Development of a Digital Health Intervention to Support Patients on a Waitlist for Orthopedic Specialist Care: Co-Design Study
Bibliographic record
Abstract
BACKGROUND: The demand for orthopedic specialist consultations for patients with osteoarthritis in public hospitals is high and continues to grow. Lengthy waiting times are increasingly affecting patients from low socioeconomic and culturally and linguistically diverse backgrounds who are more likely to rely on public health care. OBJECTIVE: This study aimed to co-design a digital health intervention for patients with OA who are waiting for an orthopedic specialist consultation at a public health service, which is located in local government areas (LGAs) of identified social and economic disadvantage. METHODS: The stakeholders involved in the co-design process included the research team; end users (patients); clinicians; academic experts; senior hospital staff; and a research, design, and development agency. The iterative co-design process comprised several key phases, including the collation and refinement of evidence-based information by the research team, with assistance from academic experts. Structured interviews with 16 clinicians (female: n=10, 63%; male: n=6, 38%) and 11 end users (age: mean 64.3, SD 7.2 y; female: n=7, 64%; male: n=4, 36%) of 1-hour duration were completed to understand the requirements for the intervention. Weekly workshops were held with key stakeholders throughout development. A different cohort of 15 end users (age: mean 61.5, SD 9.7 y; female: n=12, 80%; male: n=3, 20%) examined the feasibility of the study during a 2-week testing period. The System Usability Scale was used as the primary measure of intervention feasibility. RESULTS: Overall, 7 content modules were developed and refined over several iterations. Key themes highlighted in the clinician and end user interviews were the diverse characteristics of patients, the hierarchical structure with which patients view health practitioners, the importance of delivering information in multiple formats (written, audio, and visual), and access to patient-centered information as early as possible in the health care journey. All content was translated into Vietnamese, the most widely spoken language following English in the local government areas included in this study. Patients with hip and knee osteoarthritis from culturally and linguistically diverse backgrounds tested the feasibility of the intervention. A mean System Usability Scale score of 82.7 (SD 16) was recorded for the intervention, placing its usability in the excellent category. CONCLUSIONS: Through the co-design process, we developed an evidence-based, holistic, and patient-centered digital health intervention. The intervention was specifically designed to be used by patients from diverse backgrounds, including those with low health, digital, and written literacy levels. The effectiveness of the intervention in improving the physical and mental health of patients will be determined by a high-quality randomized controlled trial.
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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 | 0.041 | 0.037 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".