scholarly journals Correction: Impact of the COVID-19 Pandemic on Health Care Utilization in a Large Integrated Health Care System: Retrospective Cohort Study (Preprint)

2021 ◽  
Author(s):  
Stanley Xu ◽  
Sungching Glenn ◽  
Lina Sy ◽  
Lei Qian ◽  
Vennis Hong ◽  
...  
Keyword(s):  
Health Care ◽  
Cohort Study ◽  
Health Care Utilization ◽  
Health Care System ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Integrated Health Care ◽  
Care Utilization ◽  
Integrated Health ◽  
Integrated Health Care System

UNSTRUCTURED REMOVE

scholarly journals Smoking Trends Among Adults With Behavioral Health Conditions in Integrated Health Care: A Retrospective Cohort Study

2016 ◽  
Vol 67 (9) ◽  
pp. 996-1003 ◽  
Author(s):  
Kelly C. Young-Wolff ◽  
Andrea H. Kline-Simon ◽  
Smita Das ◽  
Don J. Mordecai ◽  
Chris Miller-Rosales ◽  
...  
Keyword(s):  
Health Care ◽  
Cohort Study ◽  
Behavioral Health ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Integrated Health Care ◽  
Health Conditions ◽  
Integrated Health ◽  
Smoking Trends

scholarly journals Development and Prospective Validation of a Machine Learning-Based Risk of Readmission Model in a Large Military Hospital

2019 ◽  
Vol 10 (02) ◽  
pp. 316-325 ◽  
Author(s):  
Carly Eckert ◽  
Neris Nieves-Robbins ◽  
Elena Spieker ◽  
Tom Louwers ◽  
David Hazel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Predictive Model ◽  
Health Care System ◽  
Retrospective Cohort ◽  
Integrated Health Care ◽  
Military Hospital ◽  
Retrospective Data ◽  
Integrated Health ◽  
Integrated Health Care System

Background Thirty-day hospital readmissions are a quality metric for health care systems. Predictive models aim to identify patients likely to readmit to more effectively target preventive strategies. Many risk of readmission models have been developed on retrospective data, but prospective validation of readmission models is rare. To the best of our knowledge, none of these developed models have been evaluated or prospectively validated in a military hospital. Objectives The objectives of this study are to demonstrate the development and prospective validation of machine learning (ML) risk of readmission models to be utilized by clinical staff at a military medical facility and demonstrate the collaboration between the U.S. Department of Defense's integrated health care system and a private company. Methods We evaluated multiple ML algorithms to develop a predictive model for 30-day readmissions using data from a retrospective cohort of all-cause inpatient readmissions at Madigan Army Medical Center (MAMC). This predictive model was then validated on prospective MAMC patient data. Precision, recall, accuracy, and the area under the receiver operating characteristic curve (AUC) were used to evaluate model performance. The model was revised, retrained, and rescored on additional retrospective MAMC data after the prospective model's initial performance was evaluated. Results Within the initial retrospective cohort, which included 32,659 patient encounters, the model achieved an AUC of 0.68. During prospective scoring, 1,574 patients were scored, of whom 152 were readmitted within 30 days of discharge, with an all-cause readmission rate of 9.7%. The AUC of the prospective predictive model was 0.64. The model achieved an AUC of 0.76 after revision and addition of further retrospective data. Conclusion This work reflects significant collaborative efforts required to operationalize ML models in a complex clinical environment such as that seen in an integrated health care system and the importance of prospective model validation.

Health care utilization by people with HIV on release from provincial prison in Ontario, Canada in 2010: a retrospective cohort study

AIDS Care ◽  
2018 ◽  
Vol 31 (7) ◽  
pp. 785-792 ◽  
Author(s):  
Sumeet Khanna ◽  
Jessica Leah ◽  
Kinwah Fung ◽  
Tony Antoniou ◽  
Fiona Kouyoumdjian
Keyword(s):  
Health Care ◽  
Cohort Study ◽  
Health Care Utilization ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Care Utilization
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