The Long Way From Government Open Data to Mobile Health Apps: Overcoming Institutional Barriers in the US Federal Government
Bibliographic record
Abstract
BACKGROUND: Government agencies in the United States are creating mobile health (mHealth) apps as part of recent policy changes initiated by the White House's Digital Government Strategy. OBJECTIVE: The objective of the study was to understand the institutional and managerial barriers for the implementation of mHealth, as well as the resulting adoption pathways of mHealth. METHODS: This article is based on insights derived from qualitative interview data with 35 public managers in charge of promoting the reuse of open data through Challenge.gov, the platform created to run prizes, challenges, and the vetting and implementation of the winning and vendor-created apps. RESULTS: The process of designing apps follows three different pathways: (1) entrepreneurs start to see opportunities for mobile apps, and develop either in-house or contract out to already vetted Web design vendors; (2) a top-down policy mandates agencies to adopt at least two customer-facing mobile apps; and (3) the federal government uses a policy instrument called "Prizes and Challenges", encouraging civic hackers to design health-related mobile apps using open government data from HealthData.gov, in combination with citizen needs. All pathways of the development process incur a set of major obstacles that have to be actively managed before agencies can promote mobile apps on their websites and app stores. CONCLUSIONS: Beyond the cultural paradigm shift to design interactive apps and to open health-related data to the public, the managerial challenges include accessibility, interoperability, security, privacy, and legal concerns using interactive apps tracking citizen.
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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 | 0.028 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.014 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".