Physician prescribing decisions: the effects of situational involvement and task complexity on information acquisition and decision making.

This research utilized conjoint analysis and an analysis of information acquisition to examine the effects of situational involvement and task complexity on physician's decision-making process. The predictive accuracy of the linear model in predicting drug choice across situations was also assessed. A contingency model for the selection of decision strategies was used as a framework in the study. A sample of forty-eight physicians was asked to indicate their preferences and choices for hypothetical anti-infective drugs. Situational involvement was manipulated by telling physicians in the experimental group via the written scenario to assume that his/her decision would be reviewed and evaluated by peers and (s)he would be asked to justify drug choice. Task complexity was manipulated by varying the number of drug alternatives in a choice set. Results of the study indicated that physicians shifted from using compensatory to noncompensatory decision-making processes when task complexity increased. The effect of situational involvement on the decision-making process was not supported. However, physicians in the two groups were found to differ in choice outcomes and the attention given to specific drug attribute information. Finally, the linear model was found to be robust in predicting drug choice across contexts.

HMO marketing and selection bias: are TEFRA HMOs skimming?

The research evidence indicates that health maintenance organizations (HMOs) participating in the Tax Equity and Fiscal Responsibility Act of 1982 (TEFRA) At-Risk Program tend to experience favorable selection. Although favorable selection might result from patient decisions, a common conjecture is that it can be induced by HMOs through their marketing activities. The purpose of this study is to examine the relationship between HMO marketing strategies and selection bias in TEFRA At-Risk HMOs. A purposive sample of 22 HMOs that were actively marketing their TEFRA programs was selected and data on organizational characteristics, market area characteristics, and HMO marketing decisions were collected. To measure selection bias in these HMOs, the functional health status of approximately 300 enrollees in each HMO was compared to that of 300 non-enrolling beneficiaries in the same area. Three dependent variables, reflecting selection bias at the mean, the low health tail, and the high health tail of the health status distribution were created. Weighted least squares regressions were then used to identify relationships between marketing elements and selection bias. Subject to the statistical limitations of the study, our conclusion is that it is doubtful that HMO marketing decisions are responsible for the prevalence of favorable selection in HMO enrollment. It also appears unlikely that HMOs were differentially targeting healthy and unhealthy segments of the Medicare market.