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

ImportanceLower-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. ObjectiveTo 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. DesignWe 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 MeasureThe 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. ResultsWe 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 RelevanceOur 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. Key pointsO_ST_ABSQuestionC_ST_ABSLower-resource areas like many countries in Africa are suffering from severe disease burdens, how can we utilize the modern health system in developed countries to help ease it? FindingsUtilizing AI technology with large data sets from US, we developed a smart diagnosis assistance 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. The assistance system has been tested in Pakistan and proven to be effective. MeaningOur application would provide primary healthcare providers in lower-resource areas with a tool that enables faster and more accurate documentation of medical encounters.