Effectiveness of a financial incentive to physicians for timely follow-up after hospital discharge: a population-based time series analysis
Bibliographic record
Abstract
BACKGROUND: Timely follow-up after hospital discharge may decrease readmission to hospital. Financial incentives to improve follow-up have been introduced in the United States and Canada, but it is unknown whether they are effective. Our objective was to evaluate the impact of an incentive program on timely physician follow-up after hospital discharge. METHODS: We conducted an interventional time series analysis of all medical and surgical patients who were discharged home from hospital between Apr. 1, 2002, and Jan. 30, 2015, in Ontario, Canada. The intervention was a supplemental billing code for physician follow-up within 14 days of discharge from hospital, introduced in 2006. The primary outcome was an outpatient visit within 14 days of discharge. Secondary outcomes were 7-day follow-up and a composite of emergency department visits, nonelective hospital readmission and death within 14 days. RESULTS: We included 8 008 934 patient discharge records. The incentive code was claimed in 31% of eligible visits by 51% of eligible physicians, and cost $17.5 million over the study period. There was no change in the average monthly rate of outcomes in the year before the incentive was introduced compared with the year following introduction: 14-day follow-up (66.5% v. 67.0%, overall p = 0.5), 7-day follow-up (44.9% v. 44.9%, overall p = 0.5) and composite outcome (16.7% v. 16.9%, overall p = 0.2). INTERPRETATION: Despite uptake by physicians, a financial incentive did not alter follow-up after hospital discharge. This lack of effect may be explained by features of the incentive or by extra-physician barriers to follow-up. These barriers should be considered by policymakers before introducing similar initiatives.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".