A Web-Based Clinical Decision Support Tool for Primary Health Care Management of Back Pain: Development and Mixed Methods Evaluation
Bibliographic record
Abstract
BACKGROUND: Many patients with back pain do not receive health care in accordance with best practice recommendations. Implementation trials to address this issue have had limited success. Despite the known effectiveness of clinical decision support systems (CDSS), none of these are available for back pain management. OBJECTIVE: The objective of our study was to develop a Web-based CDSS to support Australian general practitioners (GPs) to diagnose and manage back pain according to guidelines. METHODS: Asking a panel of international experts to review recommendations for sixteen clinical vignettes validated the tool. It was then launched nationally as part of National Pain Week and promoted to GPs via a media release and clinic based visits. Following this, a mixed methods evaluation was conducted to determine tool feasibility, acceptability, and utility. The 12 month usage data were analyzed, and in-depth, semistructured interviews with 20 GPs were conducted to identify barriers and enablers to uptake. RESULTS: The tool had acceptable face validity when reviewed by experts. Over a 12 month period there were 7125 website visits with 4503 (63.20%) unique users. Assuming most unique users are GPs, around one quarter of the country's GPs may have used the tool at least once. Although usage was high, GP interviews highlighted the sometimes complex nature of management where the tool may not influence care. Conversely, several "touch-points", whereby the tool may exert its influence, were identified, most notably patient engagement. CONCLUSIONS: A novel CDSS tool has the potential to assist with evidence-based management of back pain. A clinical trial is required to determine its impact on practitioner and patient outcomes.
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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 | 0.118 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".