Design Recommendations towards Developing a Smartphone-Based Point-of-Care Tool for Rural Bangladeshi Users

Abstract

Smartphone enhances healthcare support for everyone, from local to remote patients. Recent advancements in smartphone sensors redefine their usage and the prospect of remote point-of-care tools (e.g., blood diagnostic devices), especially for low-resource settings. This paper studies the sufferings of rural people due to the limited healthcare facilities and figures out the implications. The proliferation of smartphone users suggests converting many smartphones into point-of-care diagnosis devices would be a life-saving decision. Previous studies showed smartphone’s built-in camera captures physiological features (e.g., hemoglobin) from fingertip videos captured under different lights. So, we created a mobile application and attachments (light sources) to record fingertip videos for hemoglobin level calculation. Then we collected feedback on how the rural users interacted with the application. Finally, we applied qualitative and quantitative analysis to investigate their answers. Their invaluable feedback reflected the implications of various aspects of a smartphone-based point-of-care tool. The findings unveil how rural-area people can receive a smartphone's blood diagnostic services. Our results will facilitate mobile health application designers and developers to build a smartphone-based point-of-care tool for any rural area people.

Acknowledgments

We collected data from two locations (RHRS and PSTU), where several officials provided enormous support. First, we are grateful to Dr. Md Idris Ali Howlader, Senior Scientific Officer, RHRS, Patuakhali, and Professor Jamal Hossain, CSIT Department, PSTU, Patuakhali, Bangladesh. In addition, we want to thank PSTU students such as Nazmus Sakib, Monir Hossain, and Md Azharul Islam, who helped in the data collection process, application design, and development of this study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Md Kamrul Hasan

Md Kamrul Hasan is an Assistant Professor of the Practice in the Department of Computer Science, Vanderbilt University. He received his PhD in Computational Sciences from Marquette University, USA, and his master’s in Computer Sciences from the University of Trento, Italy.

Devansh Saxena

Devansh Saxena is a doctoral candidate in the Department of Computer Science at Marquette University. His dissertation research focuses on studying and developing algorithmic systems used in the public sector, especially the child-welfare system. His current work examines collaborative child-welfare practice where decisions are mediated by policies, practice, and algorithms.

Yakin Rubaiat

Yakin Rubaiat is a Senior Software Engineer in Enosis solution, Bangladesh. As a former research fellow of Ankur Research scholarship, he implemented different machine learning and deep learning models using healthcare data.

Sheikh Iqbal Ahamed

Sheikh Iqbal Ahamed is a Professor and Chair at the Department of Computer Science of Marquette University. In addition, he is the Director of the Ubicomp Research Lab at Marquette. He received his PhD in Computer Science from Arizona State University. His research work focuses on building customized and innovative solutions in the mobile health area.

Shion Guha

Shion Guha is an Assistant Professor in the Faculty of Information at University of Toronto. His broad research interests include the nascent field of Human-Centered Data Science, and algorithmic decision-making, especially in public services such as criminal justice, child welfare, and healthcare.