TeleWound Practice Within the Veterans Health Administration: Protocol for a Mixed Methods Program Evaluation
Bibliographic record
Abstract
BACKGROUND: Chronic wounds, such as pressure injuries and diabetic foot ulcers, are a significant predictor of mortality. Veterans who reside in rural areas often have difficulty accessing care for their wounds. TeleWound Practice (TWP), a coordinated effort to incorporate telehealth into the provision of specialty care for patients with skin wounds, has the potential to increase access to wound care by allowing veterans to receive this care at nearby outpatient clinics or in their homes. The Veterans Health Administration (VA) is championing the rollout of the TWP, starting with regional implementation. OBJECTIVE: This paper aims to describe the protocol for a mixed-methods program evaluation to assess the implementation and outcomes of TWP in VA. METHODS: We are conducting a mixed-methods evaluation of 4 VA medical centers and their community-based outpatient clinics that are participating in the initial implementation of the TWP. Data will be collected from veterans, VA health care team members, and other key stakeholders (eg, clinical leadership). We will use qualitative methods (ie, semistructured interviews), site visits, and quantitative methods (ie, surveys, national VA administrative databases) to assess the process and reach of TWP implementation and its impact on veterans' clinical outcomes and travel burdens and costs. RESULTS: This program evaluation was funded in October 2019 as a Partnered Evaluation Initiative by the US Department of Veterans Affairs, Diffusion of Excellence Office, and Office of Research and Development, Health Services Research and Development Service, Quality Enhancement Research Initiative Program (PEC 19-310). CONCLUSIONS: Evaluation of the TWP will identify barriers and solutions to TeleWound implementation in a small number of sites that can be used to inform successful rollout of the TWP nationally. Our evaluation work will inform future efforts to scale up the TWP across VA and optimize reach of the program to veterans across the nation. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/20139.
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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 | 0.190 | 0.132 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.060 | 0.012 |
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".