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

Abstract

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.

Change in healthcare utilisation after surgical treatment: observational study of routinely collected patient data from primary and secondary care

“…Prior studies have highlighted the potential benefits of early primary care review after surgery. 34 , These interventions may be best targeted at high-risk patients. 31 In the context of mounting pressures on primary and secondary care, the effectiveness of this approach to improve outcomes is unclear.…”

“…These strategies are likely to include preoperative optimisation, perioperative treatments to reduce complications and postoperative interventions to enhance recovery. 30 , 34 , 35 , 36 , 37 Identification of high-risk patients before their operation, and a detailed understanding of how interventions may affect subsequent healthcare need, will facilitate development of these care pathways.…”

Effectiveness of an Artificial Intelligence–Enabled Intervention for Detecting Clinical Deterioration

“…The discontinuity in regression lines fit on either side of the threshold estimates the causal effect of the intervention for patients exactly at the threshold . This method has been used to study the associations between contrast and kidney function, a readmissions prevention intervention and readmission rates, and red blood cell transfusion and organ dysfunction …”

“…The discontinuity in regression lines fit on either side of the threshold estimates the causal effect of the intervention for patients exactly at the threshold. 7 This method has been used to study the associations between contrast and kidney function, 8 a readmissions prevention intervention and readmission rates, 9 and red blood cell transfusion and organ dysfunction. 10 In this study we describe the application of an RDD to evaluate a pilot implementation of an artificial intelligence (AI) deterioration model-enabled intervention to reduce the risk of escalations in care for hospitalized patients.…”