Suchergebnisse

One hundred twenty‐two members (experts) of the Society for Risk Analysis completed a mailed questionnaire and 150 nonexperts completed a similar questionnaire on the World Wide Web. Questions asked included those about priorities on personal and government action for risk reduction, badness of the risk, number of people affected, worry, and probabilities for self and others. Individual differences in mean desire for action were largely explained in terms of worry. Worry, in turn, was largely affected by probability judgments, which were lower for experts than for nonexperts. Differences across risks in the desire for action, within each subject, were also determined largely by worry and probability. Belief in expert knowledge about the risk increased worry and the priority for risk reduction. A second study involving 91 nonexperts (42 interviewed and 49 on the Web) replicated the main findings for nonexperts from the first study. Interviews also probed the determinants of worry, attitudes toward government versus personal control, and protective behaviors.

ABSTRACTMany hospitals have historically used a fixed staffing policy for allocating nursing personnel, in which the daily demand in each ward is met by nurses who are permanently assigned to the specific wards. In recent years, the concept of variable staffing has been proposed as a means of increasing manpower efficiency. A variable staffing policy is one which provides for staffing adjustments to meet work load through the use of a common pool of cross‐trained nurses.In this paper, a model is formulated to evaluate the relative benefits of variable and fixed staffing policies. Results from a Monte Carlo evaluation of the model demonstrate how the hospital administrator can assess the sensitivity of savings to changes in policy and operating parameters. Several criteria which an administrator might adopt for equating levels of patient care under alternative staffing schemes are suggested and studied. The proposed method of analyzing benefits of alternative allocation procedures shows promise for evaluating policy choices in hospitals, as well as other service organizations with similar characteristics.

ABSTRACTAn important problem facing the manager of an outpatient health care clinic involves determining the best combination of services, facilities, and personnel to maximize profits and simultaneously achieve acceptable measures of the clinic's daily performance. This problem is often attacked using either linear programming or computer simulation. This paper uses a recursive optimization‐simulation approach which takes advantage of the best features of both optimization and simulation while minimizing the disadvantages of each method used alone. Results from a hypothetical setting using data from several actual settings demonstrate the value of the recursive method.

Physicians are increasingly asked to use cost‐effectiveness information when evaluating alternative health care interventions. Little is known about how the way such information is presented can influence medical decision making. We presented physicians with hypothetical screening scenarios with multiple options, varying the type of cost‐effectiveness ratios provided as well as whether the scenarios described cancer screening settings that were familiar or unfamiliar. Half the scenarios used average cost‐effectiveness ratios, as commonly reported, calculating benefits and costs relative to a no‐screening option. The other half used the preferred incremental cost‐effectiveness ratios, with each option's benefits and costs calculated relative to the next best alternative. Relative to average cost‐effectiveness ratios, incremental cost‐effectiveness information significantly reduced preference for the most expensive screening strategies in two of three unfamiliar scenarios. No such difference was found for familiar scenarios, for which physicians likely have established practice patterns. These results suggest that, in unfamiliar settings, average cost‐effectiveness ratios as reported in many analyses reported in the literature can hide the often high price for achieving incremental health care goals, potentially causing physicians to choose interventions with poor cost effectiveness.