A Tool to Determine Financial Impact of Adverse Events in Health Care
Bibliographic record
Abstract
OBJECTIVES: Hospital leaders lack tools to determine the financial impact of poor patient outcomes and adverse events. To provide health-care leaders with decision support for investments to improve care, we created a tool, the Healthcare Quality Calculator (HQCal), which uses institution-specific financial data to calculate impact of poor patient outcomes or quality improvement on present and future margin. METHODS: Excel and Web-based versions of the HQCal were based on a cohort study framework and created with modular components including major drivers of cost and reimbursement. RESULTS: The Healthcare Quality Calculator (HQCal) compares payment, cost, and profit/loss for patients with and without poor outcomes or quality issues. Cost and payment information for groups with and without quality issues are used by the HQCal to calculate profit or loss. Importantly, institution-specific payment and cost data are used to calculate financial impact and attributable cost associated with poor patient outcomes, adverse events, or quality issues. Because future cost and reimbursement changes can be forecast, the HQCal incorporates a forward-looking component. The flexibility of the HQCal was demonstrated using surgical site infections after abdominal surgery and postoperative surgical airway complications. CONCLUSIONS: The Healthcare Quality Calculator determines financial impact of poor patient outcomes and the benefit of initiatives to improve quality. The calculator can identify quality issues that would provide the largest financial benefit if improved; however, it cannot identify specific interventions. The calculator provides a tool to improve transparency regarding both short- and long-term financial consequences of funding, or failing to fund, initiatives to close gaps in quality or improve patient outcomes.
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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 | 0.021 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.024 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".