Lightweight Mobile Automated Assistant-to-physician for Global Lower-resource Areas

TL;DR

This paper presents a machine learning-based mobile assistant designed to support primary healthcare providers in low-resource areas by enabling real-time documentation, diagnosis suggestions, and offline functionality, thereby improving healthcare delivery.

Contribution

The authors developed an adaptive, offline-capable medical assistant app that integrates diverse data sources and assists providers in low-resource settings, a novel approach for such environments.

Findings

Successfully built an Android app tested by dozens of providers.

The app provides diagnoses and prescriptions for common diseases.

Supports offline operation with real-time data sharing.

Abstract

Importance: Lower-resource areas in Africa and Asia face a unique set of healthcare challenges: the dual high burden of communicable and non-communicable diseases; a paucity of highly trained primary healthcare providers in both rural and densely populated urban areas; and a lack of reliable, inexpensive internet connections. Objective: To address these challenges, we designed an artificial intelligence assistant to help primary healthcare providers in lower-resource areas document demographic and medical sign/symptom data and to record and share diagnostic data in real-time with a centralized database. Design: We trained our system using multiple data sets, including US-based electronic medical records (EMRs) and open-source medical literature and developed an adaptive, general medical assistant system based on machine learning algorithms. Main outcomes and Measure: The application collects basic information from patients and provides primary care providers with diagnoses and prescriptions suggestions. The application is unique from existing systems in that it covers a wide range of common diseases, signs, and medication typical in lower-resource countries; the application works with or without an active internet connection. Results: We have built and implemented an adaptive learning system that assists trained primary care professionals by means of an Android smartphone application, which interacts with a central database and collects real-time data. The application has been tested by dozens of primary care providers. Conclusions and Relevance: Our application would provide primary healthcare providers in lower-resource areas with a tool that enables faster and more accurate documentation of medical encounters. This application could be leveraged to automatically populate local or national EMR systems.