Journal of Medical Internet Research | July 2021
Objective: This study aims to demonstrate the feasibility, adoption, and accuracy of a distributed data collection system that leverages basic mobile phone technology to gather reports on the quantity and location of patient samples and test results prepared for delivery in the diagnostic network of Malawi.
Methods: We designed a system that leverages unstructured supplementary service data (USSD) technology to enable health workers to submit daily reports describing the quantity of transportation-ready diagnostic samples and test results at specific health care facilities, free of charge with any mobile phone, and aggregate these data for sample transportation administrators. We then conducted a year-long field trial of this system in 51 health facilities serving 3 districts in Malawi. Between July 2019 and July 2020, the participants submitted daily reports containing the number of patient samples or test results designated for viral load, early infant diagnosis, and tuberculosis testing at each facility. We monitored daily participation and compared the submitted USSD reports with program data to assess system feasibility, adoption, and accuracy.
Results: The participating facilities submitted 37,771 reports over the duration of the field trial. Daily facility participation increased from an average of 50% (26/51) in the first 2 weeks of the trial to approximately 80% (41/51) by the midpoint of the trial and remained at or above 80% (41/51) until the conclusion of the trial. On average, more than 80% of the reports submitted by a facility for a specific type of sample matched the actual number of patient samples collected from that facility by a courier.
Conclusions: Our findings suggest that a USSD-based system is a feasible, adoptable, and accurate solution to the challenges of untimely, inaccurate, or incomplete data in diagnostic networks. Certain design characteristics of our system, such as the use of USSD, and implementation characteristics, such as the supportive role of the field team, were necessary to ensure high participation and accuracy rates without any explicit financial incentives.
Sarang Deo is a Professor of Operations Management at the Indian School of Business (ISB). He also serves as the Executive Director of the ISB’s Max Institute of Healthcare Management, where he provides strategic leadership for ISB's healthcare initiatives through interdisciplinary research, education, and collaboration.
Professor Deo's research focuses on healthcare delivery systems, examining how operational decisions influence population-level health outcomes. His work spans a wide range of healthcare contexts, including influenza vaccine supply chains and ambulance diversion in the United States, HIV early infant diagnosis networks in sub-Saharan Africa, and formal and informal pathways for tuberculosis (TB) diagnosis and treatment in India. His recent research has focused on designing and evaluating healthcare delivery models, with a particular emphasis on digital health and the integration of artificial intelligence into healthcare systems.
He regularly collaborates with leading global health organisations, including the Gates Foundation, the Clinton Health Access Initiative (CHAI), and PATH. He currently serves on the World Health Organization's Strategic and Technical Advisory Group for Tuberculosis (STAG-TB) and the Working Group for Non-Technical Evaluation under the WHO Global Initiative on AI for Health (GI-AI4H).
Prior to joining ISB, Professor Deo was an Assistant Professor at the Kellogg School of Management. He holds a PhD from the UCLA Anderson School of Management, an MBA from the Indian Institute of Management Ahmedabad, and a BTech from the Indian Institute of Technology Bombay. Before entering academia, he worked as a management consultant with Accenture.
