Benjamin Kleinmuntz

Why we still use our heads instead of formulas: Toward an integrative approach.

This review begins with a discussion of Meehls (1957) query regarding when to use ones head (i.e., intuition) instead of the formula (i.e., statistical or mechanical procedure) for clinical prediction. It then describes the controversy that ensued and analyzes the complexity and contemporary relevance of the question itself. Going beyond clinical inference, it identifies select cognitive biases and constraints that cause decision errors, and proposes remedial correctives. Given that the evidence shows cognition to be flawed, the article discusses the linear regression, Bayesian, signal detection, and computer approaches as possible decision aids. Their cost-benefit trade-offs, when used either alone or as complements to one another, are examined and evaluated. The critique concludes with a note of cautious optimism regarding the formulas future role as a decision aid and offers several interim solutions.

The scientific study of clinical judgment in psychology and medicine

The current scientific interest in clinical reasoning originates in human judgment research among psychologists, statisticians, physicians, and computer scientists. The present state of this research is described and traced to its 1950s beginnings. Although the pivotal importance of the computer is emphasized, its impact on clinical information management is recognized as not having been as great as in some other spheres of intelligent reasoning. But its future important role in the study of clinical judgment is optimistically anticipated.

Diagnostic Reasoning of High - and Low - domain-knowledge Clinicians A Reanalysis

Thinking-aloud protocols provided by Joseph and Patel were reanalyzed to determine the extent to which their conclusions could be replicated by independently developed coding schemes. The data set consisted of protocols from four cardiologists (low domain knowledge = LDK) and four endocrinologists (high domain knowledge = HDK), individually working on a diagnostic problem in endocrinology. The two analyses agree that the HDK physicians related data to potential diagnoses more than did the LDK group and were more focused on the correct diagnostic components. However, the reanalysis found no meaningful differ ence between the groups in diagnostic accuracy, speed of diagnosis, or the breadth of the search space used to seek a solution. In the reanalysis, the HDK physicians employed more single-cue inference and less multiple-cue inference. The generalizability of results of pro tocol-analysis studies can be assessed by using several complementary coding schemes. Key words: domain knowledge; protocol analysis; cognition; reasoning; diagnostic process. (Med Decis Making 1993;13:21-29)

Diagnostic problem solving by computer: a historical review and the current state of the science

The current scientific interest in medical diagnostic problem solving originates in human judgment research among psychologists, statisticians, computer scientists, and physicians. The present state of this research is described and traced to its beginnings in the 1950s. Although the pivotal importance of the computer is emphasized, its impact on medical information management is recognized as not having been as great as in some other spheres of intelligent reasoning. But its future important role in medical diagnostic problem solving is optimistically anticipated.

Computers as clinicians: An update

Computers as clinicians entered medical settings relatively recently, but with limited success because they lack general intelligence--that is, though they can be experts in domain specific specialties, they cannot yet deal with clinical problems never before encountered. SOAR, a novel AI computer programming architecture, can learn from past encounters with prior problems and can generalize its learning to new ones. It may therefore take computers to a higher level of clinical performance.

Toward intelligent computerized clinicians

The current scientific interest in computer thinking is explored for constructing a simulated psychodiagnostician. Going beyond simply building an expert system, this paper proposes to use a self-learning and generalizing computer architecture. State Operator And Result, or the SOAR system, to model intelligent clinical behavior. Accordingly, it fosters the collection and analysis of process-tracing verbal protocols for building psychodiagnosticians. It also holds out the possibility of going beyond psychodiagnosis for functioning as a multitask clinical psychologist.

Psychodiagnosis in hypothetical problem spaces

Computer uses are demonstrated for designing a hypothetical psychodiagnostic system that can function in artificially created mental health environments. The idea is to simulate both a psychodiagnostic system and a clinician’s strategies in order to learn about computer as well as human information processing. An inductive method for teaching humans the elements of the diagnostic system and its solutions is also introduced.

Computers in Psychological Practice

In the 1970s and early 1980s, the birth of the Apple Computer and the IBM PC signaled the beginnings of a radical popularization of computer technology. In a few short years, personal computers became business necessities and invaded over 15% of American households (Squires, 1984). Nevertheless, practicing clinical psychologists have appeared to be ambivalent about computing. Although some psychologists eagerly and immediately welcomed computational methods into psychology, others were skeptical and were angered by the incursion of computers into clinical territories.