Patient Experiences With a Mobile Self-Care Solution for Low-Complex Orthopedic Injuries: Mixed Methods Study
Bibliographic record
Abstract
Background: The Dutch acute health care system faces challenges with limited resources and increasing patient numbers. To reduce outpatient follow-up, direct discharge (DD) has been implemented in over 30 out of 80 Dutch hospitals. With DD, no routine follow-up appointments are scheduled after the emergency department (ED) visit for low-complex, isolated, and stable musculoskeletal injuries. This policy is supported by information leaflets, a smartphone app, and a telephone helpline with human support. Growing evidence shows that DD is satisfactory, safe, and effective in reducing secondary health care use, but thorough patient experiences are lacking. Objective: The aim of this study was to explore the experiences of patients with DD to ensure durable adoption and to improve the treatment protocol. Methods: A mixed method study was conducted parallel to the implementation of DD in 3 hospitals. Data were collected through a survey directly after the ED visit, a survey 3 months post injury, and semistructured interviews. Quantitative data were reported descriptively, and qualitative data used thematic analysis. Outcomes included the Bowen feasibility parameters: implementation, acceptance, preliminary efficacy, and demand. All patients who consented to the study face-to-face with one of the 12 low-complex musculoskeletal injuries were included in the study during the implementation period. Results: Of the 429 patients who started the primary survey, 138 patients completed both surveys. A total of 18 semistructured interviews were conducted and analyzed. Patients reported a median treatment satisfaction score of 7.8 (IQR 6.6-8.8) on a 10-point scale of DD at the ED. Information quality was experienced as good (106/138, 77%), and most preferred DD over face-to-face follow-up (79/138, 59%). Patient information demands and app use varied among patients, with a median frequency of use of 4 times (ranging from 1 to 30). Conclusions: This study shows that patients consider DD a feasible and safe alternative to traditional treatment, with a favorable perception of its acceptability, efficacy, applicability, and demand. Nevertheless, response rates were relatively low, and personal nuances and preferences must be considered when implementing DD. Clinicians and policy makers can use the insights to improve DD and work towards the integration of DD into clinical practice and future guidelines.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".