­ciaries had a claim submitted for an influenza vaccination in 2006.

The effects on health care spending of proposals to subsi­dize or mandate coverage of preventive services would depend, in part, on whether the initiatives successfully encouraged the targeted population to use those services. Because many preventive services are already covered by both private and public health insurance, the impact of any proposal on whether an insurance plan includes a preventive service in its benefit package would tend to be modest. Moreover, providing coverage for preventive ser­vices (even at a relatively low cost to enrollees) does not ensure that those services will be used. Research in behav­ioral economics and the role of default options could yield new approaches to encourage greater use of clini­cally beneficial and cost-effective preventive services.

Effects of Preventive Services on Health Care Spending

The net effect of preventive services on health care spend­ing depends on several factors, in addition to whether people use such services. On the one hand, preventive services can lessen or eliminate the costs of treating a dis­ease by lowering the incidence of the disease or detecting it in its initial stages. On the other hand, savings from preventive services would be offset by certain costs, which could more than offset the savings from prevention or early detection. Those costs include:

■	The direct cost of the preventive service;

■	The cost of treating any adverse reactions to the pre­ventive service;

■	The cost of follow-up testing and treatment for patients with positive screening tests; and

■	The cost of treating unrelated diseases that occur because of an individual’s extended life span.

If preventive services are clinically beneficial and extend an individual’s life span, they could also increase federal spending on Medicare (for the treatment of other diseases and conditions during those extra years of life) as well as the costs of programs that are not directly related to health (such as Social Security).

Certain types of preventive services have been found to yield substantial net savings, largely because the initial costs are low and the long-term benefits are large. For example, physicians can quickly explain the benefits and harm of daily aspirin use for the prevention of cardiovas­cular events for middle-aged patients; the costs of that type of intervention are low, and the long-term health care savings are comparatively large. Similarly, medical providers can immunize children against a variety of potentially life-threatening and costly diseases in a single office visit.²⁶

For many other types of preventive services, however, the net impact on spending for health care is less certain. The influenza vaccine provides one example of a preventive service with an uncertain impact on spending. The costs of respiratory disease among the elderly are high, the direct cost of the vaccine is modest (Medicare pays physi­cians roughly $25 to $40 per vaccination), and serious adverse reactions are very rare. Because the vaccine reduces the incidence of influenza and related respiratory diseases, it lowers the costs of treating them. The vaccine also lessens the risk that recipients will transmit the dis­ease to others, a concern not only for influenza but also for other communicable diseases. Those considerations have led the Advisory Committee on Immunization Prac­tices (a panel of experts established by the federal Depart­ment of Health and Human Services, or HHS) to recom­mend annual influenza vaccinations for certain segments of the population, including adults age 50 or older.

That recommendation primarily reflects the efficacy of the vaccine; however, the evidence regarding its impact on health spending is mixed. Although some studies found that the costs of providing influenza vaccinations for the elderly were more than offset by savings from avoiding illnesses, those studies typically lacked compara­ble treatment and control groups, making it difficult to determine whether the results reflect the impact of the vaccine itself or differences between the patients who were vaccinated and the ones who were not.²⁷ A 2006 study, guided by the National Commission on Prevention Priorities, concluded that the influenza vaccination for older adults—although highly cost-effective—does not reduce net health care spending.²⁸ Viewed from a broader perspective, achieving near-universal vaccination for influenza among the elderly could improve the length and quality of people’s lives but nevertheless, on net, increase federal spending over the long term. Because the influenza vaccine appears to reduce mortality among the elderly, it increases both Medicare’s costs and Social Security spending. Those additional costs could—if sufficiently large—more than offset the substantial savings from reduced treatment of influenza and related conditions within conventional budgetary time frames.

The prostate specific antigen (PSA) test for prostate can­cer illustrates the potential for preventive services to have some adverse effects, both clinically and economically. The direct cost of a PSA blood test is modest. The bulk of the costs associated with PSA testing arise from the follow-up testing and treatment provided to patients with a positive result (that is, one that indicates possible cancer). More than 10 percent of previously unscreened men in their sixties who receive the PSA test have a posi­tive result that would generally result in a recommenda­tion for a follow-up biopsy. Among patients who receive follow-up biopsies, about 70 percent do not have prostate cancer—indicating that the PSA test generates many "false positive" results. Moreover, only a small minority of patients have a malignant condition that, if left untreated, would progress to the point of causing clini­cally significant symptoms. Therefore, researchers have expressed concern that prostate cancer is being "over-diagnosed" and that the aggressive treatment of prostate cancer can significantly reduce a patient’s quality of life. In response to such concerns, the U.S. Preventive Services Task Force (another HHS panel) recently recommended that doctors stop screening men ages 75 and older for prostate cancer.

More generally, the challenge that arises in obtaining sav­ings from the use of preventive care is that vaccinations and screening tests are typically given to a large number of people—only a fraction of whom would have the dis­ease in question otherwise. Furthermore, the savings for that subgroup have to be large enough to offset the over­all costs of the preventive services. To assess the extent of those competing considerations, researchers affiliated with the Tufts Medical Center recently reviewed hundreds of clinical studies on the health and economic effects of preventive services.²⁹ (The studies did not encompass all services, just those that had been rigorously evaluated.) The researchers found that only about 20 per­cent of the preventive services that had been assessed yielded savings. (For about 3 percent of the preventive services studied, the findings indicated that the interven­tion worsened health and increased costs. The remainder improved health but caused net spending to rise.)

In many other cases, the clinical and economic implica­tions of preventive services are not well understood. The U.S. Preventive Services Task Force publishes recommen­dations based on its reviews of clinical evidence. In its 2006 guide, the task force neither recommended for or against approximately 40 percent of the preventive ser­vices it reviewed, because of a lack of clinical evidence. The federal government could expand the clinical evi­dence base and resolve at least some of those areas of uncertainty by funding and setting priorities for clinical research. Even if that happened, however, the impact on health costs would be highly uncertain and would depend on whether research findings tended to support the use of preventive services that would also result in an increase in total health spending or those that would decrease such spending.

Establishing a "Medical Home"

The concept of establishing a "medical home" has been promoted as a means to improve the coordination and quality of medical care and to give patients greater access to care. According to its proponents, medical homes would give patients ready access to a primary care pro­vider who coordinates services across settings (specialists’ offices, hospitals, and laboratories) and across types of care (acute, chronic, and preventive). The American Academy of Family Physicians describes the medical home concept as follows:

At its core [the medical home] is an ongoing partnership between each person and a specially trained primary care physician. This new model provides modern conveniences, like e-mail communication and same-day appointments; quality ratings and pricing information; and secure online tools to help consumers manage their health information, review the latest medical findings and make informed decisions. Consumers receive reminders about necessary appointments and screenings, as well as other support to help them and their families manage chronic conditions such as diabetes or heart dis­ease. The primary care physician helps each person assemble a team when he or she needs specialists and other health care providers such as nutritionists and physical trainers.³⁰

The concept of a medical home combines several elements that would involve changes in current medical practice, including greater use of health information tech­nology, disease management tools, and preventive services (all of which are discussed in this chapter). It also relies on an adequate supply of primary care physicians. (See Chapter 5 for further discussion of issues relating to the supply of physicians.) But the medical home concept has several elements that are not discussed elsewhere in this report:

■	Assignment of patients to a primary care provider who may play a "gatekeeper" role (authorizing and manag­ing referrals to specialists);

■	Options to contact a primary care provider through means other than office visits (by e-mail or telephone, for instance); and

■	Access to a primary care provider or a trained triage nurse outside of normal office hours.

Improving coordination in health care could provide patients with better care and might reduce their anxiety and confusion as they seek assistance from a broad assort­ment of medical professionals. Some proponents also believe that improvements in coordination would yield health care savings, and they have suggested that the fed­eral government encourage more health plans to adopt medical homes (by requiring that subsidies for health insurance be used for plans that provide a medical home, for example, or by making new Medicare or Medicaid payments to physician practices qualifying as medical homes). The impact of medical homes on health care spending remains unclear, however. Medical homes could lead to increases in health care spending if patients responded by seeking more services—or if payments to primary care physicians were simply an add-on to current outlays. The available evidence suggests that medical homes would be most likely to reduce health care spend­ing if the coordinating physician also had a financial incentive to limit the use of specialty care.

Summary of Evidence

Under one model of the medical home concept, the pri­mary care provider would coordinate referrals for spe­cialty care and be paid a capitated monthly amount for that service. The American Medical Association recently estimated the additional time and equipment required for physicians and nurses to provide the coordination of care, tracking of patients, and outreach involved in the medical home model.³¹ Its estimates suggest that the annual monetary costs of providing a medical home (above and beyond standard primary care services) would be about $650 per Medicare beneficiary in 2009—an increase of more than 20 percent over the current amount of Medi­care spending per beneficiary for physicians’ services.

