Clinical Reasoning and Decision-Making in Psychiatry

Mental health professionals routinely make treatment decisions without necessarily having an overarching perspective about optimal next steps. This important new book provides them with reader-friendly, pragmatic strategies to approach clinical problems as testable hypotheses. It discusses how to apply concepts based on decision analytic theory using risk-benefit analyses, contingency planning, measurement-based care, shared decision making, pharmacogenetics, disease staging, and machine learning. Readers will learn how these tools can help them craft optimal pharmacological and psychosocial interventions tailored to the needs of an individual patient. The book covers topics such as diagnostic ambiguity, interview technique, applying statistical concepts to individual patients, artificial intelligence, and managing high-risk, treatment-resistant, or demanding and difficult patients. Valuable clinical vignettes are featured throughout the book to illustrate common dilemmas and scenarios where the relative merits of competing treatment options invite a more iterative than definitive approach. For all healthcare professionals who prescribe psychotropic medications.

• Approaches psychopharmacology from the novel vantage point of honing clinicians’ decision-making skills, rather than simply reciting findings from clinical trials, allowing prescribers to better understand ‘how’ to think rather than ‘what’ to think.
• Clinicians at all levels can learn how to gather, organize and utilize clinical information enabling them to conceptualize their patients with greater clarity, derive more confident formulations, and deliver more rationale-based, individualized pharmacological or psychosocial treatment interventions.
• Presents patient-specific decisions about all forms of treatment (pharmacological, psychotherapeutic, environmental) according to principles from decision theory, enabling clinicians to better recognize logical treatment options and foster more optimal outcomes for patients at all levels of complexity.
• Explains the impact of artificial intelligence (AI) and machine learning technologies on clinical decision-making, with clinical examples.

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