Trends in Pressure Injury Prevalence Rates and Average Days to Healing Associated With Adoption of a Comprehensive Wound Care Program and Technology in Skilled Nursing Facilities in the United States
Bibliographic record
Abstract
INTRODUCTION: A large SNF system in the United States adopted a holistic wound care model that included an AI DWMS to improve PI care. OBJECTIVE: To compare the trend in PI point prevalence rates and average days to healing linked to adopting technology in practice from 2021 to 2022, and to assess the rate of received PI F686 citations in facilities that adopted the technology compared with those that did not. METHODS: The study used the DWMS database to compare anonymized PI data assessed in 2021 (15 583 patients) vs 2022 (30 657 patients) from all SNF facilities that adopted the technology in 2021 and 2022. F686 citations data were provided by the SNF organization. RESULTS: There was a 13.1% reduction in PI prevalence from 2021 to 2022 across all PI stages. Facilities that adopted the technology demonstrated a significant reduction in days to healing from 2021 to 2022, with an average of 17.7 days saved per PI or a 37.4% faster healing rate (P < .001). A significant reduction in the average days to healing was noted for all PI stages, with the most significant savings observed for stages 3 and 4, with an average savings of 35 days (stage 3) and 85 days (stage 4) in 2022 vs 2021 (P < .001). From 2021 to 2022, facilities that adopted the technology reported an overall 8.2% reduction in F-686 citations severity >G compared to those that did not adopt the technology. CONCLUSION: Use of technology as part of a comprehensive wound care program has the potential to not only improve patient care and quality of life, but to realize considerable annual savings in additional PI out-of-pocket expenses (up to $1 410 000) and of clinicians' time (44 808 hours).
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".