Algorithms for the treatment of bipolar manic-depressive illness.
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Biomedical subjects
Publications and source records attributed to D A Zarin.
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The purpose of this article is to examine the consequences of and possible responses to uncertainty in psychiatric diagnosis. Uncertainty is inevitable because of the overlap in characteristics, or test results, between populations with and without a psychiatric disorder. As a result, there is never one correct method of identifying cases and noncases (i.e., case definition). In this paper principles of decision analysis and clinical epidemiology are used to develop a framework for thinking about the consequences of different diagnostic schema and for choosing among them. The framework illustrated here involves choosing an external validator, choosing a separator, and choosing a cutoff. This framework is applied to the problems facing three hypothetical researchers, and the consequences for their research of different diagnostic choices are explored. It is demonstrated how the relevance of research to clinicians and policy makers rests on the choice of the case definition process. The prevalent use of structured psychiatric interviews has not been accompanied by adequate attention to the problem of determining a diagnosis once the information is obtained. It is argued that more attention must be given to this process if we are to make optimal use of available resources for research and treatment.
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Since 1978, decision analysis consultations have been offered to help physicians manage particularly vexing clinical problems. Consultations were requested for decisions perceived as difficult because: the diagnosis was uncertain; the available therapy had uncertain efficacy or risks; the patient had a short life expectancy that limited the potential benefit of therapy; the risk of a given test or therapy was increased and the usual rules for applying the test or giving the treatment did not apply; the need for a therapeutic procedure was acknowledged, but competing risks created uncertainty about the optimal timing of the procedure; the optimal sequence of multiple necessary procedures could not be discerned readily; explicit patient input into decision-making was required; certain medical information could not be interpreted easily; and a rare, unique, or new problem was encountered. To carry out these consultations, standard techniques were used, including decision tree models, Bayes' theorem, Markov analysis, and utility assessment, but old approaches were modified to adapt decision analysis to individual patient problems. This experience demonstrates that decision analysis can be carried out effectively on a consultative basis for individual patients. A consultation service can also train analysts in decision-making and drive research in medical problem solving.
Physicians have developed a number of implicit and explicit approaches to complex medical decisions. Decision analysis is an explicit, quantitative method of clinical decision making that involves the separation of the probabilities of events from their relative values, or utilities. Its use can help physicians make difficult choices in a manner that promotes true patient participation. Decision analysis also provides a framework for the incorporation of data from multiple sources and for the assessment of the impact of uncertain data on the final decision. Although this approach is imperfect, it represents a significant advance in clinical decision making.
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Nationally representative data regarding the organizational, financial, and procedural features of health plans in which psychiatric patients receive treatment indicate that fewer privately insured, Medicaid, and Medicare managed care enrollees receive care from a psychiatrist than is true for "nonmanaged" enrollees. Financial considerations were reported to adversely affect treatment for one-third of all patients. Although utilization management techniques and financial/resource constraints commonly applied to patients in both managed and nonmanaged plans, performance-based incentives were rare in nonmanaged plans. The traditional health plan categories provide limited information to identify salient plan characteristics and guide policy decisions regarding the provision of care.
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