• J Palliat Med · Sep 2019

    Comparative Study

    Effects of a Population Health Community-Based Palliative Care Program on Cost and Utilization.

    • Lori Yosick, Robert E Crook, Maria Gatto, Terri L Maxwell, Ian Duncan, Tamim Ahmed, and Andrew Mackenzie.
    • Trinity Health, Livonia, Michigan.
    • J Palliat Med. 2019 Sep 1; 22 (9): 107510811075-1081.

    AbstractBackground: New population health community-based models of palliative care can result in more compassionate, affordable, and sustainable high-quality care. Objectives: We evaluated utilization and cost outcomes of a standardized, population health community-based palliative care program provided by nurses and social workers. Design: We conducted a retrospective propensity-adjusted study to quantify cost savings and resource utilization associated with a community-based palliative care program. We analyzed claims data from a Medicare Advantage (MA) plan and used a proprietary predictive model to identify 804 members at high risk for overmedicalized end-of-life care. We enrolled 204 members in the palliative care program and compared them with 600 who received standard, telephonic, health plan case management. We excluded members with fewer than two months of enrolled experience or those with insufficient data for analysis, leaving 176 members in the study group and 570 in the control group for evaluation. We compared differences in utilization and costs (medical and pharmacy), hospital admissions, bed days (acute and intensive care unit [ICU]), and emergency department visits. Setting/Subjects: A 30,000-member MA plan and a health system in Central Ohio between October 2015 and June 2016. Results: Members who received community-based palliative care showed a statistically significant 20% reduction in total medical costs ($619 per enrolled member per month), 38% reduction in ICU admissions, 33% reduction in hospital admissions, and 12% reduction in hospital days. Conclusion: A structured nurse and social work model of community-based palliative care using a predictive model to identify MA candidates for intervention can reduce utilization and medical costs.

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