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4
Impact of Prior Review Programs

The explosive growth of utilization management activities, organizations, and expenditures raises an obvious question: Does utilization management work? More specifically, does it do what it is intended to do at a reasonable cost without unacceptable effects on quality or access to care? And under what circumstances do programs have better results? How have patients, physicians, and other involved parties fared?

Unfortunately, in its search of the literature, the committee found that relatively little rigorous empirical evidence is publicly available on the effects of these programs. What is available only covers those elements of utilization management that have been in place long enough for assessment data to have been accumulated. Prior review of the actual medical need for specific procedures is mostly too new to have been evaluated.

This chapter summarizes the evidence available to the committee, describes weaknesses in the evidence, and considers the positive and negative aspects of prior service for patients, providers, and purchasers. Most of the evaluations cited here focus on preadmission review and continued-stay review. A review of evidence specific to the effects of high-cost case management is presented in Chapter 5. Appendix B of this report discusses the limited information about the effects of different methods used by HMOs to influence patient care decisions.

The committee has not attempted a global assessment of the broad societal effects of utilization management. At this point, the base is simply too weak to estimate how utilization management has specifically affected



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Page 91 4 Impact of Prior Review Programs The explosive growth of utilization management activities, organizations, and expenditures raises an obvious question: Does utilization management work? More specifically, does it do what it is intended to do at a reasonable cost without unacceptable effects on quality or access to care? And under what circumstances do programs have better results? How have patients, physicians, and other involved parties fared? Unfortunately, in its search of the literature, the committee found that relatively little rigorous empirical evidence is publicly available on the effects of these programs. What is available only covers those elements of utilization management that have been in place long enough for assessment data to have been accumulated. Prior review of the actual medical need for specific procedures is mostly too new to have been evaluated. This chapter summarizes the evidence available to the committee, describes weaknesses in the evidence, and considers the positive and negative aspects of prior service for patients, providers, and purchasers. Most of the evaluations cited here focus on preadmission review and continued-stay review. A review of evidence specific to the effects of high-cost case management is presented in Chapter 5. Appendix B of this report discusses the limited information about the effects of different methods used by HMOs to influence patient care decisions. The committee has not attempted a global assessment of the broad societal effects of utilization management. At this point, the base is simply too weak to estimate how utilization management has specifically affected

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Page 92 total health care expenditures, overall use of health services, and attitudes toward medical care. Direction of Available Evidence: Impact on Utilization and Cost Although reports of the impact of prior review programs on utilization and cost suffer individually from an array of methodological weaknesses, they present a reasonably consistent pattern of positive results. Taken together, the studies show: • a substantial initial drop in inpatient hospital utilization following introduction of prior review, with use rates tending to decline at a lesser rate or to level off thereafter; • an increase in the use of outpatient facilities and physician office services following the introduction of prior review; • a greater decline in inpatient utilization for reviewed groups than for nonreviewed groups during a period of generally declining hospital use; • among groups covered by prior review, a more sizable drop in inpatient utilization for groups that started with higher than average initial utilization rates compared with those with lower than average initial utilization rates; and • a lower rate of increase in the short-term in per-employee medical care costs for groups covered by prior review compared with those that were not, but no long-term reduction in the rate of growth in total medical care spending. Evidence discussed in this chapter about the effects of prior review programs was discovered through site visits by the committee, computerized literature searches, presentations to the committee, and less formal efforts. The majority of reports take the form of marketing materials, press releases, and client reports prepared by review organizations. Much scarcer are more sophisticated assessments prepared by review organizations or academic researchers. The earliest studies typically were simple one-group before-and-after studies with no comparison groups. Several recent studies are more methodologically sophisticated, although they too are imperfect. Limitations of available research are described at the end of this section and in the appendix to this chapter. Before-and-After Studies Most of the early and influential attempts to demonstrate the effects of prior review programs were based on simple before-and-after comparisons.

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Page 93 These comparisons are typical of the promotional materials, conference presentations, and materials that most utilization management companies release to the public to show changes in utilization for client groups. The trend data presented in summary form by one utilization management company that the committee visited are illustrative. For a large food and chemical company, the number of hospital days per 1,000 covered individuals dropped from 579 days before initiation of prior review management to 486 and then 389 days in the subsequent 2 years. For an aerospace company, the comparable figures were 627 for the preprogram year and 514 and 416 for the next 2 years. And for a large consumer products company, the number of inpatient days per 1,000 covered individuals went from 1,065 days before to 889 to 703 days after the program. Many other companies report similar drops in inpatient use after the introduction of prior review. In 1984, the journal Hospitals included some typical press release information in a story on preadmission review programs (''Preadmission Review Cuts Hospital Use,'' 1984). The story noted that Blue Cross and Blue Shield of North Carolina reported that a pilot preadmission review program helped cut hospital days by 37 percent and that Blue Cross of Northeast Ohio reported a 23 percent decrease in hospital days and savings of $30 million during the first 5 months of a preadmission review program. More fully described is the experience of Deere & Company of Moline, Illinois, as presented in a case study prepared by the Health Systems Management Center at Case Western Reserve University (Kauer, 1983). Deere initiated a utilization management program in 1977 by using PSROs to perform preadmission and concurrent review. Over 36 months, inpatient days dropped 21 percent, and the company estimated savings of $11 for every program dollar spent. The case study noted that one PSRO performed its own reviews, whereas another PSRO delegated the review responsibility to each area hospital. The nondelegated program showed greater initial results, with the delegated program catching up somewhat in later months. The Deere study mentions that company staff accompanied PSRO officials to discuss utilization problems with individual hospitals, and the company added HMO options to its benefit offerings in 1980. These activities point to a major problem with before-and-after studies. While prior review programs were being introduced, other changes were also occurring that affected utilization patterns within particular companies and across society more generally. Comparative Studies Over the past decade, several factors, including the growing safety and acceptance of procedures done on an outpatient basis, the increasing supply of outpatient resources, and restrictions on inpatient payments, have

