Predicting pressure injury risk in hospitalised patients using machine learning with electronic health records: a US multilevel cohort study.
William V PadulaDavid G ArmstrongPeter J PronovostSuchi SariaPublished in: BMJ open (2024)
These data could help hospitals conserve resources within a critical period of patient vulnerability of hospital-acquired pressure injury which is not reimbursed by US Medicare; thus, conserving between 30 000 and 90 000 labour-hours per year in an average 500-bed hospital. Hospitals can use this predictive algorithm to initiate a quality improvement programme for pressure injury prevention and further customise the algorithm to patient-specific variation by facility.
Keyphrases
- electronic health record
- healthcare
- adverse drug
- end stage renal disease
- machine learning
- deep learning
- ejection fraction
- chronic kidney disease
- newly diagnosed
- clinical decision support
- climate change
- randomized controlled trial
- acute care
- prognostic factors
- peritoneal dialysis
- case report
- neoadjuvant chemotherapy
- patient reported outcomes
- big data
- study protocol
- clinical trial
- emergency department
- patient reported
- early breast cancer