Safety of a Large Language Model-based Clinical Decision Support System in African Primary Healthcare

We conducted a retrospective evaluation of an electronic medical record-embedded large language model (LLM) clinical decision support system deployed across 16 primary care clinics in Kenya, between July-September 2024. A panel of trained physicians reviewed 1,469 records. Hallucinations were uncommon 50 encounters (3.4%, 95% confidence interval [CI] 2.5-4.5), most often involving mis-expanded acronyms or drug names. Clinical management guidance aligned with local guidelines in almost all cases (1,455; 99%, 95% CI 98.4-99.5). Despite this, clinicians did not modify documentation in 917 encounters (62%, 95% CI 59.9-64.9). Safety assessments identified actively harmful recommendations from the LLM in 115 encounters (7.8%, 95% CI 6.5-9.3), with 67 such recommendations appearing in the final documentation. Conversely, risk present in the clinician’s initial notes was fully mitigated in 118 encounters (8.0%, 95% CI 6.7-9.5 overall; 12.1%, 95% CI 9.5-15.2 of amended cases). Overall, the tool showed strong potential to support quality improvement, but the asymmetric adoption of harmful versus beneficial outputs underscore the need for usability optimization, local guardrails, and prospective trials to confirm patient-level benefit.