Utilization of clinical tools to understand health-care differences of the Discovery Health population

Abstract

Various clinical tools exist that categorize health-care data into homogenous cohorts. The tools contribute to the understanding of health-care experiences. The tools described below are used in understanding the Discovery Health population. At a patient level, the John Hopkins Adjusted Clinical Groups (ACG) case-mix system identifies common combinations of disease that impact on each other to increase an individual’s risk. The tool is thus robust not only in categorizing patient illness, but also in analyzing health-care utilization and cost. At an episode of care level, the Discovery Episode grouper (DEG) uses ICD-10 coded data to define episodes. These episodes provide a means for assessing health-care quality and cost. At the hospital level the Diagnosis Related Group (DRG), a patient classification system, provides an understanding of the costs incurred by measuring the case-mix index of a hospital. Using the ACG, DEG and DRG, Discovery Health data can be segregated into various resource bands. High-cost members can be identified, and the costs incurred by different providers assessed. Studies done in various regions in the United States have shown that Medicare spending varies in different areas across the country. Stephan Zuckerman explains that “geographic differences in Medicare spending are not necessarily evidence of inefficiency in health care,” and that “29% of the differences can be attributed to health, but 33% remains unexplained”. Using region as a starting point, the aim of this project is to understand how much of the differences in health-care cost identified can be explained using the clinical tools. This explainable portion is due to the difference in population demographics and disease burden within different regions. Possible explanatory factors for the residual differences will be explored, with regional differences forming the starting point of the study.