Risk Prediction of Emergency Department Revisit 30 Days Post Discharge: A Prospective Study

Hao, Shiying; Jin, Bo; Shin, Andrew; Zhao, Yifan; Zhu, Chunqing; Li, Zhen; Hu, Zhongkai; Fu, Changlin; Ji, Jun; Wang, Yong; Zhao, Yue; Dai, Dingwei; Culver, Devore S; Alfreds, Shaun T; Rogow, Todd; Stearns, Frank; Sylvester, Karl G.; Widen, Eric; Ling, Xuefeng B.

doi:10.1371/journal.pone.0112944

Cited by 81 publications

(76 citation statements)

References 40 publications

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“…We excluded visits in which the patient died ( n = 390) or left against medical advice ( n = 19,783) during their index visit, ED visits in which the patient was transferred out of the ED to another hospital (because we could not ascertain whether the patient was subsequently admitted to another hospital, n = 20,559), records missing a return visit variable ( n = 986,922), and index visits in the months of December and January because it was not possible to assess prior visits and return visits (New York only; Florida does not specify visit month; n = 218,820). The remaining index ED visits were followed for any ED return visits within 7 days …”

Section: Methodsmentioning

confidence: 99%

Return Visit Admissions May Not Indicate Quality of Emergency Department Care for Children

Sills

Macy

Kocher

et al. 2017

Academic Emergency Medicine

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Section: Methodsmentioning

confidence: 99%

Return Visit Admissions May Not Indicate Quality of Emergency Department Care for Children

Sills

Macy

Kocher

et al. 2017

Academic Emergency Medicine

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“…18 for random forest models on a different data set. Note, however, that 0.04-0.07 improvements in AUC still do not necessarily translate to a clinically more useful model.…”

Section: Constructing Logistic Regression Models For Patient Cohortsmentioning

confidence: 99%

Understanding Emergency Department 72-Hour Revisits Among Medicaid Patients Using Electronic Healthcare Records

2015

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Electronic Healthcare Records (EHRs) have the potential to improve healthcare quality and to decrease costs by providing quality metrics, discovering actionable insights, and supporting decision-making to improve future outcomes. Within the United States Medicaid Program, rates of recidivism among emergency department (ED) patients serve as metrics of hospital performance that help ensure efficient and effective treatment within the ED. We analyze ED Medicaid patient data from 1,149,738 EHRs provided by a hospital over a 2-year period to understand the characteristics of the ED return visits within a 72-hour time frame. Frequent flyer patients with multiple revisits account for 47% of Medicaid patient revisits over this period. ED encounters by frequent flyer patients with prior 72-hour revisits in the last 6 months are thrice more likely to result in a readmit than those of infrequent patients. Statistical L1-logistic regression and random forest analyses reveal distinct patterns of ED usage and patient diagnoses between frequent and infrequent patient encounters, suggesting distinct opportunities for interventions to improve efficacy of care and streamline ED workflow. This work forms a foundation for future development of predictive models, which could flag patients at high risk of revisiting.

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“…Many studies have discussed how best to identify,13 14 39 40 or care for,6 8 10 11 36 41 the high-risk population, but few have used data analysis to better understand different types of high-risk patients.…”

Section: Discussionmentioning

confidence: 99%

Enhancing risk stratification for use in integrated care: a cluster analysis of high-risk patients in a retrospective cohort study

2016

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ObjectiveTo show how segmentation can enhance risk stratification tools for integrated care, by providing insight into different care usage patterns within the high-risk population.DesignA retrospective cohort study. A risk score was calculated for each person using a logistic regression, which was then used to select the top 5% high-risk individuals. This population was segmented based on the usage of different care settings using a k-means cluster analysis. Data from 2008 to 2011 were used to create the risk score and segments, while 2012 data were used to understand the predictive abilities of the models.Setting and participantsData were collected from administrative data sets covering primary and secondary care for a random sample of 300 000 English patients.Main measuresThe high-risk population was segmented based on their usage of 4 different care settings: emergency acute care, elective acute care, outpatient care and GP care.ResultsWhile the risk strata predicted care usage at a high level, within the high-risk population, usage varied significantly. 4 different groups of high-risk patients could be identified. These 4 segments had distinct usage patterns across care settings, reflecting different levels and types of care needs. The 2008–2011 usage patterns of the 4 segments were consistent with the 2012 patterns.DiscussionCluster analyses revealed that the high-risk population is not homogeneous, as there exist 4 groups of patients with different needs across the care continuum. Since the patterns were predictive of future care use, they can be used to develop integrated care programmes tailored to these different groups.ConclusionsUsage-based segmentation augments risk stratification by identifying patient groups with different care needs, around which integrated care programmes can be designed.

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Risk Prediction of Emergency Department Revisit 30 Days Post Discharge: A Prospective Study

Cited by 81 publications

References 40 publications

Return Visit Admissions May Not Indicate Quality of Emergency Department Care for Children

Return Visit Admissions May Not Indicate Quality of Emergency Department Care for Children

Understanding Emergency Department 72-Hour Revisits Among Medicaid Patients Using Electronic Healthcare Records

Enhancing risk stratification for use in integrated care: a cluster analysis of high-risk patients in a retrospective cohort study

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