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Page 94 TABLE 4-1 Some Factors That May Affect the Impact or Assessment of Prior Review Benefit Plan Characteristics   Coverage of noninpatient benefits   Levels and types of beneficiary cost-sharing   Choice among altemative benefit plans Other Initiatives   Quality of care monitoring   Selective contracting   Hospital payment method and financial incentives   Physician payment method and financial incentives   Retrospective utilization review   Provider audit, feedback, and education   Consumer education and health promotion Govemment Regulation   Medicare prospective payment system, PRO programs   State rate setting and other hospital regulation   Mandated benefits and other insurance regulation   Medicaid program features   Health planning Individual, Group, and Community Characteristics   Age, income, education, race, and sex   Union membership   Marital and family status   Other health insurance coverage   Health status   Occupation and industry   Geographic region   Urban or rural location Health Care Delivery System,   Supply of hospital and other institutional resources   Supply and distribution of physicians and other practitioners   Medicare and Medicaid market shares   HMO and PPO market shares   Blue Cross and Blue Shield market shares   Proportion of self-paying and uninsured patients changed the balance of incentives for using inpatient versus outpatient care and have contributed to substantial general reductions in hospital use (Table 4-1). Data from the American Hospital Association, the Hospital Discharge Survey, the Health Interview Survey, and the Blue Cross and Blue Shield Association indicate that hospital days per 1,000 people under age 65 were beginning to level off and then drop in the latter part of the 1970s (Lerner et al., 1983b). The drop accelerated in the 1980s, before it appeared to level off again recently. Simple before-and-after studies are not capable of distinguishing the impact of utilization management from the impact of other factors and, thus, may wrongly credit those programs with changes that would have occurred anyway.

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Page 95 Some studies have attempted to control for the impact of systemwide influences by comparing the utilization and cost experience of groups covered by prior review programs with groups that were not. The RCA organization tried this approach within its own organization. In 1985, it instituted an array of utilization management programs (called Plan for Health) for about half of its employees, early retirees, and dependents while continuing the old plan for the other half. Although inpatient hospital utilization dropped for both groups, it dropped more for the utilization management group. Inpatient days for medical care patients showed the same general pattern. However, surgical days unexpectedly increased for both groups, although more for the nonmanaged group. Nevertheless, net benefit costs for employees under utilization management dropped by 4 percent, whereas they rose by 6 percent for the nonmanaged group. The company estimated a 4:1 return on its investment in the utilization management program (O'Donnell, 1987). In 1984, Blue Cross and Blue Shield of Massachusetts added a comprehensive utilization management option for its fee-for-service business and then compared what happened to hospital utilization for its subscribers who were covered by the option, those who were not covered by the option, and those who were covered under an HMO. In 1983, the number of hospital days per 1,000 covered individuals was 680 for the traditional plan and 409 for the organization's HMOs. By 1985, traditional plan days had dropped to 600 (a 12 percent drop), "managed" fee-for-service days were 520 (down 24 percent), and HMO days dropped to 374 (a 9 percent drop) (Getson, 1987). An analysis prepared for the Service Employees International Union and the State of Michigan took a somewhat different approach. The evaluation of the group's precertification program attempted to measure "the difference between what experience was expected to occur if no intervention took place, and what actual experience was given implementation of the program" (SEIU, 1988). The decline in hospital use for the program group was higher than that for a statewide comparison group for the first year of the program but not for the second year. However, in both years the program had lower hospital utilization than predicted on the basis of preprogram trends for the union group. The analysis estimated a gross savings/cost ratio for the program of 2.27 to 1. The savings were not reduced by the cost of alternative services but did exclude the estimated fixed costs of avoided admissions (Service Employees International Union, 1988). Yet another approach was used by Blue Cross of Greater Philadelphia, which established a utilization management program as part of its 1985 contractual agreement with area hospitals. Because this agreement covered all enrollees unless a client explicitly rejected application of the utilization management features (which was rare), the company did not

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Page 96 have an adequate base to compare enrollees with and without prior review. Instead, the company compared inpatient days per 1,000 covered individuals for contracting hospitals, which dropped 23 percent from 1984 to 1986, with inpatient clays for noncontracting hospitals (mostly specialized mental health, rehabilitation, and similar facilities), which saw inpatient clays increase by 28 percent. One consequence was that the share of inpatient clays accounted for by noncontracting hospitals went from 17 to 27 percent of the total inpatient days. Total inpatient days per 1,000 covered individuals for all hospitals decreased by 20 percent from 1984 to 1987. Combined inpatient and outpatient surgical rates (excluding physician office surgery), which had increased from 48.2 procedures per 1,000 in 1980 to 53.1 in 1983, declined to 47.9 in 1986 (Blue Cross of Greater Philadelphia and Pennsylvania Blue Shield, 1986, 1987). Multivariate Studies A potential contaminant in simple comparative studies is the fact that the introduction of utilization management may have been accompanied by other changes that could affect utilization and costs. For example, other cost-containment methods such as increased cost-sharing by beneficiaries have often been implemented along with prior review, and sales and acquisitions can change the work force composition, which in turn can affect health care use. Also, the purchasers that self-select utilization management could differ in various ways from those that do not. Although not all such factors can be controlled, some multivariate studies have attempted to rule out alternative explanations for changes in cost and use after the introduction of prior review. One utilization management company structures its comparisons by trying to match review and nonreview groups by industry, geographic region, and other characteristics. In an analysis for one insurance company client, the firm reported that hospital days per 1,000 covered individuals dropped 14 percent for review groups, whereas for nonreview groups they dropped 7 percent. The percentage increase in expenditures for groups with prior review was lower than the rate of medical inflation, whereas the opposite was true for comparison groups (Health Data Institute, 1988). An analysis of early state programs to contain Medicaid costs reported that prior authorization for elective surgery and specific services appeared to reduce growth in real hospital expenditures by controlling growth in the number of beneficiaries receiving hospital care. For the period 1977-1984, states with prior authorization showed a 1.4 percent average annual increase in real total inpatient expenditures, whereas states without this policy experienced a 6.5 percent increase. The number of beneficiaries receiving inpatient care dropped, on average, by 1.2 percent a year for

