Multiyear Rehospitalization Rates and Hospital Outcomes in an Integrated Health Care System

Escobar, Gabriel J.; Plimier, Colleen; Greene, J; Liu, Vincent; Kipnis, Patricia

doi:10.1001/jamanetworkopen.2019.16769

Cited by 26 publications

(20 citation statements)

References 34 publications

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“…In particular, these data include patient demographics, diagnosis codes, laboratory‐based severity of illness scores at admission and at discharge, and a comorbidity burden score that is updated monthly (Table 1). A subset of these data, which span from June 2010 to December 2017, have previously been described in greater detail 31 …”

Section: Methodsmentioning

confidence: 99%

Predicting preventable hospital readmissions with causal machine learning

Marafino

Schuler

Liu

et al. 2020

Health Services Research

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Objective: To assess both the feasibility and potential impact of predicting preventable hospital readmissions using causal machine learning applied to data from the implementation of a readmissions prevention intervention (the Transitions Program). Data Sources: Electronic health records maintained by Kaiser Permanente Northern California (KPNC). Study Design: Retrospective causal forest analysis of postdischarge outcomes among KPNC inpatients. Using data from both before and after implementation, we apply causal forests to estimate individual-level treatment effects of the Transitions Program intervention on 30-day readmission. These estimates are used to characterize treatment effect heterogeneity and to assess the notional impacts of alternative targeting strategies in terms of the number of readmissions prevented.

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Section: Methodsmentioning

confidence: 99%

Predicting preventable hospital readmissions with causal machine learning

Marafino

Schuler

Liu

et al. 2020

Health Services Research

Self Cite

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“…While statistical techniques can attempt to correct for confounding factors, they do not necessarily account for limitations in study design. These methods add a degree of robustness, however, hospitalised patient outcomes often improve over time independent of deterioration alerts among studies with large samples and long time frames 45 46…”

Section: Methodsmentioning

confidence: 99%

A scoping review of real-time automated clinical deterioration alerts and evidence of impacts on hospitalised patient outcomes

et al. 2022

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BackgroundHospital patients experiencing clinical deterioration are at greater risk of adverse events. Monitoring patients through early warning systems is widespread, despite limited published evidence that they improve patient outcomes. Current limitations including infrequent or incorrect risk calculations may be mitigated by integration into electronic medical records. Our objective was to examine the impact on patient outcomes of systems for detecting and responding to real-time, automated alerts for clinical deterioration.MethodsThis review was conducted according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews checklist. We searched Medline, CINAHL and Embase for articles implementing real-time, automated deterioration alerts in hospitalised adults evaluating one or more patient outcomes including intensive care unit admission, length of stay, in-hospital cardiopulmonary arrest and in-hospital death.ResultsOf 639 studies identified, 18 were included in this review. Most studies did not report statistically significant associations between alert implementation and better patient outcomes. Four studies reported statistically significant improvements in two or more patient outcomes, and were the only studies to directly involve the patient’s clinician. However, only one of these four studies was robust to existing trends in patient outcomes. Of the six studies using robust study designs, one reported a statistically significant improvement in patient outcomes; the rest did not detect differences.ConclusionsMost studies in this review did not detect improvements in patient outcomes following the implementation of real-time deterioration alerts. Future implementation studies should consider: directly involving the patient’s physician or a dedicated surveillance nurse in structured response protocols for deteriorating patients; the workflow of alert recipients; and incorporating model features into the decision process to improve clinical utility.

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“…Our setting consisted of the 21 KPNC hospitals. 21 29 30 31 32 33 Index hospital discharges for patients met several criteria: the patient was discharged alive from hospital between 1 June 2010 and 31 December 2018, the patient was aged ≥18 years at hospital admission, and the admission was not for childbirth (although post-delivery complications were included) or for same day surgery. We linked hospital stays for transferred patients and identified all deaths using public and internal sources.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of an intervention targeted with predictive analytics to prevent readmissions in an integrated health system: observational study

Marafino

Escobar

Baiocchi

et al. 2021

BMJ

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Objectives To determine the associations between a care coordination intervention (the Transitions Program) targeted to patients after hospital discharge and 30 day readmission and mortality in a large, integrated healthcare system. Design Observational study. Setting 21 hospitals operated by Kaiser Permanente Northern California. Participants 1 539 285 eligible index hospital admissions corresponding to 739 040 unique patients from June 2010 to December 2018. 411 507 patients were discharged post-implementation of the Transitions Program; 80 424 (19.5%) of these patients were at medium or high predicted risk and were assigned to receive the intervention after discharge. Intervention Patients admitted to hospital were automatically assigned to be followed by the Transitions Program in the 30 days post-discharge if their predicted risk of 30 day readmission or mortality was greater than 25% on the basis of electronic health record data. Main outcome measures Non-elective hospital readmissions and all cause mortality in the 30 days after hospital discharge. Results Difference-in-differences estimates indicated that the intervention was associated with significantly reduced odds of 30 day non-elective readmission (adjusted odds ratio 0.91, 95% confidence interval 0.89 to 0.93; absolute risk reduction 95% confidence interval −2.5%, −3.1% to −2.0%) but not with the odds of 30 day post-discharge mortality (1.00, 0.95 to 1.04). Based on the regression discontinuity estimate, the association with readmission was of similar magnitude (absolute risk reduction −2.7%, −3.2% to −2.2%) among patients at medium risk near the risk threshold used for enrollment. However, the regression discontinuity estimate of the association with post-discharge mortality (−0.7% −1.4% to −0.0%) was significant and suggested benefit in this subgroup of patients. Conclusions In an integrated health system, the implementation of a comprehensive readmissions prevention intervention was associated with a reduction in 30 day readmission rates. Moreover, there was no association with 30 day post-discharge mortality, except among medium risk patients, where some evidence for benefit was found. Altogether, the study provides evidence to suggest the effectiveness of readmission prevention interventions in community settings, but further research might be required to confirm the findings beyond this setting.

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Multiyear Rehospitalization Rates and Hospital Outcomes in an Integrated Health Care System

Abstract: This cohort study examines rehospitalization factors, hospitalization and mortality, discharge disposition, and the changes in these variables over time among all adult patients in the Kaiser Permanente Northern California health system.

Cited by 26 publications

References 34 publications

Predicting preventable hospital readmissions with causal machine learning

Predicting preventable hospital readmissions with causal machine learning

A scoping review of real-time automated clinical deterioration alerts and evidence of impacts on hospitalised patient outcomes

Evaluation of an intervention targeted with predictive analytics to prevent readmissions in an integrated health system: observational study

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