Unit-level variation in bed alarm use in US hospitals.
Vincent S StaggsKea TurnerCatima PotterEmily CramerNancy DuntonLorraine C MionRonald I ShorrPublished in: Research in nursing & health (2020)
Bed and chair alarms are widely used in hospitals, despite lack of effectiveness and unintended negative consequences. In this cross-sectional, observational study, we examined alarm prevalence and contributions of patient- and unit-level factors to alarm use on 59 acute care nursing units in 57 US hospitals participating in the National Database of Nursing Quality Indicators®. Nursing unit staff reported data on patient-level fall risk and fall prevention measures for 1,489 patients. Patient-level propensity scores for alarm use were estimated using logistic regression. Expected alarm use on each unit, defined as the mean patient propensity-for-alarm score, was compared with the observed rate of alarm use. Over one-third of patients assessed had an alarm in the "on" position. Patient characteristics associated with higher odds of alarm use included recent fall, need for ambulation assistance, poor mobility judgment, and altered mental status. Observed rates of unit alarm use ranged from 0% to 100% (median 33%, 10th percentile 5%, 90th percentile 67%). Expected alarm use varied less (median 31%, 10th percentile 27%, and 90th percentile 45%). Only 29% of variability in observed alarm use was accounted for by expected alarm use. Unit assignment was a stronger predictor of alarm use than patient-level fall risk variables. Alarm use is common, varies widely across hospitals, and cannot be fully explained by patient fall risk factors; alarm use is driven largely by unit practices. Alarms are used too frequently and too indiscriminately, and guidance is needed for optimizing alarm use to reduce noise and encourage mobility in appropriate patients.
Keyphrases
- healthcare
- case report
- risk factors
- end stage renal disease
- mental health
- newly diagnosed
- ejection fraction
- cross sectional
- emergency department
- randomized controlled trial
- primary care
- quality improvement
- machine learning
- air pollution
- peritoneal dialysis
- acute care
- deep learning
- electronic health record
- artificial intelligence
- patient reported