E-Health Decision Support Technologies in the Prevention and Management of Pressure Ulcers
Bibliographic record
Abstract
Pressure ulcers are problematic across clinical settings, negatively impacting patient morbidity and mortality while resulting in substantial costs to the healthcare system. E-health clinical decision support technologies can play a key role in improving pressure ulcer-related outcomes. This systematic review aimed to assess the impact of electronic health decision support interventions on pressure ulcer management and prevention. A systematic search was conducted in PubMed, Scopus, Cumulative Index to Nursing and Allied Health Literature, and Cochrane. Nineteen articles, published from 2010 to 2020, were included for review. The findings of this review showed promising results regarding the usability and accuracy of electronic health decision support tools to aid in pressure ulcer prevention and management. Evidence indicated improved clinician adherence to pressure ulcer prevention practices and decreased healthcare costs postimplementation of an electronic health decision support intervention. However, the studies included in this review did not consistently show reductions in pressure ulcer prevalence, incidence, or risk. Most of the articles included in the review were limited by small sample sizes drawn from single hospitals or long-term care homes. More high-quality studies are needed to determine the types of electronic health decision support tools that can drive sustainable improvements to 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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".