Other features of the medical home concept could also cause spending to rise, although some of the increases could be at least partially offset by savings. Improving patients’ access to primary care providers through extended hours on evenings and weekends, "24/7" telephone triage, or "open access" scheduling (that is, same- or next-day appointments) would tend to increase use and spending. Those initiatives might result in some offsetting reductions in the use of specialist services or emergency room care, but the savings would probably be small unless proposals provided substantial financial incentives for care coordination. Paying primary care providers for responding to patients’ telephone calls or e-mails could also cause health care spending to increase, but those costs could be offset either partially or fully by savings if visits to the doctor’s office or emergency room declined as a consequence. (In addition, patients might benefit by avoiding the time and travel costs associated with in-office and emergency room visits.)

Improving the coordination of care could, in principle, reduce health care spending. Determining the precise impact of care coordination is challenging, though, espe­cially when it is implemented along with other changes in care processes. One Medicaid pilot project that uses the medical home model—Community Care of North Carolina—has reported savings among beneficiaries with asthma and also among those with diabetes. However, it is difficult to determine whether those savings resulted from the gatekeeper element of the medical home model or from other key elements of that initiative, which include disease management, development of clinical guidelines, and patients’ education.³²

The effect of gatekeeper arrangements on the use of spe­cialist care by patients depends critically on the health care delivery system and payment environment. In the context of fee-for-service payments to physicians operat­ing independently, a gatekeeper arrangement does not appear to decrease referrals to specialists and may increase the number of visits to primary care providers by patients seeking referrals. Alternatively, approaches in which pri­mary care providers play more of a "gateway" role than a "gatekeeper" role (by streamlining the referral process, employing referral coordinators who are not physicians, and offering telephone-based referrals) may limit the increase in payments for primary care but facilitate larger increases in specialty care.

The experience of Harvard Vanguard Medical Associates (HVMA), a Boston-based staff-model health mainte­nance organization, illustrates the key role of payment incentives. In 1998, that plan eliminated its requirement that patients receive a referral from a primary care pro­vider before seeing a specialist.³³ Afterward, the number of specialist visits per member was unchanged, indicating that gatekeeping made little difference in the use of specialty care in that setting. One possible reason that elimination of the gatekeeping requirement had no impact on the number of visits to specialists was that primary care and most specialty care in HVMA was pro­vided by a group practice of salaried physicians, so the elimination of gatekeeping did not create new incentives for them to increase or decrease the provision of care.

In contrast, there is evidence that gatekeeping arrange­ments can reduce the use of specialists in a fee-for-service setting when coupled with clear financial incentives for primary care providers. One older study describes an experiment conducted in 1979 in which enrollees in United Healthcare were randomly assigned to a plan either with or without a gatekeeper.³⁴ In the gatekeeper plan, patients were assigned to primary care providers who also received bonuses or "withholds" (reductions in payments) based on their patients’ use of specialists’ care, hospital care, prescription drugs, and other ancillary ser­vices. The number of visits to specialists in the gatekeeper plan was one-third lower than the number of visits in the plan without gatekeepers.

Factors Affecting the Adoption of the Medical Home Model

Proposals that make federal subsidies for health insurance to plans contingent on their use of medical homes could accelerate the adoption of that model, but the impact on health care spending would depend on several factors. Proposals that simply codified the definition of a medical home would probably have only very minor implications for medical practice. Likewise, proposals that encouraged plans to adopt a gatekeeper approach might have a lim­ited impact if that change was not combined with finan­cial incentives for primary care providers. Furthermore, to the extent that the medical home model improved care or reduced spending, it would most likely be adopted over time in a competitive insurance market regardless of whether it was a precondition for receiving federal subsi­dies. Thus, the incremental effect on health care spending of a proposal to link subsidies for insurance to a plan’s adoption of the medical home model could be small.

At the same time, nationwide implementation of the medical home concept could be affected by constraints on both labor and capital resources. An inadequate sup­ply of primary care providers and a lack of multispecialty group practices in many regions of the country could slow the spread of the medical home model. The ability of primary care physicians to coordinate care across set­tings could also be severely hampered by a lack of com­patible information technology systems.

Adopting Disease Management Programs

Disease management programs can help patients manage the routine care of common chronic diseases, such as dia­betes or coronary artery disease. Those programs could improve the quality of health care. Furthermore, some observers contend that they would help control costs. Interest in disease management programs is partly moti­vated by two concerns: the large share of health care costs attributable to chronic diseases; and potential problems with coordination and continuity of care in the current fee-for-service delivery system. To date, however, evi­dence about cost reductions in the private sector from plans that have implemented such programs has been inconclusive, and programs tested in the Medicare population have not shown cost reductions either. Nevertheless, CBO will continue to analyze new evidence about such programs as it becomes available.

Background

Although much of health care could be considered dis­ease management in one form or another, structured pro­grams to manage diseases typically incorporate some or all of the following elements: education of patients about their disease and how to treat it; monitoring of patients’ symptoms and their adherence to evidence-based treat­ment plans; and efforts to coordinate care across provid­ers and settings (after a hospital discharge, for instance) or to provide support and feedback to a patient’s primary care physician. Although similar in some respects to med­ical homes, disease management programs are usually run by health plans or by outside vendors who specialize in those services (sometimes in collaboration with a patient’s primary care physician).

Disease management programs are typically designed to address a specific chronic disease, although a vendor may provide a range of programs covering several diseases. The programs also vary in their target populations and the intensity of the services they provide; some programs seek to serve all enrollees with a given health condition (known as a population-based approach) but involve less interaction with each patient, whereas others target higher-risk cases (such as those patients deemed likely to require a hospital admission) and use more aggressive and more expensive interventions. Although they often have some components in common, specific interventions tar­geting the same disease may vary widely in their design.

Formal disease management programs are a relatively new phenomenon, but their use in the private health insur­ance market has grown substantially. According to indus­try surveys, nearly all large health plans offer some type of disease management service, and 83 percent of 500 major employers use such programs.³⁵ (Widespread adoption is sometimes seen as evidence that the programs reduce costs—and some insurers report such effects—but another possibility is that the programs generate health or other benefits that warrant a net increase in spending.) Such services are generally not available in the fee-for-service Medicare program, but various approaches for serving that population have been studied in demonstra­tion projects. In addition, many states are experimenting with disease management programs for Medicaid benefi­ciaries, expanding the share of the population with access to those programs.

Summary of Evidence

In a 2004 letter, CBO reviewed a number of evaluations of disease management programs that had been published in peer-reviewed journals, focusing primarily on pro­grams for diabetes, coronary artery disease, and conges­tive heart failure.³⁶ Some studies found net cost reduc­tions for selected groups of patients, but it was not clear whether those targeting strategies could be replicated or if the results would hold up when applied to broader sets of patients. Overall, CBO found that the evidence was insufficient to conclude that disease management pro­grams generally reduce health care spending, once the costs of the programs themselves are included in the analysis.

A more recent review of the literature by analysts at RAND examined studies of programs that encompassed a broader set of diseases. That analysis reached a similar conclusion: The evidence about cost savings is inconclu­sive.³⁷ Programs for congestive heart failure were gener­ally successful in reducing hospital admission rates but not by enough to show clear savings net of the programs’ costs. Programs for patients with depression improved the care that those patients received but also were found to increase health care costs. Programs for coronary artery disease and diabetes were found to increase adherence to evidence-based treatment guidelines and to improve some intermediate measures of patients’ conditions (such as hemoglobin levels for diabetics, which measure control of blood sugar); still, evidence about improved long-term clinical outcomes or net savings was inconclusive.

Questions about the effectiveness of disease management programs for the Medicare population have led to demonstration and pilot projects designed to test those approaches. In general, those projects have not found cost reductions from disease management. One possible reason is the monthly fees paid to disease management vendors, which ranged from roughly $75 to $160 per enrollee in one of the Medicare demonstration projects. In some cases, there were no cost reductions, even before taking into account the payments made to the vendors. Offsetting those fees would require large reductions in health care spending, which have not yet been demon­strated.³⁸

Challenges in Demonstrating Savings

Studies analyzing disease management programs may fail to demonstrate conclusive cost reductions for several rea­sons. The challenges that are involved in reducing health care costs vary somewhat with the type of program being used. A population-based approach that seeks to serve all enrollees with a given health condition may provide ser­vices to many enrollees who either are not sick enough to benefit (or are unlikely to generate high costs) or are too sick to benefit. Predicting which enrollees are most likely to benefit can also be difficult, however. Alternatively, waiting for enrollees to develop a more serious condition (or for an "index" event, like a hospitalization, to occur) could improve accuracy but might miss opportunities for savings. In the Medicare population, an additional chal­lenge is that many patients have multiple chronic condi­tions. That complexity makes it more difficult to identify and treat those patients in a timely manner.

