Nursing implications of an early warning system implemented to reduce adverse events: a qualitative study

Autor: Emilie J Braun, Siddhartha Singh, Annie C Penlesky, Erin A Strong, Jeana M Holt, Kathlyn E Fletcher, Michael E Stadler, Ann B Nattinger, Bradley H Crotty
Rok vydání: 2022
Předmět:
Zdroj: BMJ Quality & Safety. 31:716-724
ISSN: 2044-5423
2044-5415
DOI: 10.1136/bmjqs-2021-014498
Popis: BackgroundUnrecognised changes in a hospitalised patient’s clinical course may lead to a preventable adverse event. Early warning systems (EWS) use patient data, such as vital signs, nursing assessments and laboratory values, to aid in the detection of early clinical deterioration. In 2018, an EWS programme was deployed at an academic hospital that consisted of a commercially available EWS algorithm and a centralised virtual nurse team to monitor alerts. Our objective was to understand the nursing perspective on the use of an EWS programme with centralised monitoring.MethodsWe conducted and audio-recorded semistructured focus groups during nurse staff meetings on six inpatient units, stratified by alert frequency (high: >100 alerts/month; medium: 50–100 alerts/month; low: ResultsWe conducted 28 focus groups with 227 bedside nurses across all shifts. We identified six principal themes: (1) Alert timeliness, nurses reported being aware of the patient’s deterioration before the EWS alert, (2) Lack of accuracy, nurses perceived most alerts as false positives, (3) Workflow interruptions caused by EWS alerts, (4) Questions of actionability of alerts, nurses were often uncertain about next steps, (5) Concerns around an underappreciation of core nursing skills via reliance on the EWS programme and (6) The opportunity cost of deploying the EWS programme.ConclusionThis qualitative study of nurses demonstrates the importance of earning user trust, ensuring timeliness and outlining actionable next steps when implementing an EWS. Careful attention to user workflow is required to maximise EWS impact on improving hospital quality and patient safety.
Databáze: OpenAIRE