Traditional Risk Indices as Predictors of Future Utilization and Charges in the Context of Population Health for an Uninsured Cohort.

INTRODUCTION: The uninsured population presents unique challenges to the application of an integrated approach to population health. Our objective is to compare and test population risk indices for identifying a cohort of uninsured patients at high-risk for avoidable healthcare utilization and costs. METHODS: Patients who had a least one visit at a safety-net clinic, had a primary address in Mecklenburg County, were aged 18-74, and had the most recent healthcare visit coded as 'uninsured' were identified in the baseline period. The five risk indices used were: the HHS Hierarchical Conditions Category (HCC), the Charlson Comorbidity Index (CCI), Total Cost Index, Total Inpatient Visits Index, and Total Emergency Department Visits Index. First, agreement across the five indices was analyzed. Then, the accuracy of the five risk indices was tested in predicting future utilization and costs for the subsequent 12-month follow-up period. RESULTS: Kappa statistics and percent overlap values showed below average to poor agreement between indices when comparing scorers.The strongest predictors of being in the 90th percentile of total cost during the 12 months follow-up period were the Total Cost Index at baseline (C statistic=0.75) and the HCC (C-statistic=0.73). The CCI and Total Inpatient Visit Index's demonstrated the lowest accuracy for predicting an unnecessary ED visit (C-statistic=0.51, for both). DISCUSSION/CONCLUSION: Prior cost and ED utilization were key in predicting their corresponding 12-month metrics. In contrast, the Total Inpatient Visit Index had the worst predictive performance for future hospitalization rates. Some indices were similarly predictive as compared to insured cohorts but others showed contrasting results.

Aiming to Improve Readmissions Through InteGrated Hospital Transitions (AIRTIGHT): study protocol for a randomized controlled trial.

BACKGROUND: Hospital readmissions remain highly prevalent despite being the target of policies and financial penalties. Evidence comparing the effectiveness and costs of interventions to reduce readmissions is lacking, leaving healthcare systems with little guidance on how to improve quality and avoid costly penalties. Effective interventions likely need to bridge inpatient and outpatient settings, incorporate information technology, and use dedicated providers. Such complex innovations will require rigorous evaluation. The framework of quality improvement research provides an approach that both improves care locally and contributes to closing the current knowledge gaps for readmissions. In this trial, we will study a comprehensive intervention that incorporates these recommendations into an integrated practice unit, called transition services, with an aim of reducing 30-day readmission rates. METHODS/DESIGN: We describe a nonblinded, pragmatic, controlled trial with two parallel groups comprising an evaluation of the effect of referral to a provider-led integrated practice unit, inclusive of comprehensive multidisciplinary care, dedicated paramedicine providers, and virtual visits, on 30-day readmission rates for high-risk hospitalized patients. An automated risk-scoring system will randomly generate referrals to either transition services or usual care for 1520 hospitalized patients who score as high-risk for readmission. Transition services will then engage with patients in the hospital setting using a patient navigator and provide bridging outpatient services for the 30 days following discharge. All outcome data are retrieved electronically from administrative medical records. After reapplication of inclusion and exclusion criteria at the time of hospital discharge, analyses will follow the intention-to-treat principle such that patients will be analyzed on the basis of the referral group to which they were initially randomized. DISCUSSION: The hospital transition program under study is complex and integrates the latest recommendations for readmission reduction strategies. As healthcare systems innovate to address readmissions through such complex interventions, there is significant benefit for stakeholders to have a clear understanding of the potential reach, cost, and real-world effectiveness. The pragmatic methods described here provide a template for conducting quality improvement research that fits seamlessly into existing care delivery and improvement efforts, leading to better-informed strategic decisions and the investments necessary to transform care and value for patients. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02763202 . Registered 3 March 2016 (retrospectively registered).

Spatial implications associated with using Euclidean distance measurements and geographic centroid imputation in health care research.

OBJECTIVE: To determine the effect of using Euclidean measurements and zip-code centroid geo-imputation versus more precise spatial analytical techniques in health care research. DATA SOURCES: Commercially insured members from a southeastern managed care organization. STUDY DESIGN: Distance from admitting inpatient facility to member's home and zip-code centroid (geographic placement) was compared using Euclidean straight-line and shortest-path drive distances (measurement technique). DATA COLLECTION: Administrative claims from October 2005 to September 2006. PRINCIPAL FINDINGS: Measurement technique had a greater impact on distance values compared with geographic placement. Drive distance from the geocoded address was highly correlated (r=0.99) with the Euclidean distance from the zip-code centroid. CONCLUSIONS: Actual differences were relatively small. Researchers without capabilities to produce drive distance measurements and/or address geocoding techniques could rely on simple linear regressions to estimate correction factors with a high degree of confidence.

Health-related quality of life and treatment compliance with diabetes care.

The objective of this study was to explore the relationship between health-related quality of life (HRQOL) and treatment compliance among a sample of patients with diabetes. A sample of 198 employees with diabetes of a large southeastern health plan, who were continuously enrolled in 2004, was surveyed using the Short Form Health Survey (SF-12)--a measure of HRQOL. Of the 198 identified members, 111 (56%) completed and returned the SF-12. Treatment compliance was measured using medical claims data. Compliance scores were then calculated as the number of American Diabetes Association (2005) recommended guidelines completed in 2004. These guidelines include two hemoglobin tests, a cholesterol test, a microalbuminuria test, and an eye exam. Compliance scores ranged from zero (no treatments) to five (all treatments). Both age and the Mental Composite Score (MCS) of the SF-12 were significant predictors of compliance. Age was positively related to compliance, which means that compliance with treatment guidelines increases as a person ages. MCS was negatively related to compliance, which means that those who score lower on the MCS are more likely to be compliant with diabetes care. Results of this pilot study indicate that disease management programs may need to focus special attention on those people with diabetes who are younger and have better mental health. Moreover, factors other than past utilization of care or predicted costs may be beneficial to consider in the inclusion criteria for disease management programs.