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HMO penetration, competition, and risk-adjusted hospital mortality - Articles - Statistical Data Included

Health Services Research - December 1, 2001

Quality of care provided by HMOs is of major concern to the public and its representatives who are charged with formulating health care policy. Recently these concerns have translated into legislative initiatives at both the federal and state levels, often labeled "patients' bills of rights." These concerns focus on the quality of care HMOs provide to their enrollees. Left out of the public debate to date is the potential effect that HMOs may have on the quality of care provided to non-HMO patients. The continuing increase in HMO enrollment, HMOs' dominance in many local health care markets, and HMOs' effect on health care expenditures through deceleration of historical increases in hospital and physician costs (Melnick, Zwanziger, and Bradley 1989; Melnick et al. 1992; Simon and Born 1996; Zwanziger and Melnick 1996, 1988), as well as premiums of non-HMO traditional insurance plans (Baker and Corts 1996), suggest the possibility for a quality spill-over effect, an HMO effect on the quality of care of non-HMO patients. HMOs may affect the care non-HMO patients receive through the following mechanisms.

HMOs often attempt to influence local practice patterns through financial and administrative programs. For example, during the 1980s most of the cost effect that HMOs achieved was the result of changes in practices related to hospital utilization: declines in both hospital admission rates and lengths of stays (Miller and Luft 1994). As practice styles change they are likely to apply to all patients, even if initially the changes emerge in response to HMO incentives. Tussing and Wojtowycz (1994) found that cesarean section rates in New York State declined among HMO enrollees and that a spill-over effect to the fee-for-service population occurred. Another example is care for patients with diabetes. The Health Plan Employer Data and Information Set (HEDIS) (Epstein 1995) HMO quality report card publishes rates of diabetic retinopathy screening, a component of the diabetes care guidelines (American Diabetes Association 1995). In response many HMOs implemented programs designed to increase knowledge of diabetes ca re guidelines among primary care physicians. As physicians become more knowledgeable they are likely to implement the guidelines for all of their patients, not only their HMO patients, as was found by Herbert, Maciejewski, and McBean (1999).

Limiting local health systems' resources is another way in which HMOs may affect the care of both HMO and non-HMO patients. HMOs have been successful in negotiating lower prices with providers, leading to lower rates of increase in costs (Simon and Born 1996; Zwanziger, Melnick, and Bamezai 1994). Furthermore, they limit the ability of providers to cost shift, a practice that historically allowed providers to mitigate the revenue effects of cost-containment efforts (Morrisey 1993). Such financial pressures limit the ability of providers to invest in improved technologies and other quality-enhancing strategies. For example, studies have found that increases in HMO market penetration are associated with a decrease in hospital beds (Chernew 1995) and availability and use of magnetic resonance imagery (MRI) (Hill and Wolfe 1997; Baker and Wheeler 1998). To the degree that such resources (e.g., the latest MRI machine) are shared by all patients, providers' inability to purchase them would have an effect on the car e of all patients, not just those enrolled in HMOs.

Channeling beneficiaries to high-quality (or low-quality) providers may also affect all patients. HMOs often direct all of their patients to a subset of providers in their market area. The impetus for such selective contracting is the market power it offers HMOs in price negotiations. A potentially unintended result is that HMOs may be taxing the capacity of the providers they contract with, making them unavailable to other patients. If HMOs' contracting practices result in panels that include the best (or worst) providers in the area, the quality choices remaining to non-HMO patients are constrained (Mukamel, Mushlin, Weimer, et al. 2000). This phenomenon is likely to be observed mostly with respect to specialized physicians, such as cardiac surgeons, rather than with respect to hospitals, which typically operate at sufficiently low occupancies that HMOs are not likely to tax their capacities.

Finally, HMOs place burdens, both financial and administrative, on providers. Many of the methods used to control utilization and costs impinge on provider autonomy and increase their costs (e.g., preauthorization and utilization review, bonuses and penalties, and risk transfer through capitation) (Gold, Hurley, Lake, et al. 1995). As HMOs become dominant in local markets, providers may not be able to maintain the volume of business they desire while still avoiding HMO patients. Some may choose to leave such markets. It is possible that the higher-quality providers with the better reputations are more mobile and more likely to exit markets dominated by HMOs. Such selective exit would affect the care of both LIMO and non-HMO patients.

Most studies to date focus on the quality of care that HMOs provide to their own enrollees (Ware, Brook, Rogers, et al. 1986; Chernew, Scanlon, and Hayward 1998; Miller and Luft 1994; Sullivan 1999). These studies offer mixed evidence, suggesting that the care HMO enrollees receive is of variable quality when compared with fee-for-service patients. Little is known about the effect of HMOs on overall quality level in markets they dominate. A study by Shortell and Hughes (1988) found that increased HMO penetration measured at the state level is associated with poorer inpatient mortality outcomes for 16 conditions, suggesting a negative HMO effect on overall quality. The analyses presented here evaluate the quality spill-over effect that HMOs may have by investigating the association between HMO penetration and competition with outcomes for fee-for-service Medicare patients for a large national sample.

DATA AND METHODS

Evaluating Hospital Quality: Excess Mortality

Risk-adjusted mortality rates have in recent years been used to evaluate quality of care in hospitals (HCFA 1992; Pennsylvania Healthcare Cost Containment Council 1992; New York State Department of Health 1997). These measures allow an assessment of excess mortality after accounting for patient risk factors that hospitals cannot control. Although the use of risk-adjusted outcomes to identify outliers is controversial (Davis, Iezzoni, Phillips, et al. 1995; Iezzoni 1998; Spector and Mukamel 1998), these measures have been shown to capture systematic differences in quality across all hospitals. They were found to be correlated with other measures of quality such as explicit and implicit chart reviews (Keeler, Rubenstein, Kahn, et al. 1992) and results of peer review (Hartz, Gottlieb, and Kuhn 1993).

In this study we used the risk-adjusted mortality measures developed by the Health Care Financing Administration (HCFA 1992). These measures are based on individual patient-level hazard models, which predict mortality hazard conditional on individual risks. Individual risk factors include patient age; gender; specific diagnoses and comorbidities; admission source; emergency or elective admission; and patient risk group based on hospitalizations during the preceding six months. These hazard models are then used to predict for each patient the probability of death within 30 days of admission. The average probability for all patients treated at the hospital is the predicted mortality rate for the hospital, conditional on its patient mix and assuming average quality. The deviation between this predicted rate and the observed rate (i.e., excess mortality) is a measure of hospital quality. The HCFA measures have been validated through comparisons with errors in care found by peer-review processes (Hartz, Gottlieb, and Kuhn 1993) and risk-adjustment models based on extensive physiologic and clinical data (Krakauer, Bailey, and Skellan 1992). The latter study found that the correlation of hospital ranking based on the HOFA data and the clinical data is .91 and that models relating hospital characteristics to these quality measures give similar results.

Data

The study included 1,927 hospitals in 134 metropolitan statistical areas (MSAs) with five or more acute-care hospitals in 1990. (1) For each MSA we obtained data on all nonfederal, acute-care, short-term hospitals in operation during 1990.

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