The Relationship Between Multimorbidity and Patients’ Ratings of Communication
- 163 Downloads
The growing interest in pay-for-performance 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 metrics.
To examine the relationship between multimorbidity and patients’ ratings of communication, a common performance metric.
Nationally representative sample of U.S. residents
A total of 15,709 noninstitutionalized adults living in the United States participated in a telephone interview.
We used 2 different measures of multimorbidity: 1) “individual conditions” approach disregards similarities/concordance among chronic conditions and 2) “condition-groups” approach considers similarities/concordance among conditions. We used a composite measure of patients’ ratings of patient–physician communication.
A 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, 12.20; 1–2 conditions, 12.06; 3+ conditions, 11.90; scale range 5 = worst, 15 = best). This relationship remains statistically significant when concordant relationships among conditions are considered (0 condition groups 12.19; 1–2 condition groups 12.03; 3+ condition groups 11.94).
In 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 (e.g., severity, specific combinations of conditions).
KEY WORDSmultimorbidity pay-for-performance patient–physician communication
We 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).
Conflict of Interests
Constance Fung is an employee of Zynx Health, Incorporated.
- 1.Agency For Healthcare Research and Quality. CAHPS Surveys and Tools. Available at https://www.cahps.ahrq.gov/default.asp. Accessed March 12, 2008.
- 6.Pacific Business Group on Health. Healthscope. Available at http://www.healthscope.org/default.asp. Accessed March 12, 2008.
- 7.Jackson C. California HMO: Doctor bonuses based on patient satisfaction. AMNews Available at http://www.ama-assn.org/amednews/2001/07/30/bil10730.htm. Accessed March 12, 2008.
- 8.Ryan R. Primary Care Redesign. The Permanente Journal. Available at http://xnet.kp.org/permanentejournal/fall97pj/redesign.html. Accessed March 12, 2008.
- 9.Werner RM, Greenfield S, Fung CH, Turner BJ. Measuring Quality of Care in Complex Patients: Findings from a Conference Organized by the Society of General Internal Medicine. Available at http://www.sgim.org/PDF/SGIMReports/MeasuringQualityCareFinalReportOct42006.pdf. Accessed March 12, 2008.
- 11.Yancik R, Ershler W, Satariano W, Hazzard W, Cohen HJ, Ferrucci L. Report of the national institute on aging task force on comorbidity. J Gerontol Ser A Biol Sci Med Sci. 2007;62:275–80.Google Scholar
- 16.Community Tracking Study Household Survey, 2000–2001:[United States] [Computer file]. Inter-university Consortium for Political and Social Research. Ann Arbor: Center for Studying Health System Change; 2003.Google Scholar
- 20.Frequently Asked Questions. Available at https://www.cahps.ahrq.gov/default.asp. Accessed March 12, 2008.
- 21.Calculate Your Body Mass Index. Available at http://www.nhlbisupport.com/bmi/. Accessed March 12, 2008.
- 23.Medicare Health Outcomes Survey. Available at http://www.hosonline.org/surveys/hos/download/HOS_DUG_PUF_CVIIB.pdf. Accessed March 12, 2008.
- 31.Graubard B, Korn E. Predictive margins with survey data. Biometrics. 55:652–9.Google Scholar
- 33.Donabedian A. Explorations in Quality Assessment and Monitoring: The Definition of Quality and Approaches to Its Assessment. Ann Arbor, MI: Health Administration Press; 1980.Google Scholar
- 39.Street RL Jr. Communication in medical encounters: an ecological perspective. In: Thompson TL, Dorsey AM, Miller KI, Parrott R, eds. Handbook of Health Communication. Mahwah, NJ: Lawrence Erlbaum Associates; 2003.Google Scholar
- 46.Comorbidity: From Bedside to Bench. Summary of the NIA/AGS R13 Conference. Available at www.americangeriatrics.org/research/confseries/CombinedfilesAGSComorbidityPresentation.ppt. Accessed March 12, 2008.