In addition, a number of methodological limitations may account for the studies’ failure to demonstrate cost reductions. Many studies focus on disease management programs’ effects on processes of care or intermediate outcomes instead of their effects on spending, so deter­mining whether those changes will reduce health care costs is difficult. And some of the studies that do analyze effects on spending do not account for the cost of provid­ing the disease management program itself. Furthermore, participation in disease management programs is volun­tary in many cases—a consideration that raises the poten­tial for selection bias. If participants in a study are health­ier than nonparticipants or take a more active role in their care, then comparisons of costs for the two groups may be misleading. Conversely, if study participants were chosen on the basis of having particularly high costs in a previous period, their costs would be expected to fall regardless of whether they participated in a disease management pro­gram (following a statistical phenomenon known as regression to the mean). Finally, it may be difficult to apply the results from a demonstration project to a broader population or another setting. For example, pro­grams undertaken by health maintenance organizations may not achieve the same results if used in a more loosely managed health plan.

For those reasons, the most reliable means of assessing disease management programs is to construct randomly assigned treatment and control groups and compare com­prehensive measures of their spending. Such methods have been used effectively in the demonstration and pilot projects undertaken for Medicare, but they are much less common in assessments of programs in private-sector health plans. Some studies may seek to exploit differences in the timing or locations of program rollouts to identify their effects, but even then questions about their method­ology may arise.³⁹ As the RAND review noted, those "vendor-run assessments typically do not meet the requirements of peer-reviewed research in terms of the comparison strategy, and adequate control for selection bias and regression to the mean."

Overall, CBO’s assessment is that proposals requiring pri­vate health insurance plans to adopt disease management programs would be unlikely to yield lower premiums. One reason is that private plans have largely embraced disease management already, so the incremental effect of a proposal on adoption rates is likely to be small. More­over, if new evidence emerged about particular programs that conclusively demonstrated net reductions in health care costs, private health plans would probably adopt those programs even in the absence of a require­ment—both to limit their own costs and to remain com­petitive in the insurance market. In that case, the effects of a legislative proposal would be limited to people who would not have access to the disease management pro­gram under current law.

Adoption in the fee-for-service Medicare program, by contrast, would probably require changes in legislation. Although evidence of savings in that setting is lacking, certain types of private-sector programs—particularly those that have been evaluated using a rigorous evidence base and demonstrated either partial offsets to spending or actual net savings—would have a greater potential to limit federal spending. However, a key consideration that remains is whether the findings of such studies can be replicated in Medicare. Programs that can be targeted more effectively toward the Medicare enrollees most likely to benefit from them or most likely to generate sav­ings may also be more likely to reduce federal spending on health care. Finally, programs in which the organiza­tion providing the disease management services has a stronger financial stake in the outcome also seem more likely to limit federal spending; for example, Medicare payments to vendors could be tied to a comparison of spending between enrollees in the program and a refer­ence group (as was done in some of the recent Medicare demonstration projects). Even then, calculating what enrollees’ costs would have been in the absence of disease management programs is a challenge, especially as those programs move from the demonstration phase to broader application.

Comparing the Effectiveness of Medical Treatments

Concerns about the limited evidence that is available to determine which treatments are most effective for which patients has led to proposals that would seek to expand the supply and use of information that compares the effectiveness of treatment options.⁴⁰ Some proposals would fund a government agency or other entity to con­duct additional research in that area; CBO estimates that such research would yield findings that reduce federal spending for certain types of health care, although not by enough to offset the costs of conducting that research over a 10-year budgetary time frame. Over the longer term, the federal costs and savings might be in rough balance. Other options would change payment rules or cost-sharing requirements under Medicare or Medicaid to encourage the use of more clinically effective or cost-effective services or discourage the use of other services. However, the extent to which savings could be realized from such approaches would depend greatly on the details of the proposal.

Availability of Evidence

The term comparative effectiveness refers to a rigorous evaluation of two or more alternatives available for treat­ing a given medical condition for a particular set of patients. The most rigorous type of evaluation is a clinical trial in which patients are randomly assigned to various treatment options. For example, one study from 2007 randomly assigned patients with stable coronary artery disease into one of two treatment protocols: an angio­plasty with a metal stent combined with a drug regimen; or the drug regimen by itself. That study found no differ­ences between the two groups of patients in survival rates or the occurrence of heart attacks over a five-year period. Angioplasty did, however, appear to reduce the preva­lence of angina (chest pain).⁴¹ Such results are not uncommon, and they do not definitively prove that a medical procedure (angioplasty, in this case) lacks value; instead, they show that, for a specific group of patients, the procedure has value in relieving symptoms but not in improving rates of survival or avoiding major complications.

Although clinical trials generally yield the most persuasive results, they are also the most expensive and time-consuming type of comparative analysis. Other approaches to research on comparative effectiveness include the use of medical claims data or systematic reviews of the available evidence on individual treatments in order to construct head-to-head comparisons. Such approaches are less costly, but using observational data to draw inferences about effectiveness is difficult. Because patients are not randomly assigned to different treat­ments, researchers may not be able to separate out the true effect of the treatment from other factors that might have led the individual or medical provider to select that approach. (For example, more intensive treatments might have been given to sicker patients.) Whatever data they use, such studies may limit their focus to clinical effec­tiveness or may also consider cost-effectiveness, weighing the additional costs of a more expensive procedure against any additional benefits it might provide.

Some information about the effectiveness of new drugs, medical devices, or procedures is available, but rigorous comparisons of different treatment options are less com­mon. Even though drugs and devices must be certified as safe and effective before they can be marketed, the regula­tory process for approving those products does not evalu­ate them relative to alternatives. (There are a few limited exceptions.) In addition, some evaluations have found that clinical trials sponsored by drug and device makers are more likely than independent studies to find favorable results. For example, a 2006 study of antipsychotic drugs found that in 90 percent of the firm-sponsored trials, the results favored the drug made by the sponsoring firm; that outcome led to conflicting results across studies when the findings of the same drugs from different spon­sors were compared.⁴² In addition, medical proce­dures—which account for a much larger share of total health care spending—can achieve widespread use with­out a systematic analysis of their impact. Estimates about the current situation vary widely, but some experts believe that less than half of all medical care is based on or sup­ported by adequate evidence about its effectiveness.⁴³

Factors constraining the supply of and demand for such research explain why the current evidence base is so lim­ited. Although private insurers sponsor some research assessing and comparing treatments (because they have incentives to restrict coverage of ineffective care), the pri­vate sector, in general, will not produce as much of that research as society would value. The knowledge created by such studies is expensive to produce, and private insur­ers (and other organizations conducting such research) may capture only a portion of the resulting benefits. Because the knowledge can be disseminated at essentially no additional cost (and charging all users for access to that information is not always feasible), all parties end up benefiting from it. Private insurers, therefore, do not invest as much in those efforts as they would if they alone benefited from the knowledge or if they took into account the benefits to all parties. For its part, the Medi­care program lacks clear legal authority to take costs into account in determining which services are covered and has made only limited use of the available data on relative clinical effectiveness; consequently, its demand for com­parative assessments has been minimal.

Limited data on the comparative effectiveness of different medical approaches may help explain why the use of cer­tain treatments and the types of care provided vary widely from one area of the country to another. Researchers at Dartmouth, for example, found about a fourfold varia­tion in the share of Medicare beneficiaries receiving a cor­onary artery bypass graft in different regions of the coun­try, and those differences were not correlated with rates of heart attacks in each region. In part, that variation may reflect differing views among doctors about the effective­ness of bypass surgery. Furthermore, some evidence sug­gests that the degree of geographic variation in treatment patterns is greater when less evidence is available about the best treatment to use. Surgery rates for joint replace­ments provide one example. There is relatively little geo­graphic variation in admission rates for Medicare benefi­ciaries who have fractured a hip, a condition that requires hospitalization. For knee and hip replacements, however, more discretion is involved, and the surgery rates vary more widely. For back surgery (the benefits of which are often in dispute), geographic variation in rates is even greater.

Effects on Health Care Spending

To generate additional information on comparative effectiveness, some proposals would fund new research through a government agency or other entity. Other options would link both new and existing evidence to payment rules or cost-sharing requirements under Medicare or Medicaid so as to provide incentives for using more clinically effective or cost-effective services or to discourage the use of other services.