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Page 97 prior authorization states but rose 1.9 percent a year for the other states. A multivariate statistical analysis linked prior authorization with a 2.7 percent annual reduction in beneficiaries who received inpatient care after several other changes in Medicaid policy and state environments were controlled (Zuckerman, 1987). Feldstein et al. (1988) and Wickizer et al. (1989) analyzed claims data for one insurer to compare utilization and costs for clients with and without utilization management. Using a combination of cross-sectional and longitudinal analyses that included controls for some differences in case mix, employee characteristics, market factors, and benefit plan features, the authors concluded that for the period 1984-1986 prior review reduced admissions by 13 percent, inpatient days by 11 percent, and total medical expenditures by 6 percent. (Because some groups included in this part of the analysis had a utilization management program in place before the first year for which data were analyzed, these results do not reflect straightforward preprogram-postprogram utilization.) From their statistical analysis, the authors concluded that the review programs produced a "onetime" reduction in use and costs but had little impact on growth in use and costs over time. In another analysis, the researchers compared groups with high utilization prior to adoption of utilization review to groups with low prior utilization. The former were found to have significant decreases in use and costs, but the latter did not (Feldstein et al., 1988). After introduction of a utilization management product called HEALTHLINE, Aetna Life and Casualty compared postimplementation utilization and cost experience for employee groups with (122,299 employees) and without (296,519 employees) the program. The time period for the study was a six-quarter period ending December 31, 1987. The study controlled for some claimant, group, and benefit plan differences across the two samples (for example, claimant age, total covered employees, and coinsurance rates). The multivariate analysis also included a time trend variable. The researchers found that hospital admission rates dropped nearly 8 percent for the program sample but only I percent for the nonprogram sample; overall hospital days per 1,000 covered individuals dropped about 4 percent for the former and about 2 percent for the latter. Surgical outpatient costs per employee increased at about the same rate for both samples (15.5 and 16.1 percent, respectively), but inpatient medical and surgical costs rose about 5 percent for the sample with utilization management and 9 percent for the sample without.1 Combined inpatient costs and outpatient surgical costs rose 6 and 10 percent, respectively. The study estimated gross savings from utilization management to be around 12 percent 1 Inpatient costs included room and board, ancillary services, and physician services. Surgical outpatient costs included physician but not facility charges.

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Page 98 during the six-quarter period. The researchers emphasize that the results apply only to the study period and should not be used to predict program performance in other periods or for other employer groups. Further analyses will include preprogram data, data on nonsurgical outpatient use and costs, and other control variables (Allen and Khandker, 1988; Harris Allen, Aetna Casualty and Life, personal communication, November 3, 1988). Scheffler et al. (1988) studied the impact of the Medicare prospective payment system (PPS) and Blue Cross benefits cost-management programs using quarterly claims data (1980-1986) on Blue Cross inpatient admissions and lengths of stay, hospital outpatient visits, total inpatient and total outpatient benefit payments, and total payments per member (adjusted for inflation). When controlled for the effects of the Medicare PPS, several other Blue Cross cost-management programs, and an array of environmental variables (for example, state regulation, state average income, and state age composition), the study found that (1) preadmission review was associated with lower hospital admission and outpatient visit rates but did not affect payments per 1,000 members for inpatient services or outpatient visits; (2) concurrent review was linked to lower inpatient and higher outpatient payments per 1,000 members and had a negative but not statistically significant impact on total payments per member; and (3) retrospective denial of payment to hospitals for inappropriate utilization was correlated with lower values for all utilization and payment measures.2 In addition, the study attributed significant declines in utilization rates and rates of payment increase to the spillover effects of Medicare PPS and Blue Cross hospital payment policies (Scheffler et al., 1988). Impact of Second-Opinion Programs The committee did not closely examine the impact of second-opinion programs. In part, this choice reflects the fact that second-opinion programs have a longer history of application and assessment than do the other utilization management techniques investigated by the committee. (For an up-to-date review of the evidence on the impact of second-opinion programs, see Rutgow and Sieverts [1989]). 2 This statistical analysis is complicated by characteristics of some of the independent variables. In all but 2 years between 1980 and 1986, over 95 percent of the Blue Cross plans reported that they had (and had used) policies to deny reimbursement for inappropriate use. In all years, over 95 percent had retrospective utilization review. The percentage of plans with concurrent review went from 52 percent in 1980 to 90 percent in 1986. No information was available on preadmission review in 1980 or 1981, but between 1982 and 1986 the percentage of plans with this program went from 28 to 95 percent. The analysis was not able to consider variations in the scope of programs nor what percentage of enrollees were covered by review requirements. Thus, a plan with 1 percent of its enrollees covered would rank the same as one with 90 percent.

