Health Care Cost Analyses for Exploring Cost Savings Opportunities in Older Patients: Longitudinal Retrospective Study

• Published in: JMIR aging 2018-08, Vol.1 (2), p.e10254-e10254
• Main Authors: Agboola, Stephen, Simons, Mariana, Golas, Sara, Op den Buijs, Jorn, Felsted, Jennifer, Fischer, Nils, Schertzer, Linda, Orenstein, Allison, Jethwani, Kamal, Kvedar, Joseph
• Format: Article
• Language: English
• Subjects: Aging; Cost control; Data analysis; Data warehouses; Electronic health records; Emergency medical care; Fiscal years; Health care expenditures; Health care policy; Health insurance; Hospitals; Medicare; Older people; Original Paper; Patients; Population; Telemedicine
• ISSN: 2561-7605; 2561-7605
• DOI: 10.2196/10254

Half of Medicare reimbursement goes toward caring for the top 5% of the most expensive patients. However, little is known about these patients prior to reaching the top or how their costs change annually. To address these gaps, we analyzed patient flow and associated health care cost trends over 5 years. To evaluate the cost of health care utilization in older patients by analyzing changes in their long-term expenditures. This was a retrospective, longitudinal, multicenter study to evaluate health care costs of 2643 older patients from 2011 to 2015. All patients had at least one episode of home health care during the study period and used a personal emergency response service (PERS) at home for any length of time during the observation period. We segmented all patients into top (5%), middle (6%-50%), and bottom (51%-100%) segments by their annual expenditures and built cost pyramids based thereon. The longitudinal health care expenditure trends of the complete study population and each segment were assessed by linear regression models. Patient flows throughout the segments of the cost acuity pyramids from year to year were modeled by Markov chains. Total health care costs of the study population nearly doubled from US $17.7M in 2011 to US $33.0M in 2015 with an expected annual cost increase of US $3.6M (P=.003). This growth was primarily driven by a significantly higher cost increases in the middle segment (US $2.3M, P=.003). The expected annual cost increases in the top and bottom segments were US $1.2M (P=.008) and US $0.1M (P=.004), respectively. Patient and cost flow analyses showed that 18% of patients moved up the cost acuity pyramid yearly, and their costs increased by 672%. This was in contrast to 22% of patients that moved down with a cost decrease of 86%. The remaining 60% of patients stayed in the same segment from year to year, though their costs also increased by 18%. Although many health care organizations target intensive and costly interventions to their most expensive patients, this analysis unveiled potential cost savings opportunities by managing the patients in the lower cost segments that are at risk of moving up the cost acuity pyramid. To achieve this, data analytics integrating longitudinal data from electronic health records and home monitoring devices may help health care organizations optimize resources by enabling clinicians to proactively manage patients in their home or community environments beyond institutional settings and