Generate Additional Information. Proposals generally specify how much research funding they would provide, so the main question that arises in determining their overall impact on the budget is whether that research would affect spending by federal health insurance pro­grams. Predicting the impact that additional information about comparative effectiveness could have on health care spending is difficult because it is hard to know what that research will show. As a general rule, however, the fee-for-service payment system by which most health care in the United States is currently financed often provides finan­cial incentives for doctors and hospitals to adopt new and more expensive treatments and procedures even if hard evidence about their effectiveness is not available. Fur­thermore, some analyses have found that clinical trials sponsored by drug and device makers are more likely than independent studies to find favorable results. Over the long term, therefore, generating additional objective information about the relative costs and benefits of treat­ments seems likely to offset those tendencies some­what—and is thus more likely to reduce total health care spending than to raise it. Under current coverage and payment rules for Medicare and Medicaid, the resulting changes in medical practice (spurred in some cases by pri­vate insurers) would reduce spending under federal pro­grams because doctors tend to treat their patients in a similar manner regardless of their source of insurance.

In some instances, comparative effectiveness research has already led to changes in patterns of medical practice, causing doctors and patients to pursue less invasive and less costly treatments. One example concerns patients with emphysema. A study initiated by Medicare exam­ined the effects of surgery to reduce lung volume for patients with that condition. Although the study found that the procedure had medical benefits for some types of patients (and Medicare continued to provide coverage in those cases), the additional information about the treat­ment’s risks apparently discouraged many doctors and patients from pursuing that option, and its use dropped as a result.⁴⁴ Similarly, the study of angioplasty cited ear­lier appears to have contributed to a decline in the use of stents.

Although new research into comparative effectiveness might lead to net cost savings over a long period of time, its effects during the conventional 10-year horizon for budgetary estimates would be limited. In addition to the time required to get the new activities under way, a lag would exist before results were generated—particularly if they depended on new clinical trials. Initially, the avail­able results would probably address a relatively small number of medical treatments and procedures; additional time would have to elapse before a substantial body of results was amassed. For all of those reasons, it would probably take several years before new research on com­parative effectiveness could reduce health care spending substantially. CBO has estimated that such approaches could eventually yield federal savings on health care that roughly equal the outlays for research, but the savings would not be large enough to offset the costs of the research within a 10-year budgetary time frame.

Some features of proposals to fund additional research on comparative effectiveness would affect their likely bud­getary impact:

■	Higher funding levels would tend to generate more studies and thus would yield greater savings (although the incremental effects would eventually decline because the capacity to conduct high-quality research in this area is not unlimited).

■	Savings may be more likely to result from a research agenda that explicitly prioritizes assessments of costly technologies that are suspected of being overused.

■

Assessing cost-effectiveness as well as clinical effective­ness would yield a somewhat larger effect on health care spending than would research focused only on clinical effectiveness—because it would help highlight cases in which the additional benefits of a more costly treatment are relatively small.

■	Efforts to bolster comparative effectiveness research would be more likely to change medical practice pat­terns if the organization coordinating the research was respected and trusted by doctors and other profession­als in the health care sector.

Other features regarding the organization and funding system for the new research—for example, whether to fund an existing government agency or create a new public/private partnership—would not affect the esti­mated budgetary impact of the research.

Provide Incentives for Implementation of Research Findings. Merely conducting comparative effectiveness research is unlikely to have major effects on clinical practice patterns. For the research to have a much larger impact, providers’ financial incentives would need to be realigned accordingly. If changes in law were made, Medi­care could use information about comparative effective­ness to promote the use of more effective care. The pro­gram could, for example, choose not to cover treatments that were found to be less clinically effective or less cost-effective. Alternatively, Medicare could tie its payments to providers to the cost of the most clinically effective or most cost-effective treatment, or enrollees could be required to pay for at least a portion of the additional costs of less clinically effective or less cost-effective proce­dures. To the extent that such approaches reduced the use of less effective services or shifted care to less expensive treatments, the potential impact on Medicare spending could be substantial. Similar approaches could be applied in the Medicaid program, although additional issues of coordination would arise because states generally set pay­ment rates and coverage rules (subject to broad federal requirements) and jointly finance the program. Although such proposals could reduce federal spending in more substantial ways than would result from added research alone, the extent to which savings could be realized would depend greatly on the details of the proposal.

Adopting Health Information Technology

Health information technology (IT) could significantly increase the efficiency of the health care sector by helping providers manage information.⁴⁵ It could also improve the quality of health care and, ultimately, the outcomes of that care for patients. In particular, electronic health records—comprising electronic documentation of pro­viders’ medical notes, electronic viewing of laboratory and radiological results, electronic prescribing of medica­tions, and an interoperable connection among providers of health care—could have a sizable impact on medical practices. When used effectively, electronic health records could reduce the duplication of diagnostic tests; remind physicians about appropriate preventive care; identify harmful drug interactions or possible allergic reactions to prescribed medications; and help physicians manage the care of patients who have complex chronic conditions.

The promise of those potential benefits has led many observers to suggest that the federal government should promote the nationwide adoption of health IT. Research indicates that, at least in certain settings, health IT facili­tates reductions in health care spending—if other steps are also taken to alter incentives so as to promote savings. By itself, the adoption of more health IT offers many benefits, but it is generally not sufficient to produce substantial cost savings because the incentives for many providers to use that technology to control costs are not strong.

Summary of Evidence on Improvements in Efficiency from Adopting Health IT

The potential of health IT to reduce spending for health care largely depends on its ability to make care more effi­cient by cutting the cost of delivering services, avoiding redundant services, and improving providers’ productiv­ity. Evidence from the literature on health IT, however, does not uniformly support the possibility of such sav­ings. The potential for savings appears to depend heavily on their source and whether that source is in a hospital or in an ambulatory care setting (such as a clinic or a physi­cian’s office). In addition, savings are difficult to assess because the trimming of costs in one area of a physician’s practice, for example, may be offset by increased costs or reduced efficiency in another area.

Estimating the impact of some potential sources of sav­ings—especially those arising from greater exchange of information among providers, insurers, and patients—is especially difficult because health IT networks are in an early stage of development. Furthermore, health care pro­viders and hospitals that were early adopters of health IT may have been motivated by particular characteristics of their organizations or operations that made them more likely than nonadopters to achieve benefits from health IT—in which case the outcomes they have seen might not apply to a broader group. Evidence of savings in the health care sector as a whole from adopting health IT is also limited.

Although the evidence of savings regarding specific applications is mixed, savings could accrue in some areas.

■	Research has shown that physicians’ offices can realize savings from reducing the pulling of paper charts and the use of transcription services, although the extent of the savings will depend on the size of the practice and how well physicians use the new systems.46

■

Most of the available evidence suggests that electronic health records have the potential to reduce duplicated or inappropriate laboratory tests.47 However, a 2005 evaluation of laboratory services in outpatient facilities that adopted health IT systems did not find a differ­ence in the number of duplications.48

■	Two studies found that the adoption of health IT did not have any significant effect on whether or not a radiology test was ordered. However, it may have affected the type of test ordered.49

■

Several studies have investigated whether electronic health records increase the productivity of nurses and physicians. Although the studies have shown mixed results, the measures of productivity that researchers have used in such studies are limited and do not exhaust the ways in which the use of health IT might affect productivity.50

■

Some research suggests that health IT could reduce the length of hospital stays by speeding up certain hospital functions (such as ordering tests and medica­tions) and by avoiding costly errors (such as adverse drug reactions that could lead to delays in discharging patients).51 However, reductions in average lengths of stay may not result in comparable reductions in costs, because health IT may speed certain procedures but not eliminate them.

Incentives for and Barriers to Adoption

The most auspicious examples of health IT have tended to involve relatively integrated health care systems. In such systems, a hospital network or a health plan typically owns the hospitals that provide most care to enrollees, and doctors and other providers work exclusively for the organization (either for a salary or under contract). Because the systems are integrated, they are able to capture savings that are generated by health IT at most points in the process of delivering care.

For example, Kaiser Permanente is a large integrated sys­tem in which the health plan (primarily a health mainte­nance organization) and the providers (physicians and most hospitals and ancillary service providers) exclusively contract with one another to provide care to the health plan’s enrollees. For such a system, reducing the number of unnecessary office visits, for example, benefits the pro­viders, the health plan, and the patients: It may lower the plan’s costs for providing health care while minimizing inconvenience for patients. Kaiser has implemented sys­temwide electronic health records in its facilities in some regions. In those areas, physicians have used such consul­tations to reduce the number of unnecessary office visits (compared with the number in regions without electronic systems).

