Cho Insook, Kim MiSoon, Song Mi Ra, Dykes Patricia C
Nursing Department, Inha University, Incheon, Republic of Korea.
Center for Patient Safety, Research, and Practice, Brigham and Women's Hospital, Boston, Massachusetts, USA.
JAMIA Open. 2023 Apr 6;6(2):ooad019. doi: 10.1093/jamiaopen/ooad019. eCollection 2023 Jul.
To assess whether a fall-prevention clinical decision support (CDS) approach using electronic analytics that stimulates risk-targeted interventions is associated with reduced rates of falls and injurious falls.
The CDS intervention included a machine-learning prediction algorithm, individual risk-factor identification, and guideline-based prevention recommendations. After a 5-month plan-do-study-act quality improvement initiative, the CDS intervention was implemented at an academic tertiary hospital and compared with the usual care using a pretest (lasting 24 months and involving 23 498 patients) and posttest (lasting 13 months and involving 17 341 patients) design in six nursing units. Primary and secondary outcomes were the rates of falls and injurious falls per 1000 hospital days, respectively. Outcome measurements were tested using a priori Poisson regression and adjusted with patient-level covariates. Subgroup analyses were conducted according to age.
The age distribution, sex, hospital and unit lengths of stay, number of secondary diagnoses, fall history, condition at admission, and overall fall rate per 1000 hospital days did not differ significantly between the intervention and control periods before (1.88 vs 2.05, respectively, =.1764) or after adjusting for demographics. The injurious-falls rate per 1000 hospital days decreased significantly before (0.68 vs 0.45, =.0171) and after (rate difference = -0.64, =.0212) adjusting for demographics. The differences in injury rates were greater among patients aged at least 65 years.
This study suggests that a well-designed CDS intervention employing electronic analytics was associated with a decrease in fall-related injuries. The benefits from this intervention were greater in elderly patients aged at least 65 years.
This study was conducted as part of a more extensive study registered with the Clinical Research Information Service (CRIS) (KCT0005378).
评估一种使用电子分析的预防跌倒临床决策支持（CDS）方法，该方法可促进针对风险的干预措施，是否与跌倒和伤害性跌倒发生率的降低相关。
CDS干预措施包括机器学习预测算法、个体风险因素识别以及基于指南的预防建议。经过为期5个月的计划-执行-研究-改进质量改进计划后，CDS干预措施在一家学术型三级医院实施，并在六个护理单元采用前测（持续24个月，涉及23498名患者）和后测（持续13个月，涉及17341名患者）设计与常规护理进行比较。主要和次要结局分别是每1000个住院日的跌倒率和伤害性跌倒率。结局测量采用先验泊松回归进行检验，并根据患者水平的协变量进行调整。根据年龄进行亚组分析。
在干预期和对照期之前（分别为1.88和2.05，P = 0.1764）或在调整人口统计学因素之后，年龄分布、性别、住院时间和科室住院时间、二级诊断数量、跌倒史、入院时状况以及每1000个住院日的总体跌倒率在干预组和对照组之间均无显著差异。在调整人口统计学因素之前（0.68对0.45，P = 0.0171）和之后（率差 = -0.64，P = 0.0212），每1000个住院日的伤害性跌倒率均显著降低。年龄至少65岁的患者伤害率差异更大。
本研究表明，采用电子分析的精心设计的CDS干预措施与跌倒相关伤害的减少有关。这种干预措施对年龄至少65岁的老年患者的益处更大。
本研究是在临床研究信息服务中心（CRIS）注册的一项更广泛研究的一部分（KCT0005378）。
