Abstract 10619: Determination of Cost Drivers and Variation Associated with Isolated Aortic Valve Replacement Surgery: Evaluating the Impact of Interval Feedback and Barriers and Facilitators of Cost Awareness among Cardiac Surgeons and Health Care Providers
Bibliographic record
Abstract
Introduction: Marked variation among surgeons and relevant health care providers (HCPs) in aortic valve replacement (AVR) costs was demonstrated. Despite presentation of these data to HCPs, cost variation persists. We sought input from cardiac HCPsregarding intraoperative cost awareness, decision making and potential motivators towards greater cost consideration. Objectives: Three fold purpose: I) Determine the primary intraoperative cost drivers of performing an isolated AVR in a single-center Canadian hospital, II) Evaluate the impact of communicating cost and driver variability on decision making, and III) Identify the barriers and facilitators of incorporating cost considerations in isolated AVRs among HCPs. Methods: From May 2017-May 2019, data was collected on AVR cost drivers (n=216). There were three phases of data feedback to HCPs. Separate focus groups were held (Phase III) for cardiac HCPs including surgeons, anesthesiologists, perfusionists , residents, and OR nurses (n=27). Semi-structured interviews were used to elicit provider perspectives. Transcribed audio data was analyzed through the use of thematic analysis to develop a core set of common and comprehensive themes. Results: Data collection demonstrated marked inter-surgeon variation around the surgical costs of procedures. After feedback to HCPs, variation persisted and the median cost for AVRs increased. Five themes were identified from HCP groups: cost awareness, intraoperative decision making, influence surrounding intraoperative cost decision making, provider-based motivation for implementing intraoperative cost decision making, and cost drivers for an AVR. Conclusions: No difference in cost or variation before and after data feedback to HCPs was insufficient to impact surgeon’s behaviour in case costing, which motivated the qualitative work in the form of focus groups. HCPs demonstrated low cost awareness regarding cost drivers of AVRs. They expressed interest in engaging in cost decision making that was informed by cost containment. Measures such as listing the price of cost drivers in the OR and providing cost feedback could potentially encourage engagement in cost decision making for AVRs.
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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 | 0.012 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".