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Page 99 The research on private sector second-opinion programs is even less likely than the research on other prior review techniques to include comparisons with groups not subject to second-opinion requirements. Moreover, refinements in the use of second opinions, particularly prior screening of cases for medical necessity before referral, are too new for much data to have accumulated on their effects; and it is not clear from discussions with those involved in such programs that the second opinions themselves are expected to have a major cost-containing effect. The major expectation is that referred patients will have more information on which to base their decisions about whether or not to have the recommended surgery. During its site visits and other discussions of utilization management, the committee found relatively little interest in second-opinion programs and very mixed opinions about their cost-effectiveness. To some degree, this may reflect the inclination of purchasers and vendors to pay more attention to newer rather than older programs. Discussions with purchasers, however, suggest that programs that include no screening prior to mandatory referral for a second opinion are increasingly seen as wasteful. In sum, the committee felt that a thorough, independent investigation of traditional second-opinion programs was not a high priority. The following reflects its assessment of such programs: • Both voluntary and mandatory second-opinion programs may enhance consumer knowledge and affect some patient decisions about whether to have surgery. The effect may be to encourage surgery in certain cases (for the reluctant patient who gets a confirming opinion) and discourage it in others (for the patient who gets a strong nonconfirming opinion). • Compared with voluntary programs, mandatory programs tend to show much higher rates of confirmation; that is, the second opinion confirms the first opinion or, more specifically, does not reject some form of surgical intervention. • Voluntary second-opinion programs generally have much lower rates of participation by patients than do mandatory programs that include penalties if a patient does not seek a second opinion. • Evidence on the net impact of second-opinion programs on utilization and costs is less supportive of the conclusion that it contains costs than is the (also imperfect) evidence about other utilization management techniques. Weaknesses in the Evidence on Effects of Prior Review Because prior review programs have been developed and implemented in an operational rather than a research context, rigorous evaluation has not been a high priority for most organizations, and studies by outside researchers have been limited. One consequence is that much of the

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Page 100 evidence about the impact of programs is of marginal value in answering the major questions that are asked by clients, policymakers, and other interested parties. All available studies suffer one or more of the following deficiencies in research design and measurement: • absence of comparisons between groups with and without prior review that use such methodologies as random selection of study participants and random assignment of participants to program and nonprogram groups; • use of short time series (for example, 1 year of preprogram data and 1 or 2 years of postimplementation data) that do not allow confident identification and assessment of preexisting trends, cyclical patterns characteristic of the health insurance industry, and long-term effects of utilization management; • heavy reliance on inpatient utilization data with little or no evidence about (1) potentially offsetting changes in inpatient prices, (2) shifts in noninpatient prices, utilization, and overall costs, (3) changes in net benefit costs, and (4) savings relative to program costs; • failure to control statistically or otherwise for nonprogram variables (for example, other cost-containment activities, scope of benefits, shifts in group composition, and market area characteristics) that may affect utilization and costs; • knowledge by evaluators of whether data are from groups with or without utilization management; • absence of comparisons of the relative impact of different program elements (for example, preadmission review versus continued-stay review) or alternative program designs (for example, greater emphasis on physician rather than nurse judgment); and • failure to specify conditions associated with better or worse program results (for example, type of client and the supply of local health care resources). Most importantly, all of the studies reviewed above confine their focus to utilization patterns and costs to the purchaser. The committee found no empirical research on the possible effects of private sector prior review programs on quality of care, patient out-of-pocket costs, patient convenience, patient-physician relationships, and attitudes and administrative costs of health care practitioners. (Four reports on the effects of Medicare payment and review programs are cited later in this chapter.) The appendix at the end of this chapter discusses methodological issues in more detail. The committee did not study actuarial estimates of the impact of prior review that insurers and others employ in setting premiums or projecting benefit expenditures. Informal conversations suggest that actuaries may initially have estimated a 5-10 percent lower premium for companies that

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Page 101 adopted prior review. Rate reductions of 1-5 percent are said to be more common for groups that have only recently adopted prior review. One explanation for the changes in estimates is that actuaries initially underestimated how much of the reduction on inpatient use would be offset by higher inpatient prices and higher outpatient use and prices and thus set premiums too low. This is consistent with the recent report of the Prospective Payment Assessment Commission (1989) which concluded that Medicare prospective payment and other public and private cost-containment efforts have reallocated health expenditures but not slowed their growth. Another explanation for changes in actuarial estimates of prior review savings is that late-adopting groups have already gained some benefit from changes in physician behavior produced by the prior review (and other) programs implemented earlier by other purchasers. Also proposed is an analog of "herd immunity," in which coverage of one-half to three-quarters of the insured population by prior review may produce "spillover" protection for the rest. There has been no systematic test of this proposition. However, one study of an insurer's program to promote inhospital ambulatory surgery in the late 1970s and early 1980s suggested spillover effects for other payers whose inpatient surgery rates also began to drop. Nonetheless, the decrease for the sponsoring insurer started earlier and the gap in rates persisted over several years (Lerner et al., 1983a). The committee notes that many reports on the impact of prior review come from those with an interest in positive results. This is most obvious for the organizations that provide prior review services. However, even union and corporate benefits managers may incline evaluations toward favorable results—unions may do this because utilization management can be seen as an alternative to benefit cutbacks and benefits managers may do this because good results can make them look effective to higher levels of management. Some purchasers of prior review services, recognizing that utilization management firms may be biased or may lack expertise in evaluation, ask that data be turned over for analysis by outside consultants. The study committee did not have access to the reports of these outside consultants. Effects of Prior Review on Specific Parties Although empirical data are very limited, it is important to consider how private sector prior review programs may positively or negatively affect different parties—enrollees of benefit plans, health care practitioners and institutions, and purchasers. The following discussion is based on committee members' experiences, testimony at the committee's June 1988 hearings, site visits of the committee members, a roundtable discussion

