ODK Scan: Digitizing Data Collection and Impacting Data Management Processes in Pakistan’s Tuberculosis Control Program

Syed Mustafa Ali, Rachel Powers, Jeffrey Beorse, Arif Noor, Farah Naureen, Naveed Anjum, Muhammad Ishaq, Javariya Aamir and Richard Anderson
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Syed Mustafa Ali: mHealth Coordinator, Mercy Corps, Pak Palace, Murree Road, Rawal Chowk, Islamabad 44000, Pakistan
Rachel Powers: Information Systems, VillageReach, Seattle, WC 98102, USA
Jeffrey Beorse: Computer Science and Engineering, University of Washington, Seattle, WA 98101, USA
Arif Noor: mHealth Coordinator, Mercy Corps, Pak Palace, Murree Road, Rawal Chowk, Islamabad 44000, Pakistan
Farah Naureen: mHealth Coordinator, Mercy Corps, Pak Palace, Murree Road, Rawal Chowk, Islamabad 44000, Pakistan
Naveed Anjum: mHealth Coordinator, Mercy Corps, Pak Palace, Murree Road, Rawal Chowk, Islamabad 44000, Pakistan
Muhammad Ishaq: mHealth Coordinator, Mercy Corps, Pak Palace, Murree Road, Rawal Chowk, Islamabad 44000, Pakistan
Javariya Aamir: mHealth Coordinator, Mercy Corps, Pak Palace, Murree Road, Rawal Chowk, Islamabad 44000, Pakistan
Richard Anderson: Computer Science and Engineering, University of Washington, Seattle, WA 98101, USA

Future Internet, 2016, vol. 8, issue 4, 1-17

Abstract: The present grievous tuberculosis situation can be improved by efficient case management and timely follow-up evaluations. With the advent of digital technology, this can be achieved through quick summarization of the patient-centric data. The aim of our study was to assess the effectiveness of the ODK Scan paper-to-digital system during a testing period of three months. A sequential, explanatory mixed-method research approach was employed to elucidate technology use. Training, smartphones, the application and 3G-enabled SIMs were provided to the four field workers. At the beginning, baseline measures of the data management aspects were recorded and compared with endline measures to determine the impact of ODK Scan. Additionally, at the end of the study, users’ feedback was collected regarding app usability, user interface design and workflow changes. A total of 122 patients’ records were retrieved from the server and analysed in terms of quality. It was found that ODK Scan recognized 99.2% of multiple choice fill-in bubble responses and 79.4% of numerical digit responses correctly. However, the overall quality of the digital data was decreased in comparison to manually entered data. Using ODK Scan, a significant time reduction is observed in data aggregation and data transfer activities, but data verification and form-filling activities took more time. Interviews revealed that field workers saw value in using ODK Scan, but they were more concerned about the time-consuming aspects of the use of ODK Scan. Therefore, it is concluded that minimal disturbance in the existing workflow, continuous feedback and value additions are the important considerations for the implementing organization to ensure technology adoption and workflow improvements.

Keywords: mHealth; ODK scan; mobile health application; digitizing data collection; data management processes; paper-to-digital system; technology-assisted data management; treatment adherence (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2016
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