Department of Industrial and Systems Engineering, University of Washington, Seattle, Washington, United States of America.
The University of Washington Department of Surgery, Harborview Medical Center, Seattle, Washington, United States of America.
PLoS One. 2024 Aug 22;19(8):e0306299. doi: 10.1371/journal.pone.0306299. eCollection 2024.
Injuries are a leading cause of death in the United States. Trauma systems aim to ensure all injured patients receive appropriate care. Hospitals that participate in a trauma system, trauma centers (TCs), are designated with different levels according to guidelines that dictate access to medical and research resources but not specific surgical care. This study aimed to identify patterns of injury care that distinguish different TCs and hospitals without trauma designation, non-trauma centers (non-TCs).
We extracted hospital-level features from the state inpatient hospital discharge data in Washington state, including all TCs and non-TCs, in 2016. We provided summary statistics and tested the differences of each feature across the TC/non-TC levels. We then conducted 3 sets of unsupervised clustering analyses using the Partition Around Medoids method to determine which hospitals had similar features. Set 1 and 2 included hospital surgical care (volume or distribution) features and other features (e.g., the average age of patients, payer mix, etc.). Set 3 explored surgical care without additional features.
The clusters only partially aligned with the TC designations. Set 1 found the volume and variation of surgical care distinguished the hospitals, while in Set 2 orthopedic procedures and other features such as age, social vulnerability indices, and payer types drove the clusters. Set 3 results showed that procedure volume rather than the relative proportions of procedures aligned more, though not completely, with TC designation.
Unsupervised machine learning identified surgical care delivery patterns that explained variation beyond level designation. This research provides insights into how systems leaders could optimize the level allocation for TCs/non-TCs in a mature trauma system by better understanding the distribution of care in the system.
在美国，受伤是导致死亡的主要原因。创伤系统旨在确保所有受伤患者都能得到适当的治疗。参与创伤系统的医院，即创伤中心（TCs），根据规定的指南被指定为不同级别，这些指南决定了获取医疗和研究资源的机会，但不包括特定的外科护理。本研究旨在确定区分不同 TC 和无创伤指定的医院（非-TCs）的创伤护理模式。
我们从 2016 年华盛顿州的州住院医院出院数据中提取了医院级别特征，包括所有 TCs 和非-TCs。我们提供了汇总统计数据，并测试了每个特征在 TC/非-TC 级别之间的差异。然后，我们使用 Partition Around Medoids 方法进行了 3 组无监督聚类分析，以确定哪些医院具有相似的特征。第 1 组和第 2 组包括医院外科护理（数量或分布）特征和其他特征（例如，患者的平均年龄、支付者组合等）。第 3 组则在没有额外特征的情况下探索外科护理。
聚类结果仅部分与 TC 分类相符。第 1 组发现外科护理的数量和变化区分了医院，而在第 2 组中，矫形手术和其他特征（如年龄、社会脆弱性指数和支付者类型）驱动了聚类。第 3 组结果表明，手术量而不是手术比例更能与 TC 分类一致，尽管不是完全一致。
无监督机器学习确定了外科护理提供模式，这些模式解释了超出级别指定的变化。这项研究为系统领导者提供了一些见解，即通过更好地了解系统中的护理分布，他们可以优化 TC/非-TCs 的级别分配。