A number of other integrated health care sys­tems—including Intermountain Healthcare, Geisinger Health System, and Partners HealthCare—have imple­mented electronic health records either across their orga­nizations or in some regions, and administrators of those systems believe that the efficiency and quality of the care they provide have improved as a result. The Department of Veterans Affairs, which also has an integrated health care system, uses electronic health records to serve nearly 6 million patients in more than 1,400 hospitals, clinics, and nursing homes. According to the VA, its use of health IT has reduced its costs and improved the quality of the care it provides.⁵²

For doctors and hospitals that are not part of integrated systems, however, the benefits of health IT are not as easy to capture, and perhaps not coincidentally, those physi­cians and facilities have adopted electronic health records much more slowly. Even though the use of health IT could generate savings for the health system as a whole that might offset the start-up and operating costs involved, many physicians might not be able to reduce their own office expenses or increase their own revenue sufficiently to pay for it. As a result, relatively few providers have adopted health information technol­ogy—according to recent estimates, about 5 percent of physicians.⁵³

Costs of Implementing Health IT. The fixed costs of investing in health IT can be quite high; for small physi­cian practices and small hospitals, those costs might be particularly high relative to their expected revenues. A few studies have examined the costs of implementing electronic health records and computerized physician order entry systems in hospitals. Such costs are difficult to measure, however, because hospitals vary widely in size and type, different health IT applications may be imple­mented, and there is a general lack of data on costs. For those same reasons, any single hospital’s experience in implementing a health IT system cannot be applied more generally to all hospitals.

For integrated health care systems, the annual costs to develop and maintain a health IT system are around 4 percent of operating costs. That calculation would imply that the costs of a nationwide health IT system (including systems already in place) would be on the order of $50 billion per year (given that approximately $1.3 trillion is expected to be spent in 2009 on hospital and physicians’ services in the United States). Some studies indicate that, in addition to any initial investments, annual costs to operate and maintain a physician’s office can average any­where from $3,000 to $9,000 per physician. But other studies indicate that the costs of health IT may be falling. In particular, some Internet-based applications require an annual subscription fee that could be as low as $2,000 per physician. In general, if prices for a given level of capabil­ity continue to fall over time, the quantity and quality of the health IT systems that are purchased should increase.⁵⁴ Because of the fixed costs involved in develop­ing health IT systems, those prices may themselves depend on rates of adoption.

Limited Incentives to Adopt Health IT. Even if the price of a health IT system fell, limited incentives would still tend to constrain the rate of adoption and blunt the impact that greater adoption would have on the use of health care services. Office-based physicians in particular may see no benefit if they purchase such a product, and they may even suffer financial harm. The use of health IT could reduce the number of duplicate diagnostic tests, for example (because the results of past tests would be more readily available), but that improvement in efficiency would be unlikely to increase the income of many physi­cians. For physicians who perform certain diagnostic tests in the office, decreasing the number of tests would reduce their income. (For physicians who order tests from labo­ratories and imaging centers, their income would not drop because those groups are paid separately by health insurance plans.) As a result, the capacity to avoid dupli­cating tests might not spur many physicians to invest in and implement a health IT system. Indeed, physicians might have a more powerful financial incentive to pur­chase additional office diagnostic equipment than to pur­chase a health IT system. Nevertheless, some physicians might invest in health IT to improve the quality of their patients’ care, even if those purchases resulted in little or no net monetary savings.

Health insurance companies that are not integrated may still have an incentive to help providers acquire health IT systems. The technology could help lower the companies’ costs and could improve both the quality of the care that providers deliver and the health of the patients. For doc­tors or hospitals that contract with many health plans, however, any benefits of adopting health IT would be spread across those plans, so no one plan would want to subsidize the full cost of a provider’s health IT system. In addition, a plan may be reluctant or unable to coordinate with other plans regarding the assistance they offer to providers to acquire health IT systems. As an alternative to upgrading their providers’ technology, plans might be able to obtain some of the same benefits by making improvements to their own IT systems and relying pri­marily on claims data.

Effects of Proposals to Adopt Health IT

In considering the impact of legislative proposals relating to health information technology, it is important first to consider the projected rate of adoption under current law. In the near term, the adoption of health IT is expected to continue to grow, primarily among providers who are able to capture the benefits of health IT internally, such as integrated systems, bigger hospitals, and larger physi­cian practices. CBO expects that about 40 percent of physicians will adopt health IT by 2019, with near-universal adoption anticipated over the next quarter-century. The next step is to evaluate whether a proposal affects the expected adoption of health IT—either its speed or its scope—and then whether that change would increase or decrease health care spending or federal bud­getary outlays. In general, the effects of health IT on spending would depend on the incentive structure facing providers and patients. As with all analyses of the budget­ary effects of proposals, the estimated impact would be limited to changes that occurred as the result of federal legislation as opposed to changes that would have natu­rally happened as the industry evolved over time.

If the federal government chose to intervene directly to promote the use of health IT, it could do so by subsidiz­ing that use or by imposing a penalty for failing to use a health IT system. From a budgetary perspective, a penalty is more likely than subsidies to generate savings for the federal government because of the costs of the subsidies. Under the latter approach, payments would end up going to some providers who would have adopted a health IT system even without a subsidy as well as to providers for whom the subsidy made the difference in their decision to adopt a system. Conversely, penalties for providers who do not adopt a system would generate federal receipts. However, providers might respond differently to a subsidy or a penalty depending on how those interven­tions were presented and enforced.

In the context of a broader proposal to modify the health care system, expansions in the use of health IT would interact with other systemwide changes. For example, a proposal that would institute a system of bonuses paid to providers that reduced the total costs of patients who have chronic diseases might encourage providers to adopt health IT so that they could more effectively monitor and influence their use of care. Changes made to other com­ponents of the health care system could even increase the potential for savings from health IT by providing stron­ger incentives for providers and patients to focus on the cost and value of the health care they produce and consume.

One potential benefit of health IT that has not been examined carefully involves its role in research on the comparative effectiveness of medical treatments and prac­tices. Widespread use of health IT could make available large amounts of data on patients’ care and health, which could be used for empirical studies that might improve the quality of health care and help make the delivery of services more efficient. By making clinical data easier to collect and analyze, health IT systems could support rigorous studies to compare the effectiveness and costs of different treatments for a given disease or condition. Then, in response to the studies’ findings, those systems could aid in implementing changes in the kinds of care provided and the way in which services were delivered, as well as track progress in carrying out the changes.

Modifying Laws About Medical Malpractice

Some proposals would seek to change medical practices by focusing on the ways in which patients and medical providers settle disputes about treatment. Such proposals would modify the system for determining liability for medical malpractice. (Medical malpractice claims are a class of common-law causes of action, known as torts.) State law allows individuals to sue physicians and other health care providers for breaches of duty that result in personal injury. The medical malpractice system has two basic objectives:

■	Compensating injured patients for their losses (which can include medical costs, wages, and pain and suffer­ing); and

■	Deterring negligent behavior by medical providers.

Critics charge that the current system is subjective and too costly and that excessive damage awards have increased health care spending, both directly (through higher premiums for malpractice insurance) and indi­rectly (by leading doctors to order additional tests or procedures in an attempt to diminish their risk of being sued—so-called defensive medicine). Other charges are that legal fees consume too large a share of awards and that many patients and their families receive little or no compensation when malpractice occurs. Doctors and hospitals generally have malpractice insurance to protect against the financial risk of a lawsuit, but they have raised concerns about the rising costs of that insurance.

Some proposals would address concerns about the malpractice system by establishing tort limits, such as caps on damage awards. Although some studies have found that tort limits have substantial effects on health care spending, CBO’s own analysis has yielded mixed results—partly reflecting the difficulty of disentangling the impact of those limits from other factors that affect spending. Overall, the analysis indicates that tort limits would reduce malpractice premiums but might not have a broader impact on the use of health care services.

Other approaches could be taken to address concerns about the malpractice system. Those approaches include subsidizing medical malpractice premiums or regulating their growth; creating alternative processes for dispute resolution; providing malpractice protection to physi­cians and hospitals in return for compliance with national guidelines for clinical practice; and establishing a "no-fault" system, which would provide compensation for all medical injuries regardless of whether any negligence was involved. Some states have already taken similar steps, but CBO has not yet analyzed their effects and would have to draw on those experiences as well as other research in evaluating any new federal proposals.

Background

In 2003, about 181,000 severe medical injuries occurred in U.S. hospitals (representing 0.5 percent of all hospital admissions) that were attributable to negligence (see Table 7-1). Only about 17 percent of affected patients chose to file a malpractice claim. Patients who did not file a claim may have been unaware that negligence had occurred, or they may have been discouraged from filing a lawsuit because of the time, effort, and expense involved.

Table 7-1.  

Medical Injuries, Negligence, and the Filing of Malpractice Claims, 2003

Did a Severe Medical Injury Occur?		Was the Severe Injury Due to Negligence?	Medical Encounters				Number of Malpractice Claims Filed (Thousands)		Percentage of Medical Encounters That Result in a Malpractice Claim
			Number (Thousands)		Percentage of Total
No		Not Applicable	37,685		98.6		11		0.03
Yes		No	355		0.9		15		4.26
Yes		Yes	181		0.5		30		16.77
			_____		____		__
All Medical Encounters		Sometimes	38,221		100.0		57		0.15

Source: Congressional Budget Office adapted from David M. Studdert and others, "Disclosure of Medical Injury to Patients: An Improbable Risk Management Strategy," Health Affairs, vol. 26, no. 1 (January/February 2007), pp. 215–226.

