Integrating Well-Being Information and the Multidimensional Adaptive Prediction Process to Estimate Individual-Level Future Health Care Expenditure Levels.

Decades of research exist focusing on the utility of self-reported health risk and status data in health care cost predictive models. However, in many of these studies a limited number of self-reported measures were considered. Compounding this issue, prior research evaluated models specified with a single covariate vector and distribution. In this study, the authors incorporate well-being data into the Multidimensional Adaptive Prediction Process (MAPP) and then use a simulation analysis to highlight the value of these findings for future cost mitigation. Data were collected on employees and dependents of a nationally based employer over 36 months beginning in January 2010. The first 2 years of data (2010, 2011) were utilized in model development and selection; 51239 and 54085 members were included in 2010 and 2011, respectively. The final results were based on prospective prediction of 2012 cost levels using 2011 data. The well-being-augmented MAPP results showed a 5.7% and 13% improvement in accurate cost capture relative to a reference modeling approach and the first study of MAPP, respectively. The simulation analysis results demonstrated that reduced well-being risk across a population can help mitigate the expected upward cost trend. This research advances health care cost predictive modeling by incorporating well-being information within MAPP and then leveraging the results in a simulation analysis of well-being improvement.

Impact of a scalable care transitions program for readmission avoidance.

OBJECTIVES: To evaluate the Care Transition Solution (CTS) as a means to improve quality through reduction of preventable hospital readmissions among patients with readmission-sensitive conditions subject to penalties imposed by the Affordable Care Act. STUDY DESIGN: A retrospective quasi-experimental evaluation of the impact of the CTS among admitted patients diagnosed with heart failure, acute myocardial infarction, chronic obstructive pulmonary disease, and/or pneumonia (CMS readmission-penalty diagnoses) in 14 acute care hospitals in Texas. The program, designed for scalable delivery, incorporated identification of high readmission-risk patients, assessment of individual needs, medication reconciliation, discharge planning, care coordination, and telephonic postdischarge follow-up. METHODS: The treatment group of program enrollees (N = 560) and the comparison group with no program contact (N = 3340) were matched on 8 coarsened demographic, diagnosis, and severity variables associated with readmission risk. Assessed outcomes included relative risk and odds of readmission within 30 days postdischarge and overall within the 6-month evaluation period. Zero-inflated Poisson multivariate models were used to estimate intervention effects controlling for matching-generated weights, age, disease status, and period of evaluation. RESULTS: Treatment group risk of readmission was 22% lower overall (incidence rate ratio [IRR], 0.78; P < .01) and 30-day readmission risk was 25% lower (IRR, 0.75; P = .01) relative to the comparison group. Odds of any or 30-day readmission were 0.47 (95% CI, 0.35-0.65) and 0.56 (95% CI, 0.41-0.77), respectively, for treatment relative to comparison. CONCLUSIONS: Participation in the CTS resulted in significantly lower rates of readmissions among patients with readmission-sensitive conditions, offering a scalable and sustainable approach to reduce the number of preventable hospital readmissions.

Exploring robust methods for evaluating treatment and comparison groups in chronic care management programs.

Evaluation of chronic care management (CCM) programs is necessary to determine the behavioral, clinical, and financial value of the programs. Financial outcomes of members who are exposed to interventions (treatment group) typically are compared to those not exposed (comparison group) in a quasi-experimental study design. However, because member assignment is not randomized, outcomes reported from these designs may be biased or inefficient if study groups are not comparable or balanced prior to analysis. Two matching techniques used to achieve balanced groups are Propensity Score Matching (PSM) and Coarsened Exact Matching (CEM). Unlike PSM, CEM has been shown to yield estimates of causal (program) effects that are lowest in variance and bias for any given sample size. The objective of this case study was to provide a comprehensive comparison of these 2 matching methods within an evaluation of a CCM program administered to a large health plan during a 2-year time period. Descriptive and statistical methods were used to assess the level of balance between comparison and treatment members pre matching. Compared with PSM, CEM retained more members, achieved better balance between matched members, and resulted in a statistically insignificant Wald test statistic for group aggregation. In terms of program performance, the results showed an overall higher medical cost savings among treatment members matched using CEM compared with those matched using PSM (-$25.57 versus -$19.78, respectively). Collectively, the results suggest CEM is a viable alternative, if not the most appropriate matching method, to apply when evaluating CCM program performance.