Financial and Clinical Risk Evaluation of Pressure Injuries in US Hospitals: A Business Case for Initiating Quality Improvement.
Bibliographic record
Abstract
INTRODUCTION: Pressure injuries (PIs) are a serious, avoidable condition that affect many patients during hospital stays. Yet, to date, there is no comprehensive assessment of the financial and clinical risks of PIs. OBJECTIVE: This study evaluates the cost of treatment, impact of reimbursement policies, and clinical consequences of PIs for US hospitals. METHODS: A financial and clinical calculator was created to estimate the impact of PI prevention using a traditional literature review to drive assumptions. RESULTS: Two drivers of hospital revenue loss resulting from PIs were identified: nonpayment for PI treatment by health insurance providers and personal injury litigation. Increased hospital length of stay (LOS) and patient mortality associated with PIs further contributed to negative consequences. For an average 160-bed hospital, the authors estimated an annual total financial risk of $5.97 million, 911 days added to LOS, and 16.4 deaths related to avoidable PIs. CONCLUSIONS: Results of this analysis will be useful for health care organizations implementing quality improvement initiatives and new technologies, such as digital wound care management systems, to reduce the prevalence of PIs, thereby protecting patients and mitigating financial and clinical risks.
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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 | 0.039 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".