The Fault in Our Sets: A Mixed Methods Analysis of Clinical Value Set Errors.

Objective: To characterize clinical value set issues and identify common patterns of errors.

Materials And Methods: We conducted semi-structured interviews with 26 value set experts and performed root cause analyses of errors identified in electronic health records (EHRs). We also analyzed a random sample of user-reported issues from the Value Set Authority Center (VSAC), developing a categorization scheme for value set errors.

Regulation of artificial intelligence in healthcare: Clinical Laboratory Improvement Amendments (CLIA) as a model.

Objectives: To assess the potential to adapt an existing technology regulatory model, namely the Clinical Laboratory Improvement Amendments (CLIA), for clinical artificial intelligence (AI).

Materials And Methods: We identify overlap in the quality management requirements for laboratory testing and clinical AI.

Results: We propose modifications to the CLIA model that could make it suitable for oversight of clinical AI.

Implementation of Electronic Triggers to Identify Diagnostic Errors in Emergency Departments.

Importance: Missed diagnosis can lead to preventable patient harm.

Objective: To develop and implement a portfolio of electronic triggers (e-triggers) and examine their performance for identifying missed opportunities in diagnosis (MODs) in emergency departments (EDs).

Design, Setting, And Participants: In this retrospective medical record review study of ED visits at 1321 Veterans Affairs health care sites, rules-based e-triggers were developed and implemented using a national electronic health record repository.