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Page 108 voluntary guidelines for private review (American Hospital Association, 1989). The last three organizations already have cooperated on guidelines for the conduct of utilization management programs (American Medical Association, 1989a). There are eight guidelines specific to prior review. • The medical protocols as well as other relevant medical issues used in prior authorization programs should be established with input from physician advisers selected by the health benefit plan. • Prior authorization programs may be conducted on a targeted review basis rather than attempting to prereview all services eligible for coverage. • All preadmission review programs should provide for immediate hospitalization of any patient for whom the treating physician determines the admission to be of an emergency nature, so long as medical necessity is subsequently documented. • In the absence of any contractual agreement between physician and health benefit plan, the responsibility for obtaining prior authorization required by a claims administrator should be that of the enrollee. • The claims administrator and employee benefits manager should work together to alert enrollees to the need to be aware of and to inform the physician of any prior authorization requirements applying to their insurance coverage. • In cases where a claims administrator requires prior authorization, the claims administrator should respond promptly and efficiently to requests for authorization. A physician or a patient should receive a response within 2 business days. • In any instance where authorization is questioned on the basis of medical necessity, the attending physician should be able to review medical necessity with the physician adviser representing the claims administrator. • To the extent that prior authorization programs are administered efficiently with minimal disruption to the provision of medical care, additional payment to physicians for complying with prior authorization requirements should not be necessary. Effects of Prior Review on Purchasers Employers are the principal purchasers of private sector review programs and are affected by its application in a variety of ways. Initial employer reaction to prior review has been positive (Jennings, 1987; Vibbert, 1989). They feel it has had some impact on benefit costs and has improved the value they receive for their expenditures. On the other hand, the fact that prior review programs seem not to have shifted the basic slope of the cost curve intensifies employers' frustration with rising expenditures. This frustration over basic trends

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Page 109 repeats employers' experiences with other cost-containment tools, including increased employee cost-sharing and alternative delivery systems (such as HMOs). In general, the adoption of prior review programs by private purchasers has reinforced their long-standing dissatisfaction with data about the health services they are buying and has strengthened their efforts to obtain better information. Most utilization management firms are under heavy pressure to report more utilization and cost data and present more credible analyses of program impact. Some employers are concerned that prior review exposes them to a new risk of liability, particularly if they administer the programs directly. The case of Wickline v. California held that third-party payers "can be held legally accountable when medically inappropriate decisions result from defects in the design or implementation of cost containment mechanisms . . ." (emphasis added) (see the paper by William A. Helvestine in Appendix A of this report for further discussion). Employers often seek protection from such liability in their contracts with review organizations, but the latter are understandably reluctant to promise to indemnify their clients against damages. As purchasers move from retrospective to prospective review of care, relationships with employees may change. Many employers recognize the need to enlist the employee in their efforts to hold down costs. Some have developed programs that teach employees about making informed health care decisions and assist them with decisions about the course and place of treatment. At a minimum, most prior review programs increase the administrative responsibilities of employees Purchasers of prior review services also may find themselves in a different relationship with providers. Until recently, purchasers generally paid for whatever treatment the doctor ordered. Now, in their new role as managers of utilization and armed with data on the prices and practices of community providers, they are using their purchasing power to influence community practice standards. The committee noted that purchaser decisions are shaped by many factors. For example, although the committee is unaware of any systematic research on this point, different industries and different firms within industries appear to have different norms governing their decisions on employee benefits. For example, in a roundtable discussion with benefits managers from several large companies that the Institute of Medicine held in December 1988, some firms were portrayed as slow to adopt prior review out of concern that they would antagonize their white-collar employees. In areas where workers are in short supply, less restrictive health benefits may help in recruiting new workers but may be of little significance where unemployment is high. Employers that have multiple locations and small

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Page 110 work sites may find it difficult to locate and coordinate effective utilization management services. Overall, employer reaction to prior review has been positive, but the long-term picture is uncertain. This uncertainty has two aspects, neither empirically documented. First, to the extent that prior review and other programs influence provider practices throughout the community regardless of a particular benefit plan's provisions, some employers may consider dropping programs in the hope that the spillover benefit from other employers' programs will suffice. Others may feel that prior review has had all the impact it is going to have, that changes in practice patterns are now entrenched, and that they do not need to be reinforced by continued application of these techniques. Second, employer discontent with the reemergence of sharply rising benefit expenditures may lead some to make drastic changes in their funding of employee benefits. For example, if employers switch from a defined benefit to a defined contribution program that limits their yearly increase for health benefits to a level they can control, regardless of what happens to health care costs, then their interest in various cost-control programs may diminish. Defined contribution programs have become common for employee pension plans but are not widespread for health benefit plans. Conclusion Although the evidence on prior review is generally not rigorous, it does tend to be consistently positive about the short-term effects of prior review on hospital use and expenditures. It focuses almost entirely on reviews of the site, timing, and duration of care rather than on the medical necessity of specific procedures, because the latter emphasis is too new to have produced adequate data for evaluation. The impact of prior review techniques on access and quality of care has not been assessed systematically, but no serious suggestion of negative consequences has come to the committee's attention. Qualitative assessments of the impact of utilization management on patients, providers, and purchasers suggest the potential for both positive and negative effects. The committee recognizes that rigorous evaluations are expensive and difficult. In the clinical arena itself, rigorous evaluations of the impact of specific medical services are the exception, not the rule. Furthermore, the committee recognizes that many health benefit programs are adopted, maintained, or discontinued by private and public decision-makers on the basis of evidence as weak as or weaker than that available for utilization management. Nevertheless, the committee is concerned about the limited commitment to systematic evaluation of utilization management. Its recommendations on this point are contained in Chapter 6.