Note: Medical encounters represent all inpatient hospital discharges in 2003.

From the point of view of many physicians and hospital officials, the medical malpractice system is a "lottery" in which being sued depends on factors beyond their con­trol. To some extent, data on malpractice suits supports that perception. One study found that among the mal­practice claims filed in 2003, only about half were associ­ated with a severe negligent injury.⁵⁵ That study also esti­mated that about 12 percent of indemnity payments in medical malpractice cases went to claimants who did not suffer an injury because of negligence. Examined in another way, however, the same data indicate that the fil­ing of malpractice claims and the payment of claims are not random. Hospital stays during which a severe negli­gent injury occurred were about 250 times as likely to result in a malpractice claim when compared with stays in which such an injury did not occur.⁵⁶ In addition, that study found that claimants in cases in which a negligent injury occurred were about two and a half times as likely to receive a payment compared with claimants in cases without such an injury.

In 2008, health care providers are likely to spend more than $30 billion to defend against and pay medical malpractice claims.⁵⁷ Although that amount of money is substantial, it represents about 1.5 percent of national health expenditures and less than 3 percent of total pay­ments to doctors and hospitals. Administrative costs in the medical malpractice system—including legal fees, administrative costs for malpractice insurers, and court costs—have been found to account for about half of the total spending on malpractice claims.⁵⁸ That high per­centage primarily reflects the current legal process of determining whether negligence occurred and what the compensatory payment should be.

In theory, new tort limits could lower overall spending for health care in two ways. (See Box 7-1 for a description of commonly proposed limits.) First, tort limits would reduce premiums for malpractice insurance by decreasing the size of the average award paid by malpractice insurers to claimants and perhaps also by reducing the probability that a medical provider would be sued for malpractice.⁵⁹ A drop in malpractice premiums would tend to reduce the prices that providers and insurers negotiate for health care services. (It would also decrease Medicare spending, because Medicare’s payments rates for physicians’ and hospitals’ services include an amount to pay for mal-practice premiums.) Second, changes in tort law could decrease health care spending by reducing the intensity and volume of health care services provided. The argu­ment for such a utilization effect is built on two premises: that fear of litigation drives medical providers to deliver additional—and often unnecessary—medical services, and that the proposed tort limits would lessen that per­ceived threat among physicians and thereby reduce utili­zation and spending. Note, however, that imposing limits on malpractice torts could also constrain the ability of injured patients to collect compensation and might lead to more negligent care.

Box 7-1. 

Types of Tort Reforms

Proposals to modify health care in the United States might place limitations on the system that governs tort claims for medical malpractice in a number of ways.1 In general, those limits are of two types: limits on who can be found liable, and caps on the payments that can be made. Limits on Liable Parties The principle of joint-and-several liability allows a claimant to recover the entire amount of a damage award from any one of the parties found to be responsible for an injury, regardless of each party’s degree of responsibility for that injury. Proposals to eliminate that principle could specify instead that each party is responsible only for the share of damages equal to its degree of responsibility for the injury. Proposals to modify joint-and-several liability might, for example, allow it to be applied only to the defendant found to be at least 50 percent responsible for an injury (so that only that party could be required to pay the full claim). Eliminating joint-and-several liability would reduce the awards that are actually paid in cases in which some of the defendants did not have adequate resources to pay their share of the award. The statute of limitations specifies the period of time following an injury during which the injured party may file a claim for damages. Proposals affecting that statute generally would shorten the period of time available to file and thus would tend to reduce the number of lawsuits and awards. Two types of limits could be applied, the first based on the amount of time that had elapsed since the alleged injury occurred, and the second based on the amount of time that had elapsed since the alleged injury was discovered. One recent proposal would impose a filing deadline of three years after an alleged injury occurred or one year after it was discovered (whichever date was earlier). Caps on Payments Caps on payments can themselves take several forms. One common proposal would limit the amount of noneconomic damages that can be awarded. Economic damages cover medical costs and lost earnings; noneconomic damages compensate for pain and -suffering and mental distress. Other proposals would place a cap on punitive damages. Punitive damages are not intended to compensate the injured party for losses but instead to punish the defendant for egregious behavior and deter other health care providers from similar behavior. Proposals could limit the situations in which plaintiffs might receive punitive damages, cap the amount of punitive damages that plaintiffs could receive, or do both. Finally, some proposals would cap the contingency fees that claimants’ attorneys can collect as a percentage of the total damages recovered. Proposals could also address the more complex issue of payments that are known as “collateral-source benefits.” They constitute compensation for an injury from other sources, such as a health or disability insurance policy. Some proposals would reduce the amount of damages a plaintiff can receive by the amount of any collateral-source benefits the plaintiff had received (either on a mandatory basis or at the discretion of the court). Other proposals would prevent those third parties from receiving any portion of a damage award. 1. For a broader discussion of tort reform proposals and their implications for equity and economic efficiency, see Congressional Budget Office, The Economics of U.S. Tort Liability: A Primer (October 2003).

Summary of Evidence

Several studies have examined the experience of states that have implemented tort limits and found that various types of restrictions on malpractice liability can reduce total awards and thereby lead to lower premiums for mal­practice insurance. The Office of Technology Assessment issued a report in 1993 summarizing the first wave of studies on the experience of states that set limits on mal­practice liability in the 1970s and 1980s.⁶⁰ The report concluded that caps on damage awards consistently reduced the size of claims and, in turn, lowered rates for malpractice insurance premiums. Furthermore, it found that limits on the extent to which various parties could be held liable were also effective in slowing the growth of premiums. Similarly, a 2004 study that examined state data from 1993 to 2002 found that a cap on non-economic damages reduced malpractice insurance premiums by more than 15 percent.⁶¹

In previous analyses, CBO considered the effects of limits on tort claims for medical malpractice at the state level and concluded that such limits decreased both malprac­tice awards and malpractice insurance premiums. In its 2008 report titled Budget Options, Volume 1: Health Care, CBO estimated that imposing limits on torts for medical malpractice cases would lower malpractice premiums nationwide by about 6 percent, on average, from the lev­els likely to occur under current law. (The savings in each state would depend in part on the restrictions already in effect.) Savings of that magnitude would have only a modest impact on total health care expenditures, how­ever—reducing total health care spending by less than 0.2 percent.

CBO and other researchers have also used the variation in state laws to assess whether tort limits on malpractice claims have broader effects on health care spending. One prominent set of studies examined the relationship between state tort limits and Medicare spending on hos­pital care for patients with heart disease and concluded that those limits would ultimately reduce such spending by between 4 percent and 9 percent.⁶² Other studies have found much smaller effects.

After carefully considering the economic literature and conducting its own statistical analysis of the data, CBO has not found consistent evidence that changes in the medical malpractice environment would have a measur­able impact on health care spending.⁶³ In part that is because the estimated effects of limits on malpractice torts vary substantially across different measures of health care spending and across different types of tort limits. In some cases, specific tort limits appear to be associated with reductions in health care spending; in other cases, there appears to be no relationship; and in still other cases, tort limits appear to be associated with higher spending (a finding that is counterintuitive). That data analysis also indicated the challenges involved in using statistical methods to separate the effects of tort reforms from the impact of other factors that might affect spend­ing on health care.

CBO has not yet analyzed in detail other approaches to change the malpractice system or offset effects that are perceived to be adverse. Most of those approaches—such as restricting the increases in premiums made by medical malpractice insurance carriers or creating special courts for malpractice cases or processes for alternative dispute resolution—have already been adopted by one or more states. In addition, a no-fault compensation fund is in place for injuries related to vaccines.⁶⁴ CBO would consider the evidence from those examples in estimating the effect of enacting one or more of the approaches into federal law.

---

1

Ali H. Mokdad and others, "Actual Causes of Death in the United States, 2000," Journal of the American Medical Association, vol. 291, no. 10 (March 10, 2004).

2

Congressional Budget Office,Technological Change and the Growth of Health Care Spending (January 2008).

3

Obesity is defined on the basis of body mass index (BMI), which equals weight (in kilograms) divided by height squared (in meters). Among adults, normal weight is defined as a BMI between 18.5 and 25, overweight is defined as a BMI between 25 and 30, and obesity is defined as a BMI of 30 or greater. Morbid obesity—also known as clinically severe obesity—is defined as a BMI of 40 or more. Children are defined as overweight if they exceed the 95th percentile of the Centers for Disease Control and Prevention’s 2000 growth charts for their age and sex.

4

Roland Sturm, "The Effects of Obesity, Smoking, and Drinking on Medicaid Problems and Costs," Health Affairs (March/April 2002).

5

Department of Health and Human Services, National Center for Health Statistics, Health, United States, 2007 (Hyattsville, Md., 2007), Figure 13.

