Lightweight Mobile Automated Assistant-to-physician for Global Lower-resource Areas Article Swipe
Chao Zhang
,
Hanxin Zhang
,
Atif Ali Khan
,
Ted Kim
,
Olasubomi J. Omoleye
,
Oluwamayomikun Abiona
,
Amy Lehman
,
Christopher O. Olopade
,
Olufunmilayo I. Olopade
,
Pedro Lopes
,
Andrey Rzhetsky
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.11.01.21265487
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.11.01.21265487
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. Key points Question Lower-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? Findings Utilizing 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. Meaning Our application would provide primary healthcare providers in lower-resource areas with a tool that enables faster and more accurate documentation of medical encounters.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2021.11.01.21265487
- https://www.medrxiv.org/content/medrxiv/early/2021/11/04/2021.11.01.21265487.full.pdf
- OA Status
- green
- Cited By
- 3
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3210701940
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3210701940Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2021.11.01.21265487Digital Object Identifier
- Title
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Lightweight Mobile Automated Assistant-to-physician for Global Lower-resource AreasWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-11-04Full publication date if available
- Authors
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Chao Zhang, Hanxin Zhang, Atif Ali Khan, Ted Kim, Olasubomi J. Omoleye, Oluwamayomikun Abiona, Amy Lehman, Christopher O. Olopade, Olufunmilayo I. Olopade, Pedro Lopes, Andrey RzhetskyList of authors in order
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https://doi.org/10.1101/2021.11.01.21265487Publisher landing page
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https://www.medrxiv.org/content/medrxiv/early/2021/11/04/2021.11.01.21265487.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
- OA URL
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https://www.medrxiv.org/content/medrxiv/early/2021/11/04/2021.11.01.21265487.full.pdfDirect OA link when available
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Medical diagnosis, The Internet, Documentation, Computer science, Resource (disambiguation), Health care, Medical prescription, World Wide Web, Medicine, Nursing, Pathology, Economics, Economic growth, Computer network, Programming languageTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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