Implementing Mobile Health Interventions to Capture Post-Operative Patient-Generated Health Data
Bibliographic record
Abstract
Abstract Background: The implementation of health information technology interventions is at the forefront of most hospital institutional policy agendas. Despite the availability of numerous apps and mobile platforms focusing on specific areas in healthcare the widespread integration into clinical practice can be a complex process. Here we present guidelines and methodology that we have learned in the implementation process of new technology and an overview of some of the current barriers and enablers specific to implementation of post-surgical site surveillance technology. Methods: Analysis of the experience of successful information technology (IT) implementation in different healthcare systems reveals that, despite differences among patient groups, care providers, and hospitals, there are common barriers and enablers to implementation of health IT. Results: The process of implementation in organizations and among individuals can be most successful by identifying barriers and enablers within three key stakeholder groups: (1) patients; (2) care providers/clinicians; and (3) manager/administration within healthcare systems. This can be achieved by specific engagement and co-design processes establishing clear benefits, sufficient incentives, and adequate support for clinicians as well as payer–provider relationships, marketplace competition and privacy legislation. Conclusions: The successful implementation of such programs requires appropriate strategic planning to address the needs of three specific components: patients, care provider, and policymakers/healthcare management understanding and acceptance.
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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 | 0.008 | 0.031 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".