Kim Song-Hee, Chan Carri W, Olivares Marcelo, Escobar Gabriel J
1Data Sciences and Operations, Marshall School of Business, University of Southern California, Los Angeles, CA.2Decision, Risk, and Operations Division, Columbia Business School, New York, NY.3Departamento de Ingenieria Industrial, Universidad de Chile, Santiago, Chile.4Kaiser Permanente Northern California, Division of Research, Oakland, CA.5Kaiser Permanente Medical Center, Department of Inpatient Pediatrics, Walnut Creek, CA.
Crit Care Med. 2016 Oct;44(10):1814-21. doi: 10.1097/CCM.0000000000001850.
To employ automated bed data to examine whether ICU occupancy influences ICU admission decisions and patient outcomes.
Retrospective study using an instrumental variable to remove biases from unobserved differences in illness severity for patients admitted to ICU.
Fifteen hospitals in an integrated healthcare delivery system in California.
Seventy thousand one hundred thirty-three episodes involving patients admitted via emergency departments to a medical service over a 1-year period between 2008 and 2009.
None.
A third of patients admitted via emergency department to a medical service were admitted under high ICU congestion (more than 90% of beds occupied). High ICU congestion was associated with a 9% lower likelihood of ICU admission for patients defined as eligible for ICU admission. We further found strong associations between ICU admission and patient outcomes, with a 32% lower likelihood of hospital readmission if the first inpatient unit was an ICU. Similarly, hospital length of stay decreased by 33% and likelihood of transfer to ICU from other units-including ICU readmission if the first unit was an ICU-decreased by 73%.
High ICU congestion is associated with a lower likelihood of ICU admission, which has important operational implications and can affect patient outcomes. By taking advantage of our ability to identify a subset of patients whose ICU admission decisions are affected by congestion, we found that, if congestion were not a barrier and more eligible patients were admitted to ICU, this hospital system could save approximately 7.5 hospital readmissions and 253.8 hospital days per year. These findings could help inform future capacity planning and staffing decisions.
利用自动床位数据来研究重症监护病房(ICU)占用情况是否会影响ICU入院决策及患者预后。
采用工具变量进行回顾性研究,以消除因入住ICU患者疾病严重程度未观察到的差异所导致的偏差。
加利福尼亚州一个综合医疗服务系统中的15家医院。
2008年至2009年期间,70133例通过急诊科收治到内科服务的患者病例。
无。
通过急诊科收治到内科服务的患者中,三分之一是在ICU高度拥堵(床位占用率超过90%)的情况下入院的。ICU高度拥堵与被定义为符合ICU入院条件的患者入院可能性降低9%相关。我们还进一步发现ICU入院与患者预后之间存在密切关联,如果首个住院科室是ICU,再次入院的可能性会降低32%。同样,住院时间缩短了33%,从其他科室转入ICU的可能性(包括如果首个科室是ICU再次入住ICU的情况)降低了73%。
ICU高度拥堵与较低的ICU入院可能性相关,这具有重要的运营意义,并且可能影响患者预后。通过利用我们识别出的一部分ICU入院决策受拥堵影响的患者的能力,我们发现,如果拥堵不是障碍且更多符合条件的患者入住ICU,该医院系统每年可节省约7.5次再次入院和253.8个住院日。这些发现有助于为未来的容量规划和人员配置决策提供参考。