Workplace Predictors of Quality and Safe Patient Care Delivery Among Nurses Using Machine Learning Techniques
| Autor: | Farinaz Havaei, Sheila A. Boamah, Xuejun Ryan Ji |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
business.industry
media_common.quotation_subject Psychological intervention Nurses Survey research Nursing Staff Hospital Mental health Patient care Machine Learning Workload management Cross-Sectional Studies Nursing Surveys and Questionnaires Humans Medicine Quality (business) Patient Care Patient Care Delivery Workplace business General Nursing media_common |
| Zdroj: | Journal of Nursing Care Quality. 37:103-109 |
| ISSN: | 1057-3631 |
| DOI: | 10.1097/ncq.0000000000000600 |
| Popis: | BACKGROUND Working in unhealthy environments is associated with negative nurse and patient outcomes. Previous body of evidence in this area is limited as it investigated only a few factors within nurses' workplaces. PURPOSE The purpose of this study was to identify the most important workplace factors predicting nurses' provision of quality and safe patient care using a 13-factor measure of workplace conditions. METHODS A cross-sectional correlational survey study involving 4029 direct care nurses in British Columbia was conducted using random forest data analytics methods. RESULTS Nurses' reports of healthier workplaces, particularly workload management, psychological protection, physical safety and engagement, were associated with higher ratings of quality and safe patient care. CONCLUSION These workplace conditions are perceived to impact patient care through influencing nurses' mental health. To ensure a high standard of patient care, data-driven policies and interventions promoting overall nurse mental health and well-being are urgently required. |
| Databáze: | OpenAIRE |
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