人员配备
可比性
医学
技能组合
梅德林
一致性(知识库)
护理部
重症监护医学
医疗急救
急诊医学
计算机科学
经济
法学
政治学
医疗保健
人工智能
组合数学
经济增长
数学
作者
Helen Myers,Judith D. Pugh,Di Twigg
出处
期刊:Collegian
[Elsevier BV]
日期:2017-10-19
卷期号:25 (4): 447-456
被引量:19
标识
DOI:10.1016/j.colegn.2017.10.004
摘要
Background Nurse-sensitive patient outcomes that are suitable for general medical and surgical settings are well developed. Indicators developed for general ward settings may not be suitable for stand-alone high acuity areas; therefore, a different set of indicators is required. Aim The aim of this review was to identify suitable indicators for measuring the impact of nurse staffing and nurse skill mix variations on patient outcomes in stand-alone high acuity areas. Methods A systematic review of the literature was undertaken for studies published between January 2000 and November 2016. Suitable indicators were identified based on simple criteria. That is, if there were at least three studies that found a significant relationship between the outcome and staffing variables and at least 50% of all the studies that investigated that outcome reported a significant association, that variable was included in the list of potential outcomes. Findings This review identified eight indicators from 44 eligible research articles. These were: mortality, length of stay, central-line-associated bloodstream infection, ventilator-associated pneumonia, sepsis, falls with injury, reintubation, and medication errors. Discussion Further work is needed to clarify the definitions for each of the indicators. Standard definitions should be developed using algorithms linked to International Classification of Diseases codes to ensure consistency and comparability across studies. The majority of these outcomes could be measured using administrative patient datasets. Reintubation and medication errors may be difficult to measure with available datasets requiring specialised data collections. Conclusion This comprehensive review identified a number of indicators that could be developed for further testing to monitor the quality of nursing care in Intensive Care Units.
科研通智能强力驱动
Strongly Powered by AbleSci AI