Impact of predictive model-directed end-of-life counseling for Medicare beneficiaries.

OBJECTIVES: To validate a predictive model for identifying Medicare beneficiaries who need end-of-life care planning and to determine the impact on cost and hospice care of a telephonic counseling program utilizing this predictive model in 2 Medicare Health Support (MHS) pilots. STUDY DESIGN: Secondary analysis of data from 2 MHS pilot programs that used a randomized controlled design. METHODS: A predictive model was developed using intervention group data (N = 43,497) to identify individuals at greatest risk of death. Model output guided delivery of a telephonic intervention designed to support educated end-of-life decisions and improve end-of-life provisions. Control group participants received usual care. As a primary outcome, Medicare costs in the last 6 months of life were compared between intervention group decedents (n = 3112) and control group decedents (n = 1630). Hospice admission rates and duration of hospice care were compared as secondary measures. RESULTS: The predictive model was highly accurate, and more than 80% of intervention group decedents were contacted during the 12 months before death. Average Medicare costs were $1913 lower for intervention group decedents compared with control group decedents in the last 6 months of life (P = .05), for a total savings of $5.95 million. There were no significant changes in hospice admissions or mean duration of hospice care. CONCLUSIONS: Telephonic end-of-life counseling provided as an ancillary Medicare service, guided by a predictive model, can reach a majority of individuals needing support and can reduce costs by facilitating voluntary election of less intensive care.

Effectiveness of disease management programs on improving diabetes care for individuals in health-disparate areas.

In addition to race and ethnicity, specific geographic regions are associated with poorer outcomes of care. Individuals with diabetes experiencing health disparities typically have worse long-term outcomes, such as increased diabetes complications and mortality. Zip code mapping, or geocoding, was utilized in this study to identify regions of the United States with high diabetes prevalence rates and to identify areas with high densities of minority populations. Use of this methodology to examine the effect of disease management on a large, diverse diabetes population revealed greater improvement in clinical testing rates in health disparity zones compared with members living outside of these areas. In particular, significant improvement was achieved by members living in minority zip codes and by members aged 65 years or older. These findings demonstrate that members living in areas of health disparity obtain even greater benefit from diabetes disease management program participation, helping to reduce gaps in care.