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Page 111 Appendix Some Methodological Issues in Assessing the Effects of Utilization Management Programs An exhaustive review of methodological problems in evaluating the effects of utilization management is beyond the scope of this report. However, a brief overview of key issues illustrates the array of difficulties faced by purchasers, program managers, policymakers, and others interested in the consequences of utilization management. This overview includes a table describing typical measures of program impact and their limitations (Table 4-2). Claims Data Health care cost and utilization data based on claims submitted by providers or patients may suffer from a variety of defects (Wennberg, 1987). The claim form itself may be improperly designed to capture the needed information about the site of care, type of care, or diagnosis in an unambiguous form. Information submitted on the claim form may be inaccurate because of errors in medical records, transcription mistakes, imprecise diagnosis or procedure codes, and deliberate provision of false information (for example, recording a medical problem for an examination undertaken purely for screening purposes). Claims information from different companies may be difficult to merge for multigroup studies. Claims data alone are not sufficient to establish the severity of illness for purposes of comparison over time or across groups. Even when data are accurate, they may not be available until months or even years after care has been provided. This limits the efforts of program managers to identify problems and adjust programs in a timely fashion. Also, individual claims data may be accurate but limited in scope, often not reflecting an entire episode of care for a patient, particularly if the patient uses some care for which claims are not recorded (for example, care covered by a spouse's plan or care from a noncovered provider). This means that actual utilization and costs per episode of care may be underestimated for alternative modes of treatment. Information that tracks multiple episodes of care for an individual is even more limited, and the patient-level links between prior review decisions and subsequent care are typically not examined. Group Data Important characteristics of employee groups and individuals covered by utilization management may be unmeasured or measured inadequately.

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Page 112 TABLE 4-2 Measures of Prior Review Impact ACTIVITY DATA   Number of admissions or days requested   Number of requests approved, negotiated, or denied   Number of requests referred to physician reviewers   Number of denials appealed and upheld or not upheld   Number of admissions or days averted (requested minus approved)   Number of second opinions obtained and confirmed or not confirmed   Number of nonconfirming opinions not followed by surgery   Source   Review organization   Comments   • Do not measure health services use or costs   • Are helpful in assessing workload and checking some administrative practices   • Are relatively simple to collect   •·Can be manipulated by utilization management organizations to project unrealistically favorable results   •·Can be manipulated by providers who request more days than are really wanted   •·Cannot tap "sentinel effect" (that is, admissions discouraged with no prior authorization approval sought)   •·May not be matched to actual utilization (that is, may ignore days or admissions approved but not used; days or admissions denied but approved upon appeal or after an emergency admission; for second opinion. may ignore individuals encouraged by second opinion to get surgery when they otherwise would not have) INPATIENT AND OUTPATIENT UTILIZATION   Inpatient hospital days per 1,000 covered individuals   Inpatient admissions per 1,000 covered individuals   Average length of stay   Outpatient medical claims per 1,000 covered individuals   Inpatient and outpatient surgical claims per 1,000 covered individuals   Total outpatient claims per 1,000 covered individuals   Physician office visits per 1,000 covered individuals   Source     Claims data     Benefit plan enrollment statistics   Comments   •·Can correct some limitations of activity data   •·Are needed to help interpret changes in site of care, plan costs, and other variables   •·May involve questionable denominators for rates if the number of covered dependents is unknown or is estimated by using outdated multipliers for family size and if additional coverage for working spouses is not accounted for   •·May count only hospital outpatient facility services and not visits to physician offices or other sites   •·Are generally not aggregated to show entire episodes of care (Table continued on next page)

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Page 113 (Table continued from previous page) TABLE 4.2 (continued) INPATIENT AND OUTPATIENT COSTS   Inpatient payments per admission or day of care   Inpatient payments per 1,000 covered individuals   Outpatient payments (medical, surgical, or total) per claim   Outpatient payments per 1,000 covered individuals   Payments for inpatient or outpatient physician services per 1,000 covered individuals   Source     Claims data   Hospital cost reports   Comments   •·Are useful for assessing changes in hospital payments, which have traditionally accounted for the largest share of total outlays   •·May not adjust for differences in costs for days of care averted earlier versus later in a hospital stay   •·May not adjust for fixed costs that are eventually absorbed by most retrospective cost- or charge-based payment systems   •·Do not adjust for severity of remaining admissions and days of care   •·May have same problems with denominators of rates as utilization statistics PROGRAM SAVINGS AND COSTS   Benefit payments per 1,000 covered individuals   Premium per covered individual   Administrative charge per contract (or other basis)   Ratio of review program costs to program savings   Benefit payments less program costs per 1.000 covered individuals   Number of hospital days averted and savings   Number of surgeries averted and savings   Source   Claims data   Plan contract, enrollment, and premium data   Review organization   Comments   •·Are needed to assess trends in overall benefit costs and to assess net savings   •·May not measure true costs to the supplier, depending on market strategy of utilization management company (importance of this issue depends on evaluator's objectives)   •·May not reflect costs of alternatives to admissions or surgeries or costs from care only temporarily averted   •·May exclude noncontractual costs to client for explaining program to employees, etc.   •·Do not include any costs or savings or other effects for providers or patients

