Effects of removing a fee-for-service incentive on specialist chronic disease services: a time-series analysis
Bibliographic record
Abstract
INTRODUCTION: Physician payment models are known to affect the nature and volume of services provided. Our objective was to study the effects of removing a financial incentive, the fee-for-service premium, on the provision of chronic disease follow-up services by internal medicine, cardiology, nephrology and gastroenterology specialists. METHODS: We collected linked administrative health care data for the period 1 April 2013 to 31 March 2017 from databases held at the Institute for Clinical Evaluative Sciences (ICES) in Ontario, Canada. We conducted a time-series analysis before and after the removal of the fee-for-service premium on 1 April 2015. The primary outcome was total monthly visits for chronic disease follow-up services. Secondary outcomes were monthly visits for total follow-up services and new patient consultations. We compared internal medicine, cardiology, nephrology and gastroenterology specialists practising during the study timeframe with respirology, hematology, endocrinology, rheumatology and infectious diseases specialists who remained eligible to claim the premium. We chose this comparison group as these are all subspecialties of internal medicine, providing similar services. RESULTS: The number of chronic disease follow-up visits decreased significantly after removal of the premium, but there was no decrease in total follow-up visits. There was also a significant downward trend in new patient consultations. No changes were observed in the comparison group. CONCLUSION: The decrease in volume of chronic disease follow-up visits can be explained by diagnostic criteria being met less often, rather than an actual reduction in services provided. Potential effects on patient outcomes require further exploration.
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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 | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".