Medical Malpractice Overview

Principles

Definition

Medical malpractice is a core concept investigated across legal scholarship, healthcare ethics, and patient safety research. It examines instances where healthcare professionals deviate from the accepted standard of care, resulting in patient injury or death, analyzing the associated legal liability, ethical responsibilities, and systemic factors contributing to preventable harm.

Ontological type

Liability Standards

Standard of Care

Reform Models

Key developments

Key Figures

Consent and Transparency era

Ezekiel J. Emanuel[1] is a prominent scholar associated with Harvard University[3] and John F. Kennedy University[4] during this era. Ezekiel J. Emanuel[1] contributed by articulating Four Models of the Physician-Patient Relationship[7], explaining how different configurations of disclosure and dialogue shape consent and patient autonomy, which was crucial for shifting malpractice discourse toward communication. Albert W. Wu[2] is linked to the University of California, San Francisco[5] and Johns Hopkins University[6] in this era. Albert W. Wu[2] highlighted the concept of medical error as the 'second victim' in his 2000 paper Medical error: the second victim[8], underscoring why disclosure, apologies, and supportive practices for clinicians are important for patient safety and accountability.

Systemic Safety Reform era

David M. Studdert [1] is a leading scholar whose work in this era was associated with Harvard University [3] and Stanford University [4]. His key contributions in this era draw on Missed and Delayed Diagnoses in the Emergency Department: A Study of Closed Malpractice Claims From 4 Liability Insurers [6] and National Costs Of The Medical Liability System [7], which demonstrated how systematic data on malpractice claims and costs can inform safety reforms and governance. Troyen A. Brennan [2] is a scholar whose work in this era is linked to Harvard University [3] and the University of California, San Francisco [5]. His contribution centers on the Missed and Delayed Diagnoses paper [6], whose findings underscored the role of nonpunitive reporting and organizational learning in preventing diagnostic harm in this era.

AI and Accountability era

Ryan Abbott (UCLA) has argued that AI-driven diagnostic tools create shared liability among clinicians, institutions, and developers, prompting malpractice frameworks that distribute accountability beyond physicians alone. Brent Mittelstadt (Oxford) has shaped the explainability discourse by advocating context-aware, ethically grounded explanations for AI in care and urging governance designs that balance transparency with patient safety. Frank Pasquale (Brooklyn Law School) has linked algorithmic opacity to accountability gaps and pressed for regulatory reforms and rights to explanation to address AI-mediated malpractice. Kate Crawford (Microsoft Research) has illuminated the political and data-driven dimensions of AI systems, including opacity and bias, reinforcing calls for robust governance, redress mechanisms, and safety oversight in AI-enabled medical care.