A Nurse-Led Care Delivery App and Telehealth System for Patients Requiring Wound Care: Mixed Methods Implementation and Evaluation Study
Bibliographic record
Abstract
BACKGROUND: Innovative solutions to nursing care are needed to address nurse, health system, patient, and caregiver concerns related to nursing wellness, work flexibility and control, workforce retention and pipeline, and access to patient care. One innovative approach includes a novel health care delivery model enabling nurse-led, off-hours wound care (PocketRN) to triage emergent concerns and provide additional patient health education via telehealth. OBJECTIVE: This pilot study aimed to evaluate the implementation of PocketRN from the perspective of nurses and patients. METHODS: Patients and part-time or per-diem, wound care-certified and generalist nurses were recruited through the Stanford Medicine Advanced Wound Care Center in 2021 and 2022. Qualitative data included semistructured interviews with nurses and patients and clinical documentation review. Quantitative data included app use and brief end-of-interaction in-app satisfaction surveys. RESULTS: This pilot study suggests that an app-based nursing care delivery model is acceptable, clinically appropriate, and feasible. Low technology literacy had a modest effect on initial patient adoption; this barrier was addressed with built-in outreach and by simplifying the patient experience (eg, via phone instead of video calls). This approach was acceptable for users, despite total patient enrollment and use numbers being lower than anticipated (N=49; 17/49, 35% of patients used the app at least once beyond the orientation call). We interviewed 10 patients: 7 who had used the app were satisfied with it and reported that real-time advice after hours reduced anxiety, and 3 who had not used the app after enrollment reported having other resources for health care advice and noted their perception that this tool was meant for urgent issues, which did not occur for them. Interviewed nurses (n=10) appreciated working from home, and they reported comfort with the scope of practice and added quality of care facilitated by video capabilities; there was interest in additional wound care-specific training for nonspecialized nurses. Nurses were able to provide direct patient care over the web, including the few participating nurses who were unable to perform in-person care (n=2). CONCLUSIONS: This evaluation provides insights into the integration of technology into standard health care services, such as in-clinic wound care. Using in-system nurses with access to electronic medical records and specialized knowledge facilitated app integration and continuity of care. This care delivery model satisfied nurse desires for flexible and remote work and reduced patient anxiety, potentially reducing postoperative wound care complications. Feasibility was negatively impacted by patients' technology literacy and few language options; additional patient training, education, and language support are needed to support equitable access. Adoption was impacted by a lack of perceived need for additional care; lower-touch or higher-acuity settings with a longer wait between visits could be a better fit for this type of nurse-led care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".