The authors apply a conditional choice model to simulate the results of patient and physician choices of hospitals for a specific surgical procedure in response to improvements in quality or changes in charges. The model includes all zip code areas and relevant hospitals in a large metropolitan area and estimates the impact on admissions at each hospital. It can be used to estimate both the impact of decisions by a given hospital and the potential responses of competitors, as well as the effects of selective contracting with hospitals by certain payors.

If a person were faced with a medical problem requiring a risky operation, it would be natural, even prudent, for that person to want to know the comparative experience with the procedure (as shown by annual volumes) and the relative success with which area hospitals perform the operation. Unfortunately, such information is often impossible for the average consumer or even the average employee benefits manager to obtain.

Various studies have shown that certain complicated procedures are volume-sensitive. That is, when hospitals perform higher volumes of the procedure, morality rates and charges decline. This relationship is said to reflect experience and economies of scale. While some argue that such quality and cost information can easily be misinterpreted, consumers deserve as much information as can be made available to help them determine where to obtain care. Unfortunately, hospitals believe that information regarding their quality and efficiency is proprietary and should not be released, and they have been quite successful at guarding hospital-specific information.

In recent years, much information has been provided to the public and to physicians about hospital quality measured in terms of patient outcomes. To examine if, before these public data releases, quality influenced the attractiveness of a hospital to referring or admitting physicians and to patients, we estimated the influences of quality, charges, ownership, and distance on the choice of hospitals for patients with seven surgical procedures and five medical diagnoses in hospitals in three geographic areas in California in 1983. Greater distance and public or proprietary ownership consistently reduced the likelihood of selection while medical school affiliation increased the likelihood of selection. For five of seven surgical procedures and two of five medical diagnoses, hospitals with poorer than expected outcomes attracted significantly fewer admissions. The reverse held for two surgical procedures and one medical diagnosis. The results suggest that quality played an important role in choices among hospitals even before explicit data were widely available.

This study used 1982-1986 data on 262 private community hospitals to evaluate the effects of selective contracting for inpatient services by California's Medicaid program. Selective contracting by Medicaid significantly reduced the rate of inflation in average costs per admission and per patient day, while slightly increasing average lengths of patient stays. Private sector contracting also reduced cost inflation rates significantly and caused small, non-significant, reductions in lengths of stays. Hospital savings in 1986 due to Medicaid selective contracting were $836 million, 7.6% of what hospital expenditures would have been in the absence of contracting.

It is plausible that distance, quality, and hospital charges all influence which hospital patients (and their referring physicians) choose. Several researchers have estimated conditional choice models that explicitly incorporate the existence of competing hospitals. To be useful for hospital administrators, health planners and insurers, however, estimates must be made for specific types of patients and include entire market areas. Data sets meeting these requirements have many combinations of hospitals and locations with zero patients. This raises computational difficulties with the linear estimation techniques used previously. In this paper, we use data on patients undergoing cardiac catheterization in several market areas to assess alternative estimation techniques. First, we estimate the conditional choice model with the two techniques used previously to transform the non-linear choice model. These involve using as a reference (1) a single hospital, or (2) the geometric mean of all the hospitals in the market. When there are many zeros, these techniques require extensive adjustments to the data which may lead to biased estimators. We then compare these results with maximum likelihood estimates. The latter results are substantively and significantly different from those using traditional techniques. More importantly, the linear estimates are much more sensitive to the proportion of zeros. We thus conclude that maximum likelihood estimates are preferable when there are many zeros.