Policies to Optimize Physician Billing Data in Academic Alternative Relationship Payment Plans: Practices and Perspectives
Bibliographic record
Abstract
ABSTRACT ObjectivesChanges in physician reimbursement policies may hinder the collection of billing claims in administrative databases. Various provincial academic alternative payment programs (APPs) use incentive- or punitive-based tools to motivate physicians to submit billing claims called shadow billings; however, these incentives are not well documented in the literature. We conducted a nation-wide survey and semi-structured face-to-face interviews in Alberta, Canada to determine existing policies and guidelines for incentivizing and promoting physician billing practices. ApproachMail and online surveys were sent out to academic department head physicians in the following provinces: British Columbia, Alberta, Saskatchewan, Manitoba, Ontario, New Brunswick, Prince Edward Island and Newfoundland and Labrador. Face-to-face interviews were conducted in the province of Alberta with managers, government stakeholders, and physicians/administrators from academic APPs and Fee-for-Service plans. Face-to-face interviews and responses by mail and email submission were summarized using content analysis grouped by question type. ResultsIn total, there were 46 respondents (15 interviews, 26 mail/online). Content analysis revealed three primary perspectives, grouped at the level of individual physician, academic, and government. Across all of these unique perspectives, three primary themes emerged: 1) governance; 2) accountability; and 3) funding. Within these themes, findings were categorized as either (a) instruments or tools to promote physician billing in AAPPs; (b) enabling factors to support physician billing in AAPPs; and, (c) constraining factors impeding physician billing in AAPPs. ConclusionAccording to the majority of our respondents, financial disincentives (i.e. income at risk, financial clawbacks) appear to be most effective as a mechanism to motivate physicians within an academic APP to submit their billings. However, key barriers to successful implementation and delivery of academic APPs include a lack of alignment between government stakeholders, academic leadership and APP physician members and differences in the organizational and accountability structures of APP plans between academic facilities. It is necessary in moving forward to achieve commonly defined standards and frameworks between the various APP models across provinces and academic institutions.
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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 | 0.040 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".