The Relationship Between Multimorbidity and Patien

Authors: Constance H Fung Claude M Setodji FuanYue Kung Joan Keesey Steven M Asch John Adams Elizabeth A McGlynn

Publish Date: 2008/04/22

Volume: 23, Issue: 6, Pages: 788-793

PDF Link

Abstract

The growing interest in payforperformance and other quality improvement programs has generated concerns about potential performance measurement penalties for providers who care for more complex patients such as patients with more chronic conditions Few data are available on how multimorbidity affects common performance metricsWe used 2 different measures of multimorbidity 1 “individual conditions” approach disregards similarities/concordance among chronic conditions and 2 “conditiongroups” approach considers similarities/concordance among conditions We used a composite measure of patients’ ratings of patient–physician communicationA higher number of individual conditions is associated with lower ratings of communication although the magnitude of the relationship is small adjusted average communication scores 0 conditions 1220 1–2 conditions 1206 3+ conditions 1190 scale range 5 = worst 15 = best This relationship remains statistically significant when concordant relationships among conditions are considered 0 condition groups 1219 1–2 condition groups 1203 3+ condition groups 1194In our nationally representative sample patients with more chronic conditions gave their doctors modestly lower patient–doctor communication scores than their healthier counterparts Accounting for concordance among conditions does not widen the difference in communication scores Concerns about performance measurement penalty related to patient complexity cannot be entirely addressed by adjusting for multimorbidity Future studies should focus on other aspects of clinical complexity eg severity specific combinations of conditionsWe are indebted to the Robert Wood Johnson Foundation for their support to Paul Ginsburg at the Center for Studying Health System Change for his support of this collaboration to Richard Strauss at Mathematica Policy Research for developing systems for passing the initial sample from the Community Tracking Study household survey to RAND for this study to RAND’s Survey Research Group Josephine Levy and Laural Hill and the telephone interviewers for recruiting participants to Liisa Hiatt for serving as the project manager to Allen Fremont for his role in developing the survey instrument and providing comments on an earlier version of the paper and to Paul Shekelle for providing suggestions on the manuscript The Robert Wood Johnson Foundation which funded the study did not have any role in the design analysis or interpretation of our study or in the decision to submit the manuscript for publication This manuscript was supported by a grant from the Robert Wood Johnson Foundation The views expressed in this article are those of the authors and do not necessarily represent the views of the Zynx Health the RAND Corporation or the Department of Veterans Affairs Findings were presented at the 2007 AcademyHealth Annual Research Meeting June 3–5 2007 Orlando Florida

Keywords: