A scoping review to explore the suitability of interactive voice response to conduct automated performance measurement of the patient’s experience in primary care
Bibliographic record
Abstract
INTRODUCTION: Practice-based performance measurement is fundamental for improvement and accountability in primary care. Traditional performance measurement of the patient's experience is often too costly and cumbersome for most practices. OBJECTIVE/METHODS: This scoping review explores the literature on the use of interactive voice response (IVR) telephone surveys to identify lessons for its use for collecting data on patient-reported outcome measures at the primary care practice level. RESULTS: The literature suggests IVR could potentially increase the capacity to reach more representative patient samples and those traditionally most difficult to engage. There is potential for long-term cost effectiveness and significant decrease of the burden on practices involved in collecting patient survey data. Challenges such as low response rates, mode effects, high initial set-up costs and maintenance fees, are also reported and require careful attention. CONCLUSION: This review suggests IVR may be a feasible alternative to traditional patient data collection methods, which should be further explored.
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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 | 0.016 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".