# AI-based Clinical Decision Support for Primary Care: A Real-World Study

arXiv: https://arxiv.org/abs/2507.16947 · HTML: https://mlsanity.com/paper/2507.16947/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2025.

## Built on by (most cited first, 12 of 12)

- [PhysicianBench: Evaluating LLM Agents in Real-World EHR Environments](https://mlsanity.com/paper/2605.02240/) (2026)
- [Grounding Large Language Models in Clinical Evidence: A Retrieval-Augmented Generation System for Querying UK NICE Clinical Guidelines](https://mlsanity.com/paper/2510.02967/) (2025)
- [Towards Conversational Medical AI with Eyes, Ears and a Voice](https://mlsanity.com/paper/2605.09272/) (2026)
- [DialogGuard: Multi-Agent Psychosocial Safety Evaluation of Sensitive LLM Responses](https://mlsanity.com/paper/2512.02282/) (2025)
- [Case-Specific Rubrics for Clinical AI Evaluation: Methodology, Validation, and LLM-Clinician Agreement Across 823 Encounters](https://mlsanity.com/paper/2604.24710/) (2026)
- [Scalable Stewardship of an LLM-Assisted Clinical Benchmark with Physician Oversight](https://mlsanity.com/paper/2512.19691/) (2025)
- [Reflections and New Directions for Human-Centered Large Language Models](https://mlsanity.com/paper/2605.06901/) (2026)
- [A global log for medical AI](https://mlsanity.com/paper/2510.04033/) (2025)
- [Benchmarking and Adapting On-Device LLMs for Clinical Decision Support](https://mlsanity.com/paper/2601.03266/) (2025)
- [Deployment and Evaluation of an EHR-integrated, Large Language Model-Powered Tool to Triage Surgical Patients](https://mlsanity.com/paper/2603.17234/) (2026)
- [Reasoning in Real World Clinical Care: Why Large Language Models Are Not Yet Safe for Autonomous Clinical Decision Support](https://mlsanity.com/paper/2607.28677/) (2026)
- [Towards Expert-level Medical AI for Real-time Video Consultations](https://mlsanity.com/paper/2608.09861/) (2026)

## Abstract (arXiv)

We evaluate the impact of large language model-based clinical decision support in live care. In partnership with Penda Health, a network of primary care clinics in Nairobi, Kenya, we studied AI Consult, a tool that serves as a safety net for clinicians by identifying potential documentation and clinical decision-making errors. AI Consult integrates into clinician workflows, activating only when needed and preserving clinician autonomy. We conducted a quality improvement study, comparing outcomes for 39,849 patient visits performed by clinicians with or without access to AI Consult across 15 clinics. Visits were rated by independent physicians to identify clinical errors. Clinicians with access to AI Consult made relatively fewer errors: 16% fewer diagnostic errors and 13% fewer treatment errors. In absolute terms, the introduction of AI Consult would avert diagnostic errors in 22,000 visits and treatment errors in 29,000 visits annually at Penda alone. In a survey of clinicians with AI Consult, all clinicians said that AI Consult improved the quality of care they delivered, with 75% saying the effect was "substantial". These results required a clinical workflow-aligned AI Consult implementation and active deployment to encourage clinician uptake. We hope this study demonstrates the potential for LLM-based clinical decision support tools to reduce errors in real-world settings and provides a practical framework for advancing responsible adoption.