Remote health monitoring and disease detection in the developing world are hampered by a lack of accurate, convenient and affordable diagnostic tests. Many of the tests routinely administered in well-equipped clinical laboratories are inappropriate for the settings encountered at the point of care, where low-income patients may be best served. To address this problem, medical researchers have developed innovative rapid diagnostic tests (RDTs) that are capable of detecting diseases at the point of care within a single patient visit to a clinic. However, for these new diagnostic technologies to be effective, tools must be developed to support the health workers who will be responsible for administering the tests and interpreting their results.
Broadly, this work seeks to answer the following research question: How can we support health workers as they are required to make increasingly complex diagnostic decisions for an increasing number of diseases and medical conditions? To address this challenge, we designed ODK Diagnostics, a smartphone application that supports health workers in three ways: (1) by facilitating the creation of digital job aids to guide users step-by-step through the process of administering each test correctly, (2) by automatically interpreting the test results using computer vision algorithms running locally on the phone and delivering the diagnosis to health workers, and (3) by automating the process of collecting data regarding the type and outcome of the test administered, which will alleviate some of the reporting burden placed on health workers and allow them to spend more time focusing on patients and less time doing paperwork and record keeping.
Thus far, we have completed the technical implementation and initial evaluation of Diagnostics, which represent only the first steps towards building a effective point-of-care diagnostic system. Our results suggest that the system is ready to be field tested with health workers, and our next steps will involve more focused field studies that rigorously evaluate both the digital job aids and the algorithm for automatically interpreting the test results to ensure that they are usable and appropriate for point-of-care settings in developing countries.
Nicola Lee Dell, Sugandhan Venkatachalam, Dean Stevens, Paul Yager, Gaetano Borriello. 2011. Towards a point-of-care diagnostic system: automated analysis of immunoassay test data on a cell phone. In Proceedings of the 5th ACM workshop on Networked systems for developing regions. ACM, New York, NY, USA, 3–8. PDF