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Page 114 If different employee groups or subgroups vary in their exposure to benefit claims in unmeasured ways, then per-employee or per-covered-individual use and cost comparisons may be misleading (see also Table 4-1). The scope of health plan benefits for a group can have an important impact on the use of services but is often unmeasured. Age, geographic location, and many other factors can also have an impact. A fundamental inadequacy of many data bases is imprecise specification of the size of the groups covered by a health plan. Although the number of covered employees (contracts) is generally known, the number of covered dependents may only be estimated by "family" factors that ignore drops in average family size and increases in alternate primary coverage for spouses. This imprecision makes the denominators for utilization and expenditure rates problematic. Plans such as HMOs that require explicit enrollment information are in a stronger position to calculate use and payment rates. In addition, although plan managers are making strenuous efforts to coordinate benefits (that is, see that care for dependents with primary coverage from another source is not improperly reimbursed under their employee's contract), the extent of such duplicate coverage is often not known in the aggregate (Lerner et al., 1983b; Luft, 1981)). Important information about the health status of group members is often tapped in only the most rudimentary way, if at all. This has been a major controversy in comparisons of HMOs and fee-for-service groups. Some claim that HMOs are getting healthier enrollees, and thus price and use comparisons are misleading (Luft, 1987; Scheffler and Rossiter, 1985). Program Data Information about the prior review program often is limited. Programs may theoretically go into effect on a specified date, but actual implementation may lag considerably. Differences in program quality, scope, techniques, and other characteristics may not be described, much less assessed, in reports on prior review program effects. Savings Calculations Many utilization management organizations estimate savings by taking the number of hospital (lays requested by a physician or hospital, subtracting the number of hospital days not authorized, and multiplying the result by the average cost of a hospital day. This approach has many flaws—the days requested may be overstated by providers who are trying to game the program, the number of days approved may be exceeded without subsequent adjustment, and the average cost of a hospital day may overstate the cost

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Page 115 of days avoided at the end of a hospital stay when care is normally less intense than it is earlier in the stay. Other Interventions Prior review programs are often one of many efforts to deal with escalating benefit costs. Their implementation frequently coincides or overlaps with initiatives to collect more detailed and accurate information about use, costs, and covered individuals. Although such initiatives typically improve subsequent data, earlier data generally cannot be supplemented to provide a comparable time series for analysis. In addition, retrieval of preprogram data is often time-consuming and expensive, particularly when data are being aggregated for several groups and must be obtained from a separate claims payer. Utilization management also may be introduced simultaneously with a major redesign of benefits (for example, increased cost sharing) and the addition of a choice among multiple health plans. This complicates efforts to distinguish any effects due independently to, or interactively with, utilization management. The study by Scheffler et al. (1988) attempts to distinguish the effects of Blue Cross cost-containment programs from each other and from the Medicare prospective payment system. Any such effort is inevitably plagued with problems in combining data from multiple sources, evaluating the accuracy of these data, measuring the intensity and quality of programs, and adjusting for statistical characteristics and quirks of the data. Medical Care Prices Costs are a function of both utilization and unit prices of services. If inpatient prices are better controlled than outpatient prices, then savings from shifting care may be illusory. Likewise, if hospitals increase their prices to cover fixed costs in the face of reduced occupancy and if payers cannot limit their exposure to these increases because they pay uncontrolled hospital charges or costs, the reduced inpatient use may not bring a net savings for purchasers of care. If some payers can limit their exposure to these increases, as Medicare and some other payers do, then costs for payers without such protection may increase even more. Noneconomic effects Sometimes claims data can be explored in an effort to assess possible effects of prior review programs on quality of care. For example, emergency admissions, readmissions, admissions following outpatient surgery, length

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Page 116 of home or nursing home care, and other events can—with varying degrees of difficulty—be identified and links with prior review decisions can be attempted. PROs track some of these events in an effort to monitor the effects of prospective hospital payment. Some utilization management organizations are trying to build data systems that allow patient histories to be easily retrieved and analyzed. More direct information on health status and health outcomes is more expensive and difficult to obtain. Medicare, other purchasers, and other organizations, such as the Joint Commission on the Accreditation of Health Care Organizations, are actively working on quality assurance programs to identify potential quality problems at various sites of care. Much remains to be done. The Institute of Medicine is engaged in a major project to help design a quality assurance program for Medicare (Institute of Medicine, 1989). Some employers and utilization management organizations survey employees to assess their reactions to utilization management programs. This is more the exception than the rule. Possibly, the absence of volunteered complaints from employees is taken as evidence that employees are not unhappy with the program. They are not adverse to complaining about other aspects of benefit plan administration, for example, retrospective claims denials, slow payment of claims, and confusing explanations of coverage. Systematic assessments of provider problems and attitudes are even less common than checks on beneficiary attitudes. References Allen, Harris, and Khandker, Rexaul, ''Aetna's HEALTHLINE Program: Fourth Quarter, 1987 Update,'' Unpublished paper, September 30, 1988. American College of Physicians, Clinical Efficacy Assessment Project, Philadelphia, PA, October 1986. American Hospital Association, Private Utilization Review, State Issues Forum Monograph Series, Washington, DC, August 1989. American Medical Association, Guidelines for the Conduct of Prior Authorization Programs, Chicago, 1989a. American Medical Association, Summary Report: 1988 Payer Accountability Monitoring Survey, Chicago, May 1989b. Anderson, Suzanne, "Hospitals Can Improve Cash Flow by Managing Preauthorizations," Healthcare Financial Management, December 1988, pp. 56-59. Blue Cross of Greater Philadelphia and Pennsylvania Blue Shield, Extending the Influence Beyond the Source: Community Data Report 1987, Philadelphia, June 1987. Blue Cross of Greater Philadelphia and Pennsylvania Blue Shield, State of the Art Health Care Management: Community Data Report 1986, Philadelphia, July 1986. Carroll, Robert P., "The Problem with PROs Is Hard Heads Like Me," Medical Economics, December 19, 1988, pp. 103-115.

