Unsupervised clustering analysis of trauma/non-trauma centers using hospital features including surgical care.
Journal
PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081
Informations de publication
Date de publication:
2024
2024
Historique:
received:
04
10
2023
accepted:
14
06
2024
medline:
22
8
2024
pubmed:
22
8
2024
entrez:
22
8
2024
Statut:
epublish
Résumé
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.
Sections du résumé
BACKGROUND
BACKGROUND
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).
STUDY DESIGN
METHODS
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.
RESULTS
RESULTS
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.
CONCLUSION
CONCLUSIONS
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.
Identifiants
pubmed: 39172912
doi: 10.1371/journal.pone.0306299
pii: PONE-D-23-30842
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0306299Informations de copyright
Copyright: © 2024 Sun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.