Schmude Marcel, Salim Nahya, Azadzoy Hila, Bane Mustafa, Millen Elizabeth, O'Donnell Lisa, Bode Philipp, Türk Ewelina, Vaidya Ria, Gilbert Stephen
Ada Health GmbH, Berlin, Germany.
Muhimbili University of Health and Allied Sciences, Dar es Salaam, United Republic of Tanzania.
JMIR Res Protoc. 2022 Jun 7;11(6):e34298. doi: 10.2196/34298.
Low- and middle-income countries face difficulties in providing adequate health care. One of the reasons is a shortage of qualified health workers. Diagnostic decision support systems are designed to aid clinicians in their work and have the potential to mitigate pressure on health care systems.
The Artificial Intelligence-Based Assessment of Health Symptoms in Tanzania (AFYA) study will evaluate the potential of an English-language artificial intelligence-based prototype diagnostic decision support system for mid-level health care practitioners in a low- or middle-income setting.
This is an observational, prospective clinical study conducted in a busy Tanzanian district hospital. In addition to usual care visits, study participants will consult a mid-level health care practitioner, who will use a prototype diagnostic decision support system, and a study physician. The accuracy and comprehensiveness of the differential diagnosis provided by the diagnostic decision support system will be evaluated against a gold-standard differential diagnosis provided by an expert panel.
Patient recruitment started in October 2021. Participants were recruited directly in the waiting room of the outpatient clinic at the hospital. Data collection will conclude in May 2022. Data analysis is planned to be finished by the end of June 2022. The results will be published in a peer-reviewed journal.
Most diagnostic decision support systems have been developed and evaluated in high-income countries, but there is great potential for these systems to improve the delivery of health care in low- and middle-income countries. The findings of this real-patient study will provide insights based on the performance and usability of a prototype diagnostic decision support system in low- or middle-income countries.
ClinicalTrials.gov NCT04958577; http://clinicaltrials.gov/ct2/show/NCT04958577.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/34298.
低收入和中等收入国家在提供充足医疗保健方面面临困难。原因之一是合格卫生工作者短缺。诊断决策支持系统旨在协助临床医生开展工作，并有可能减轻医疗保健系统的压力。
坦桑尼亚基于人工智能的健康症状评估（AFYA）研究将评估一种基于英语的人工智能原型诊断决策支持系统在低收入或中等收入环境中对中级卫生保健从业者的潜在作用。
这是一项在坦桑尼亚一家繁忙的地区医院进行的观察性前瞻性临床研究。除了常规护理就诊外，研究参与者将咨询一名中级卫生保健从业者，该从业者将使用原型诊断决策支持系统，以及一名研究医生。将根据专家小组提供的金标准鉴别诊断来评估诊断决策支持系统提供的鉴别诊断的准确性和全面性。
患者招募于2021年10月开始。参与者在医院门诊候诊室直接招募。数据收集将于2022年5月结束。数据分析计划于2022年6月底完成。研究结果将发表在同行评审期刊上。
大多数诊断决策支持系统是在高收入国家开发和评估的，但这些系统在改善低收入和中等收入国家的医疗保健服务方面具有巨大潜力。这项真实患者研究的结果将基于原型诊断决策支持系统在低收入或中等收入国家的性能和可用性提供见解。
ClinicalTrials.gov NCT04958577；http://clinicaltrials.gov/ct2/show/NCT04958577。
国际注册报告识别码（IRRID）：DERR1-10.2196/34298。
