Utilizing Lean Software Methods To Improve Acceptance of Global eHealth Initiatives: Results From the Implementation of the Basic Emergency Care App
Bibliographic record
Abstract
BACKGROUND: Health systems in low- and middle-income countries face considerable challenges in providing high-quality accessible care. eHealth has had mounting interest as a possible solution given the unprecedented growth in mobile phone and internet technologies in these locations; however, few apps or software programs have, as of yet, gone beyond the testing phase, most downloads are never opened, and consistent use is extremely rare. This is believed to be due to a failure to engage and meet local stakeholder needs and the high costs of software development. OBJECTIVE: World Health Organization Basic Emergency Care course participants requested a mobile point-of-care adjunct to the primary course material. Our team undertook the task of developing this solution through a community-based participatory model in an effort to meet trainees' reported needs and avoid some of the abovementioned failings. We aimed to use the well-described Lean software development strategy-given our familiarity with its elements and its ubiquitous use in medicine, global health, and software development-to complete this task efficiently and with maximal stakeholder involvement. METHODS: From September 2016 through January 2017, the Basic Emergency Care app was designed and developed at the University of California San Francisco. When a prototype was complete, it was piloted in Cape Town, South Africa and Dar es Salaam, Tanzania-World Health Organization Basic Emergency Care partner sites. Feedback from this pilot shaped continuous amendments to the app before subsequent user testing and study of the effect of use of the app on trainee retention of Basic Emergency Care course material. RESULTS: Our user-centered mobile app was developed with an iterative participatory approach with its first version available within 6 months and with high acceptance-95% of Basic Emergency Care Course participants felt that it was useful. Our solution had minimal direct costs and resulted in a robust infrastructure for subsequent assessment and maintenance and allows for efficient feedback and expansion. CONCLUSIONS: We believe that utilizing Lean software development strategies may help global health advocates and researchers build eHealth solutions with a process that is familiar and with buy-in across stakeholders that is responsive, rapid to deploy, and sustainable.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".