6

Ibid.

7

See, for example, R. Whitaker and others, "Predicting Obesity in Young Adulthood from Childhood and Parental Obesity," New England Journal of Medicine, vol. 337, no. 13 (September 25, 1997), pp. 869–873; and M. K. Serdula and others, "Do Obese Children Become Obese Adults? A Review of the Literature," Preventive Medicine, vol. 22, no. 2 (1993), pp. 167–177.

8

Congressional Budget Office, Growing Disparities in Life Expec­tancy, Issue Brief (April 17, 2008).

9

Ellen R. Meara, Seth Richards, and David M. Cutler, "The Gap Gets Bigger: Changes in Mortality and Life Expectancy, by Education, 1981–2000," Health Affairs, vol. 27, no. 2 (2008), pp. 350–360.

10

Charles L. Baum and Christopher J. Ruhm, Age, Socioeconomic Status, and Obesity Growth, Working Paper No. 13289 (Cambridge, Mass.: National Bureau of Economic Research, August 2007).

11

David Mechanic and others, eds., Policy Challenges in Modern Health Care (Piscataway, N.J.: Rutgers University Press, 2005).

12

W. Kip Viscusi and Jahn Karl Hakes, "Risk Beliefs and Smoking Behavior," Economic Inquiry, vol. 46, no. 1 (January 2008), pp. 45–59.

13

However, the share of light-to-moderate smokers who quit was 3 percentage points higher in the intervention group than in the control group. See COMMIT Research Group, "Community Intervention Trial for Smoking Cessation (COMMIT): I. Cohort Results from a Four-Year Community Intervention," American Journal of Public Health, vol. 85, no. 2 (February 1995), pp. 183–192.

14

Current law allows individuals to deduct from their taxable income the costs of smoking cessation programs and, if the treat­ment is for a specific disease diagnosed by a physician, fees for membership in a weight reduction group, but taxpayers must have total medical expenses in excess of 7.5 percent of their adjusted gross income to qualify for such deductions. Overall, less than 8 percent of tax filers claim the deduction for itemized medical expenses. The share of filers who deduct costs related to the prevention of smoking or obesity is probably much smaller.

15

Jason M. Fletcher, David Frisvold, and Nathan Tefft, Can Soft Drink Taxes Reduce Population Weight? University of Michigan Working Paper (August 18, 2007), http://sitemaker.umich.edu/frisvold/files/soda_taxes_and_obesity_20070817web.pdf; and Oliver Mytton and others, "Could Targeted Food Taxes Improve Health?" Journal of Epidemiology and Community Health, vol. 61, no. 8 (2007), pp. 689–694.

16

Anjali Jain, What Works for Obesity?: A Summary of the Research Behind Obesity Interventions, paper prepared for United Health Foundation (London: BMJ Publishing Group, April 30, 2004), www.unitedhealthfoundation.org/obesity.pdf; Institute of Medi­cine, Health and Behavior: The Interplay of Biological, Behavioral, and Societal Influences (Washington, D.C.: National Academy Press, 2001); and Kim Sutherland, Sheila Leatherman, and Jon Christianson, Paying the Patient, Does it Work? (London: Health Foundation, October 2008), http://health.org.uk/publications/research_reports/paying_the_patient.html.

17

William N. Evans, Matthew C. Farrelly, and Edward Montgom­ery, "Do Workplace Smoking Bans Reduce Smoking?" American Economic Review, vol. 89, no. 4 (September 1999), pp. 728–747.

18

David R. Just, Lisa Mancino, and Brian Wansink, Could Behav­ioral Economics Help Improve Diet Quality for Nutrition Assistance Program Participants? Economic Research Report No. ERR-43 (Department of Agriculture, Economic Research Service, June 2007).

19

Debbie A. Lawlor and Nish Chaturvedi, "Treatment and Preven­tion of Obesity—Are There Critical Periods for Intervention?" International Journal of Epidemiology, vol. 35, no. 1 (2006), pp. 3–9.

20

Adam Gilden Tsai and Thomas A. Wadden, "Systematic Review: An Evaluation of Major Commercial Weight Loss Programs in the United States," Annals of Internal Medicine, vol. 142, no. 1 (January 4, 2005), pp. 56–66; and Jerome P. Kassirer and Marcia Angell, "Losing Weight—An Ill-Fated New Year’s Resolution," New England Journal of Medicine, vol. 338, no. 1 (January 1, 1998), pp. 52–54.

21

Evelyn P. Whitlock and others, Effectiveness of Weight Management Programs in Children and Adolescents, Evidence Report/Technology Assessment No. 170 (prepared by the Oregon Evidence-Based Practice Center, Portland, Ore., for the Agency for Healthcare Research and Quality, September 2008).

22

Counseling to encourage healthy behavior or discourage unhealthy habits is also considered a preventive service. Public health measures, such as wastewater treatment, play an important role in preventing disease but are beyond the scope of this report.

23

Cost-effectiveness is measured by comparing the net cost of a ser­vice (measured in discounted, or current year, dollars) to the net health benefits (typically measured in quality-adjusted life years, or QALYs). QALYs take into account both an individual’s life expectancy and his or her health status over the remaining life span. A year of perfect health is worth 1 QALY, and a year of less than perfect health is given a weight that is less than 1. A preven­tive service, or a medical service more generally, is regarded as cost-effective if the cost per QALY falls below a given threshold.

24

See Eileen Salinsky, Clinical Preventive Services: When Is the Juice Worth the Squeeze? Issue Brief No. 806 (Washington, D.C.: National Health Policy Forum, August 24, 2005); and Kaiser Family Foundation and Health Research and Educational Trust, Employer Health Benefits: 2008 Annual Survey (Washington, D.C.: Kaiser/HRET, September 2008).

25

J. P. Bynum and others, "The Influence of Health Status, Age, and Race on Screening Mammography in Elderly Women," Archives of Internal Medicine, vol. 165, no. 18 (October 10, 2005), pp. 2083–2088.

26

See Michael Maciosek and others, "Priorities Among Effective Clinical Preventive Services: Results of a Systemic Review and Analysis," American Journal of Preventive Medicine, vol. 31, no. 1 (2006), pp. 52–61.

27

John P. Mullooly and others, "Influenza Vaccination Programs for Elderly Persons: Cost-Effectiveness in a Health Maintenance Organization," Annals of Internal Medicine, vol. 121, no. 12 (December 15, 1994), pp. 947–952; and Kristin L. Nichol and others, "Benefits of Influenza Vaccination for Low-, Intermediate-, and High-Risk Senior Citizens," Archives of Internal Medicine, vol. 158, no. 16 (September 14, 1998), pp. 1769–1776. For a recent critique of the methodology used on those studies, see L. Simonsen and others, "Mortality Benefits of Influenza Vaccina­tion in Elderly People: An Ongoing Controversy," Lancet Infec­tious Diseases, vol. 7, no. 10 (October 2007), pp. 658–666.

28

Maciosek and others, "Priorities Among Effective Clinical Preven­tive Services."

29

Joshua T. Cohen and others, "Does Preventive Care Save Money? Health Economics and the Presidential Candidates," New England Journal of Medicine, vol. 358, no. 7 (February 14, 2008), pp. 661–663.

30

American Academy of Family Physicians, fact sheet on patient-centered medical home, www.aafp.org/online/etc/medialib/aafp_org/documents/policy/fed/medicalhome.Par.0001.File.tmp/PC-MHfactsheet.doc (accessed June 25, 2008).

31

American Medical Association, Specialty Society RVS Update Committee, "Medicare Medical Home Demonstration Project," letter to Kerry N. Weems, administrator of the Centers for Medi­care and Medicaid Services (April 29, 2008). The $650 estimate is based on the "Tier III" medical home, as defined in the AMA letter.

32

Stephan Wilhide and Tim Henderson, Community Care of North Carolina: A Provider-Led Strategy for Delivering Cost-Effective Pri­mary Care to Medicaid Beneficiaries (June 2006), www.aafp.org/online/etc/medialib/aafp_org/documents/policy/state/medicaid/ncfull.Par.0001.File.tmp/ncfullreport.pdf.

33

Timothy G. Ferris and others, "Leaving Gatekeeping Behind—Effects of Opening Access to Specialists for Adults in a Health Maintenance Organization," New England Journal of Med­icine, vol. 345, no. 18 (November 1, 2001), pp. 1312–1317.

34

Diane P. Martin and others, "Effect of a Gatekeeper Plan on Health Services Use and Charges: A Randomized Trial," American Journal of Public Health, vol. 79, no. 12 (December 1, 1989), pp. 1628–1632.

35

David Matheson and others, Realizing the Promise of Disease Man­agement: Payer Trends and Opportunities in the United States (Bos­ton: Boston Consulting Group, February 2006).

