Effective care management by Next Generation accountable care organizations.

OBJECTIVES: The objectives of this study were to estimate the utilization and spending impact of a standardized complex care management program implemented at 5 Next Generation accountable care organizations (NGACOs) and to identify reproducible program features that influenced program effectiveness. STUDY DESIGN: In 2016 and 2017, high-risk Medicare beneficiaries aligned to 5 geographically diverse NGACOs were identified using predictive analytics for enrollment in a standardized complex care management program. We estimated the program's impact on all-cause inpatient admissions, emergency department visits, and total medical expenditures (TME) relative to a matched cohort of nonparticipants. In a subanalysis, we studied the modifying effects of intervention fidelity on program impact. METHODS: We created 1897 propensity score-matched case-control pairs based on preprogram similarities in disease profile, predictive risk score, medical cost, and utilization. Changes in outcomes 6 months post program were measured using difference-in-differences analyses. We used principal components analysis to identify program features associated with reduced inpatient admissions, classified cases according to intervention fidelity, and measured postprogram changes in TME for each subgroup. RESULTS: Program participation was associated with a 21% reduction in all-cause inpatient admissions (P = .03) and a 22% reduction in TME (P = .02) 6 months after program completion. Relative spending reductions were 2.1 times greater for high-fidelity interventions compared with overall program participation (P < .001). CONCLUSIONS: Centrally staffed complex care management programs can reduce costs and improve outcomes for high-risk Medicare beneficiaries. Integrating predictive risk stratification, evidence-based intervention design, and performance monitoring can ensure consistent outcomes.

The bird's-eye view: A data-driven approach to understanding patient journeys from claims data.

OBJECTIVE: In preference-sensitive conditions such as back pain, there can be high levels of variability in the trajectory of patient care. We sought to develop a methodology that extracts a realistic and comprehensive understanding of the patient journey using medical and pharmaceutical insurance claims data. MATERIALS AND METHODS: We processed a sample of 10 000 patient episodes (comprised of 113 215 back pain-related claims) into strings of characters, where each letter corresponds to a distinct encounter with the healthcare system. We customized the Levenshtein edit distance algorithm to evaluate the level of similarity between each pair of episodes based on both their content (types of events) and ordering (sequence of events). We then used clustering to extract the main variations of the patient journey. RESULTS: The algorithm resulted in 12 comprehensive and clinically distinct patterns (clusters) of patient journeys that represent the main ways patients are diagnosed and treated for back pain. We further characterized demographic and utilization metrics for each cluster and observed clear differentiation between the clusters in terms of both clinical content and patient characteristics. DISCUSSION: Despite being a complex and often noisy data source, administrative claims provide a unique longitudinal overview of patient care across multiple service providers and locations. This methodology leverages claims to capture a data-driven understanding of how patients traverse the healthcare system. CONCLUSIONS: When tailored to various conditions and patient settings, this methodology can provide accurate overviews of patient journeys and facilitate a shift toward high-quality practice patterns.