Will Kristen K, Liang Yue, Chi Chih-Lin, Lamb Gerri, Todd Michael, Delaney Connie
Arizona State University, Phoenix, AZ.
University of Minnesota, Minneapolis, MN.
J Patient Cent Res Rev. 2024 Apr 2;11(1):18-28. doi: 10.17294/2330-0698.2019. eCollection 2024 Spring.
Team-based care has been linked to key outcomes associated with the Quadruple Aim and a key driver of high-value patient-centered care. Use of the electronic health record (EHR) and machine learning have significant potential to overcome previous barriers to studying the impact of teams, including delays in accessing data to improve teamwork and optimize patient outcomes.
This study utilized a large EHR dataset (n=316,542) from an urban health system to explore the relationship between team composition and patient activation, a key driver of patient engagement. Teams were operationalized using consensus definitions of teamwork from the literature. Patient activation was measured using the Patient Activation Measure (PAM). Results from multilevel regression analyses were compared to machine learning analyses using multinomial logistic regression to calculate propensity scores for the effect of team composition on PAM scores. Under the machine learning approach, a causal inference model with generalized overlap weighting was used to calculate the average treatment effect of teamwork.
Seventeen different team types were observed in the data from the analyzed sample (n=12,448). Team sizes ranged from 2 to 5 members. After controlling for confounding variables in both analyses, more diverse, multidisciplinary teams (team size of 4 or more) were observed to have improved patient activation scores.
This is the first study to explore the relationship between team composition and patient activation using the EHR and big data analytics. Implications for further research using EHR data and machine learning to study teams and other patient-centered care are promising and could be used to advance team science.
基于团队的护理与与四重目标相关的关键结果以及高价值以患者为中心的护理的关键驱动因素相关联。电子健康记录（EHR）的使用和机器学习具有巨大潜力，可克服以往研究团队影响的障碍，包括获取数据以改善团队合作和优化患者结果方面的延迟。
本研究利用来自城市卫生系统的大型EHR数据集（n = 316,542）来探讨团队组成与患者激活之间的关系，患者激活是患者参与的关键驱动因素。团队根据文献中团队合作的共识定义进行运作。使用患者激活量表（PAM）测量患者激活情况。将多水平回归分析的结果与使用多项逻辑回归的机器学习分析进行比较，以计算团队组成对PAM分数影响的倾向得分。在机器学习方法下，使用具有广义重叠加权的因果推断模型来计算团队合作的平均治疗效果。
在分析样本（n = 12,448）的数据中观察到17种不同的团队类型。团队规模从2人到5人不等。在两项分析中控制混杂变量后，观察到更多样化的多学科团队（团队规模为4人或更多）的患者激活分数有所提高。
这是第一项使用EHR和大数据分析来探索团队组成与患者激活之间关系的研究。使用EHR数据和机器学习进一步研究团队及其他以患者为中心的护理的前景广阔，可用于推动团队科学发展。