36

Congressional Budget Office, "An Analysis of the Literature on Disease Management Programs," letter to the Honorable Don Nickles (October 13, 2004).

37

Soeren Mattke, Michael Seid, and Sai Ma, "Evidence for the Effect of Disease Management: Is $1 Billion a Year a Good Investment?"American Journal of Managed Care, vol. 13, no. 12 (December 2007), pp. 670–676.

38

Randall Brown and others, The Evaluation of the Medicare Coordinated Care Demonstration: Findings for the First Two Years (Princeton, N.J.: Mathematica Policy Research, March 2007).

39

Victor Villarga and Tamin Ahmet, "Effectiveness of a Disease Management Program for Diabetes," Health Affairs, vol. 23, no. 4 (July/August 2004), pp. 255–266. For responses to Villarga and Ahmet, see Thomas Wilson and Ariel Linden, "Measuring Diabe­tes Management," and Joe Selby and K.M. Narayan, "Lowering Diabetes Costs," both in Health Affairs, vol. 23, no. 6 (November/December 2004), pp. 277–278.

40

For additional discussion of this topic, see Congressional Budget Office, Research on the Comparative Effectiveness of Medical Treat­ments: Issues and Options for an Expanded Federal Role (December 2007).

41

William E. Boden and others, "Optimal Medical Therapy With or Without PCI for Stable Coronary Disease," New England Journal of Medicine, vol. 356, no. 15 (April 12, 2007), pp. 1503–1516.

42

Stephan Heres and others, "Why Olanzapine Beats Risperidone, Risperidone Beats Quetiapine, and Quetiapine Beats Olanzapine: An Exploration of Head-to-Head Comparison Studies of Second Generation Antipsychotics," American Journal of Psychiatry, vol. 163, no. 2 (February 2006), pp. 185–194.

43

See Institute of Medicine, Learning What Works Best: The Nation’s Need for Evidence on Comparative Effectiveness in Health Care (September 2007), p. 2, www.iom.edu/ebmeffectiveness.

44

National Emphysema Treatment Trial Research Group, "A Ran­domized Trial Comparing Lung-Volume-Reduction Surgery with Medical Therapy for Severe Emphysema," New England Journal of Medicine, vol. 348, no. 21 (May 22, 2003), pp. 2059–2073.

45

For more extensive discussion, see Congressional Budget Office, Evidence on the Costs and Benefits of Health Information Technology (May 2008).

46

Samuel J. Wang and others, "A Cost-Benefit Analysis of Electronic Medical Records in Primary Care," American Journal of Medicine, vol. 114, no. 5 (April 1, 2003), pp. 397–403.

47

David W. Bates and others, "A Randomized Trial of a Computer-Based Intervention to Reduce Utilization of Redundant Labora­tory Tests," American Journal of Medicine, vol. 106, no. 2 (February 1999), pp. 144–150; David W. Bates and others, "What Proportion of Diagnostic Tests Appear Redundant?" American Journal of Medicine, vol. 104, no. 4 (April 1998), pp. 361–368; William M. Tierney and others, "Computerized Dis­play of Past Test Results: Effects on Outpatient Testing," Annals of Internal Medicine, vol. 107, no. 4 (October 1987), pp. 569–574; and William M. Tierney and others, "Computer Predictions of Abnormal Test Results: Effects on Outpatient Testing," Journal of the American Medical Association, vol. 259, no. 8 (February 26, 1988), pp. 1194–1198.

48

Terhilda Garrido and others, "Effect of Electronic Health Records in Ambulatory Care: Retrospective, Serial, Cross Sectional Study," British Medical Journal, vol. 330, no. 7491 (March 12, 2005), pp. 581–585.

49

Ibid.; and Linda H. Harpole and others, "Automated Evidence-Based Critiquing of Orders for Abdominal Radiographs: Impact on Utilization and Appropriateness," Journal of the American Medical Informatics Association, vol. 4, no. 6 (November/December 1997), pp. 511–521.

50

Lise Poissant and others, "The Impact of Electronic Health Records on Time Efficiency of Physicians and Nurses: A Systemic Review," Journal of the American Medical Informatics Association, vol. 12, no. 5 (September/October 2005), pp. 505–516; Lisa Pizziferri and others, "Primary Care Physician Time Utilization Before and After Implementation of an Electronic Health Record: A Time-Motion Study," Journal of Biomedical Informatics, vol. 38, no. 3 (June 2005), pp. 176–188; J. Marc Overhage and others, "Controlled Trial of Direct Physician Order Entry: Effects on Physicians’ Time Utilization in Ambulatory Primary Care Internal Medicine Practices," Journal of the American Medical Informatics Association, vol. 8, no. 4 (July/August 2001), pp. 361–371; and David Gans and others, "Medical Groups’ Adoption of Electronic Health Records and Information Systems," Health Affairs, vol. 24, no. 5 (September/October 2005), pp. 1323–1333.

51

Hagop S. Mekhjian and others, "Immediate Benefits Realized Fol­lowing Implementation of Physician Order Entry at an Academic Medical Center," Journal of the American Medical Informatics Asso­ciation, vol. 9, no. 5 (September/October 2002), pp. 529–539.

52

A recent Congressional Budget Office report discusses the VA sys­tem in greater detail; see Congressional Budget Office, The Health Care System for Veterans: An Interim Report (December 2007).

53

Rates of adoption vary by the definition of health IT used in a particular survey. The rates provided in this analysis are based on the adoption of health IT systems that include all or most recom­mended capabilities—such as electronic documentation of pro­viders’ notes, electronic viewing of laboratory and radiological results, electronic prescribing, computerized physician order entry, clinical decision support, and interoperability with other systems. See Catherine M. DesRoches and others, "Electronic Health Records in Ambulatory Care—A National Survey of Physicians," New England Journal of Medicine, vol. 359, no. 1 (July 3, 2008), pp. 50–60.

54

Extremely low prices, however, might signal that a product has lower quality and fewer components or features.

55

David M. Studdert and others, "Claims, Errors, and Compensa­tion Payments in Medical Malpractice Litigation," New England Journal of Medicine, vol. 354, no. 19 (May 11, 2006), pp. 2024–2033.

56

Among hospital stays during which a patient experienced a severe negligent injury, the probability of the hospital being sued was about 17 percent. Among hospital stays during which a patient either did not experience a severe medical injury or experienced a severe medical injury that was not due to negligence, the probabil­ity of the hospital being sued was about 0.07 percent. The ratio of those probabilities (17 percent/ 0.07 percent) is about 250.

57

Congressional Budget Office calculations based on data from Towers Perrin, 2007 Update on U.S. Tort Cost Trends (December 2007).

58

Studdert and others, "Claims, Errors, and Compensation Pay­ments in Medical Malpractice Litigation." Tillinghast-Towers Per­rin examines tort costs more broadly, including nonmedical torts, and estimates that payments to claimants (net of attorneys’ fees) represent about 46 percent of insured tort costs. See Tillinghast-Towers Perrin, U.S. Tort Costs: 2003 Update (December 2003).

59

The proposed limits could reduce the probability that claims would be filed if they affected the decisionmaking process of potential plaintiffs and their attorneys. Generally, plaintiffs’ attor­neys receive payment in the form of a contingency fee, meaning they receive a percentage of any award or settlement. Plaintiffs’ attorneys, in choosing which cases to take on and which cases to pursue, assess each case to determine the likelihood of receiving an award and the probable amount of any resulting fee. If caps on awards and on attorneys’ fees reduced contingency payments, plaintiffs’ attorneys would be less likely to take on certain cases, and, in the longer run, fewer attorneys might practice that branch of the law.

60

Office of Technology Assessment, Impact of Legal Reforms on Medical Malpractice Costs, OTA-BP-H-119 (September 1993).

61

Kenneth E. Thorpe, "The Medical Malpractice ‘Crisis’: Recent Trends and the Impact of State Tort Reforms, Health Tracking Trends, Web Exclusive (January 21, 2004).

62

See these articles by Daniel P. Kessler and Mark B. McClellan: "Malpractice Law and Health Care Reform: Optimal Liability Policy in an Era of Managed Care," Journal of Public Economics, vol. 84, no. 2 (May 2002), pp. 175–197; How Liability Law Affects Medical Productivity, Working Paper No. 7533 (Cam­bridge, Mass.: National Bureau of Economic Research, February 2000); and "Do Doctors Practice Defensive Medicine?" Quarterly Journal of Economics, vol. 111, no. 2 (May 1996), pp. 353–390.

63

The details of that research and CBO’s synopsis of other studies examining defensive medicine can be found in Congressional Budget Office, Medical Malpractice Tort Limits and Health Care Spending, Background Paper (April 2006).

64

See Health Resources and Services Administration, National Vaccine Injury Compensation Program (VICP), www.hrsa. gov/vaccinecompensation.