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

• URL: http://arxiv.org/abs/2110.15127v1
• Date: Thu, 28 Oct 2021 14:02:16 GMT
• Title: Lightweight Mobile Automated Assistant-to-physician for Global Lower-resource Areas
• Authors: Chao Zhang, Hanxin Zhang, Atif Khan, Ted Kim, Olasubomi Omoleye, Oluwamayomikun Abiona, Amy Lehman, Christopher O. Olopade, Olufunmilayo I. Olopade, Pedro Lopes, Andrey Rzhetsky
• Abstract summary: We designed an artificial intelligence assistant to help primary healthcare providers in lower-resource areas document demographic and medical sign/symptom data. The application collects basic information from patients and provides primary care providers with diagnoses and prescriptions suggestions.
• Score: 9.978987200997686
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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