Feasibility of a Web-Based and Mobile-Supported Follow-Up Treatment Pathway for Adult Patients With Orthopedic Trauma in the Netherlands: Concurrent Mixed Methods Study
Bibliographic record
Abstract
Background: Orthopedic trauma care encounters challenges in follow-up treatment due to limited patient information provision, treatment variation, and the chaotic settings in which it is provided. Additionally, pressure on health care resources is rising worldwide. In response, digital follow-up treatment pathways were implemented for patients with orthopedic trauma, aiming to optimize health care resource use and enhance patient experiences. Objective: We aim to assess digital follow-up treatment pathway feasibility from the patient's perspective and its impact on health care resource use. Methods: A concurrent mixed methods study was conducted parallel to implementation of digital follow-up treatment pathways in an urban level-2 trauma center. Inclusion criteria were (1) minimum age of 18 years, (2) an active web-based patient portal account, (3) ability to read and write in Dutch, and (4) no cognitive or preexisting motor impairment. Data were collected via electronic patient records, and surveys at three time points: day 1-3, 4-6 weeks, and 10-12 weeks after an initial emergency department visit. Semistructured interviews were performed at 10-12 weeks post injury. Anonymous data from a pre-existing database were used to compare health care resource use between the digital treatment pathways and traditional treatment. Quantitative data were reported descriptively. A thematic analysis was used for qualitative data. All outcomes were categorized according to the Bowen feasibility parameters: acceptability, demand, implementation, integration, and limited efficacy. Results: Sixty-six patients were included for quantitative data collection. Survey response rates were 100% (66/66) at day 1-3, 92% (61/66) at 4-6 weeks, and 79% (52/66) at 10-12 weeks. For qualitative data collection, 15 semistructured interviews were performed. Patients reported median satisfaction scores of 7 (IQR 6-8) with digital treatment pathways and 8 (IQR 7-9) for overall treatment, reflecting positive experiences regarding functionality, actual and intended use, and treatment safety. Digital treatment pathways reduced secondary health care use, with fewer follow-up appointments by phone (median 0, IQR 0-0) versus the control group (median 1, IQR 0-1; P<.001). Consequently, fewer physicians were involved in follow-up treatment for the intervention group (median 2, IQR 1-2) than for the control group (median 2, IQR 1-3; P<.001). Fewer radiographs were performed for the intervention group (median 1, IQR 0-1) than for the control group (P=.01). Qualitative data highlighted positive experiences with functionalities, intended use, and safety, but also identified areas for improvement, including managing patient expectations, platform usability, and protocol adherence. Conclusions: Use of digital follow-up treatment pathways is feasible, yielding satisfactory patient experiences and reducing health care resource use. Recommendations for improvement include early stakeholder involvement, integration of specialized digital tools within electronic health record systems, and hands-on training for health care professionals. These insights can guide clinicians and policy makers in effectively integrating similar tools into clinical practice.
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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 | 0.025 | 0.044 |
| 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 | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".