Patient and Provider Experiences With a Digital App to Improve Compliance With Enhanced Recovery After Surgery (ERAS) Protocols: Mixed Methods Evaluation of a Canadian Experience
Bibliographic record
Abstract
BACKGROUND: Of all the care provided in health care systems, major surgical interventions are the costliest and can carry significant risks. Enhanced Recovery After Surgery (ERAS) is a bundle of interventions that help improve patient outcomes and experience along their surgical journey. However, given that patients can be overwhelmed by the multiple tasks that they are expected to follow, a digital application, the ERAS app, was developed to help improve the implementation of ERAS. OBJECTIVE: The objective of this work was to conduct a thorough assessment of patient and provider experiences using the ERAS app. METHODS: Patients undergoing colorectal or gynecological oncology surgery at 2 different hospitals in the province of Alberta, Canada, were invited to use the ERAS app and report on their experiences using it. Likewise, care providers were recruited to participate in this study to provide feedback on the performance of this app. Data were collected by an online survey and using qualitative interviews with participants. NVivo was used to analyze qualitative interview data, while quantitative data were analyzed using Excel and SPSS. RESULTS: Overall, patients found the app to be helpful in preparation for and recovery after surgery. Patients reported having access to reliable unbiased information regarding their surgery, and the app provided them with clarity of actions needed along their surgical journey and enhanced the self-management of their care. Clinicians found that the ERAS app was easy to navigate, was simple for older adults, and has the potential to decrease unnecessary visits and phone calls to care providers. Overall, this proof-of-concept study on the use of a digital health app to accompany patients during their health care journey has shown positive results. CONCLUSIONS: This is an important finding considering the massive investment and interest in promoting digital health in health care systems around the world.
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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 | 0.039 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".