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量化医疗服务团队对患有多种疾病患者住院结局的影响:对临床信息学的启示

Quantifying care delivery team influences on the hospitalization outcomes of patients with multimorbidity: Implications for clinical informatics.

作者信息

Williams Tremaine B, Robins Taiquitha, Vincenzo Jennifer L, Lipschitz Riley, Baghal Ahmad, Sexton Kevin Wayne

机构信息

Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.

Department of Physical Therapy, University of Arkansas for Medical Sciences, Fayetteville, AR, USA.

出版信息

J Multimorb Comorb. 2023 May 13;13:26335565231176168. doi: 10.1177/26335565231176168. eCollection 2023 Jan-Dec.

Abstract

The primary objective was to quantify the influences of care delivery teams on the outcomes of patients with multimorbidity. Electronic medical record data on 68,883 patient care encounters (i.e., 54,664 patients) were extracted from the Arkansas Clinical Data Repository. Social network analysis assessed the minimum care team size associated with improved care outcomes (i.e., hospitalizations, days between hospitalizations, and cost) of patients with multimorbidity. Binomial logistic regression further assessed the influence of the presence of seven specific clinical roles. When compared to patients without multimorbidity, patients with multimorbidity had a higher mean age (i.e., 47.49 40.61), a higher mean dollar amount of cost per encounter (i.e., $3,068 $2,449), a higher number of hospitalizations (i.e., 25 4), and a higher number of clinicians engaged in their care (i.e., 139,391 7,514). Greater network density in care teams (i.e., any combination of two or more Physicians, Residents, Nurse Practitioners, Registered Nurses, or Care Managers) was associated with a 46-98% decreased odds of having a high number of hospitalizations. Greater network density (i.e., any combination of two or more Residents or Registered Nurses) was associated with 11-13% increased odds of having a high cost encounter. Greater network density was not significantly associated with having a high number of days between hospitalizations. Analyzing the social networks of care teams may fuel computational tools that better monitor and visualize real-time hospitalization risk and care cost that are germane to care delivery.

摘要

主要目标是量化医疗服务团队对患有多种疾病患者治疗结果的影响。从阿肯色州临床数据存储库中提取了关于68,883次患者护理接触(即54,664名患者)的电子病历数据。社会网络分析评估了与改善患有多种疾病患者的护理结果(即住院次数、两次住院之间的天数和费用)相关的最小护理团队规模。二项式逻辑回归进一步评估了七个特定临床角色的存在所产生的影响。与未患有多种疾病的患者相比,患有多种疾病的患者平均年龄更高(即47.49岁对40.61岁),每次接触的平均费用金额更高(即3,068美元对2,449美元),住院次数更多(即25次对4次),以及参与其护理的临床医生数量更多(即139,391名对7,514名)。护理团队中更高的网络密度(即两名或更多医生、住院医师、执业护士、注册护士或护理经理的任何组合)与住院次数较多的几率降低46 - 98%相关。更高的网络密度(即两名或更多住院医师或注册护士的任何组合)与高费用接触几率增加11 - 13%相关。更高的网络密度与两次住院之间天数较多没有显著关联。分析护理团队的社会网络可能会推动计算工具的发展,这些工具能够更好地监测和可视化与医疗服务相关的实时住院风险和护理成本。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8798/10184258/363c1831834b/10.1177_26335565231176168-fig1.jpg

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