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Page 117 Feldstein, Paul J., Wickizer, Thomas M., and Wheeler, John R. C., "The Effects of Utilization Review Programs on Health Care Use and Expenditures," New England Journal of Medicine, May 19, 1988, pp. 1310-1314. (See also Zwarenstein, M. B., et al., letter to the editor and E J. Feldstein response, New England Journal of Medicine, October 17, 1988, p. 1158.) Findlay, Stevan, "Looking over the Doctor's Shoulder," U.S. News & World Report, January 30, 1989, pp. 70-73. Fitzgerald, John F., Moore, Patricia S., and Dittus, Robert S., "The Care of Elderly Patients with Hip Fracture," New England Journal of Medicine, November 24, 1988, pp. 1392-1397. Getson, Jacob, "Reforming Health Care Delivery: The Massachusetts Blues' Role," Business and Health, February 1987, pp. 30-35. Health Data Institute, Inc., "OPTIMED Managed Care Program," Lexington, MA, April 1988. Imperiale, Thomas, et al., "Preadmission Screening of Medicare Patients," Journal of the American Medical Association, June 17, 1988, pp. 3418-3421. Institute of Medicine, "Designing a Strategy for Quality Review and Assurance in Medicare: Twelve Month Update," Washington, DC, February 1989. Jennings, Susan, "Survey of PEW Corporate Fellows in Health Policy," Unpublished paper, Boston University, December 1987. Kauer, Robert, "Evaluating a Corporate Health Care Utilization Review Program: The Case of Deere & Company," Working Paper No. 013, Cleveland, OH, Health Systems Management Center, Case Western Reserve University, December 1983. Lerner, Monroe, Salkever, David S., and Davis, Leonard, "Evaluation of Program to Move Care for Certain Surgical Procedures to an Ambulatory Care Setting," Paper presented at the annual meeting of the American Public Health Association, Dallas, November 14-17, 1983a. Lerner, Monroe, Salkever, David S., and Newman, John E, "The Decline in Blue Cross Plan Admission Rates: Four Explanations," Inquiry, Summer 1983b, pp. 103-113. London, Alan E., and Anderson, Richard A., "Provider and Reviewer Speak Out on Utilization Management," Federation of American Health Systems Review, July/August 1988, pp. 36-40. Luft, Harold, "Divergent Trends in Hospitalization: Fact or Artifact?" Medical Care, October 1981, pp. 979-994. Luft, Harold, Health Maintenance Organizations, New Brunswick, NJ: Transaction Books, 1987. Mayo Clinic, "The 'Cost' of Effective Utilization Review Programs," Statement submitted to the Institute of Medicine Committee on Utilization Management, May 1988. McIlrath, Sharon, "AMA, Rand Corp. Plan Joint Development of Practice Guidelines," American Medical News, October 28, 1988, pp. 2, 27. Meyer, Harris, and Page, Leigh, "New Era in Utilization Review," American Medical News, December 9, 1988, pp. 1, 42-45. Michaelson, Leslie, Medical Review System, Santa Monica, CA: Value Health Sciences, Inc., 1988. O'Donnell, Peter S., "Controlling Costs Under a Fee-for-Service Plan," Business and Health, March 1987, pp. 38-41. Physician Payment Review Commission, Annual Report to Congress, Washington, DC, March 1988. Physician Payment Review Commission, Annual Report to Congress, Washington, DC, 1989. "Preadmission Review Cuts Hospital Use," Hospitals, August 1, 1984, pp. 54-55. Project HOPE, A Study of the Preadmission Review Process, Prepared for the Prospective Payment Assessment Commission, Washington, DC, November 1987. Prospective Payment Assessment Commission, Medicare Prospective Payment and the American Health Care System: Report to Congress, Washington, DC, June 1989. Rutgow, Ira M., and Sieverts, Steven, "Surgical Second Opinion Programs," in Socioeconomics of Surgery, Ira Rtugow, ed., St. Louis: C. V. Mosby Company, 1989.

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Page 118 Sager, Mark A., et al., "Changes in the Location of Death After Passage of Medicare's Prospective Payment System," New England Journal of Medicine, February 16, 1989, pp. 433-439. Scheffler, Richard M., and Rossiter, Louis E, eds., Biased Selection in Health Care Markets, Advances in Health Economics and Health Services Research, Vol. 6, Greenwich, CT: JAI Press, Inc., 1985. Scheffler, Richard M., Gibbs, James O., and Gurnick, Deborah, The Impact of Medicare's Prospective Payment System and Private Sector Initiatives: Blue Cross Experience, 1980-1986, HCFA Grant No. 15-C-98757-5-01, Berkeley, University of California, July 1988. Schwartz, J. S. "The Role of Professional Medical Societies in Reducing Variations," Health Affairs, Summer 1984, pp. 90-101. Service Employees International Union, "Utilization Review and Case Management in Employee Benefit Plans," Washington, DC, July 1988. Vibbert, Spencer, "Is Utilization Review Paying Off?" Business and Health, February 1989, pp. 20-26. Walker, Allison, "Findings of Physician Focus Groups," Unpublished paper prepared for the Institute of Medicine Committee to Design a Strategy for Quality Review and Assurance in Medicare, Washington, DC, January 1989. Wennberg, John, "Use of Claims Data Systems to Evaluate Health Care Outcomes," Journal of the American Medical Association, February 20, 1987, pp. 933-936. Wickizer, Thomas M., Wheeler, John R. C., and Feldstein, Paul J., "Does Utilization Review Reduce Unnecessary Hospital Care and Contain Costs?" Medical Care, June 1989, pp. 632-647. Zuckerman, Stephen, "Medicaid Hospital Spending: Effects of Reimbursement and Utilization Control Policies," Health Care Financing Review, Winter 1987, pp. 65-77.