title: Selecting clinical diagnoses: logical strategies informed by experience.
abstract: This article describes reasoning strategies used by clinicians in different diagnostic circumstances and how these modes of inquiry may allow further insight into the evaluation and treatment of patients. Specifically, it aims to make explicit the implicit logical considerations that guide a variety of strategies in the diagnostic process, as exemplified in specific clinical cases. It focuses, in particular, in strategies that clinicians use to move from a large set of possible diagnoses initially suggested by abductive inferences - the process of hypothesis generation that creates a diagnostic space - to a narrower set or even to a single 'best' diagnosis, where the criteria to determine what is 'best' may differ according to different strategies. Experienced clinicians should have a diversified kit of strategies - for example, Bayesian probability or inference to a lovely explanation - to select from among previously generated hypotheses, rather than rely on any one approach every time.
authors.affiliation: Department of Pathology, Maine Medical Center, Portland, Maine, USA.
authors.forename: Donald Edward
authors.initials: DE
authors.lastname: Stanley
authors.affiliation: Department of Philosophy, Brooklyn College, The City University of New York, New York, NY, USA.
authors.forename: Daniel G
authors.initials: DG
authors.lastname: Campos
mesh_terms: D001499:Bayes Theorem; D000066491:Clinical Decision-Making / Q000941:ethics* / Q000379:methods*; D003933:Diagnosis; D006801:Humans; D000072281:Lymphadenopathy / Q000175:diagnosis; D010146:Pain / Q000175:diagnosis; D010817:Physician-Patient Relations; D011340:Problem Solving; D012871:Skin Diseases / Q000175:diagnosis; D013276:Stomach Ulcer / Q000150:complications / Q000175:diagnosis; D020217:Vertebral Artery Dissection / Q000175:diagnosis
keywords: Bayesian probability; Peirce; abduction; diagnosis; hypothesis; induction