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| Content Provider | ACM Digital Library |
|---|---|
| Author | Coelho, Aida Crawford, Jessica Dell, Nicola Borriello, Gaetano Breit, Nathan McCord, Joseph Chaluco, Timóteo |
| Abstract | We describe our experiences integrating ODK Scan into the community health worker (CHW) supply chain in Mozambique. ODK Scan is a mobile application that uses computer vision techniques to digitize data from paper forms. The application automatically classifies machine-readable data types, like bubbles and checkboxes, and assists users with the manual entry of handwritten text and numbers. We designed an intervention that uses paper forms in conjunction with ODK Scan to monitor CHW usage of essential health commodities, finding that the application is capable of providing supervisors and stakeholders with important information regarding health commodity availability in the field. Specifically, we (1) detail our experiences integrating ODK Scan into the health worker supply chain in Mozambique, with particular emphasis on the critical (and often under-reported) role of practitioners; (2) evaluate the impact of the technology at multiple levels of the information hierarchy, providing quantitative and qualitative data that exposes the benefits, challenges and limitations of the technology; and (3) share lessons learned and provide actionable guidance to researchers and practitioners interested in ODK Scan or other systems that bridge the gap between paper-based and digital data collection. |
| Starting Page | 228 |
| Ending Page | 237 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781450319065 |
| DOI | 10.1145/2516604.2516611 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-12-07 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Computer vision Dev Omr Ictd Computing for development Hci4d Paper forms Smartphone Cell phone Data entry |
| Content Type | Text |
| Resource Type | Article |
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