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About the Algorithm for Identifying Patients with Multiple Chronic Conditions (Multimorbidity)
Patients with multiple chronic conditions comprise almost a third of the United States population, and account for 71% of healthcare spending. Suboptimal health outcomes and rising healthcare expenditures for those with multiple chronic conditions have been identified by the US Department of Health and Human Services as a major public health challenge. As a result, research focusing on the prevalence and impact of multiple chronic conditions is a priority to supporting future interventions and quality measures to improve the health of the overall population. Identification of patient multiple chronic conditions is necessary for this work.
To support research on patients with multiple chronic conditions, we created indicator variables for the presence or absence of 69 chronic conditions. These variables can be used to identify patient chronic conditions in health care and billing records. To create this comprehensive set of variables, we combined 358 clinical categories from the Agency for Health Care Quality and Research (AHRQ) Healthcare Cost and Utilization Project’s (HCUP) Clinical Classification Software (CCS) that cover 4,427 ICD-9 codes, identified as chronic conditions by HCUP’s Chronic Condition Indicator, into 69 clinically-relevant condition categories, including modifications from a previous set of conditions to incorporate recent CCS updates and further highlight metabolic and cardiovascular conditions.
Who should use this toolkit?
This toolkit is intended for researchers interested in examining the effect of multiple chronic conditions or specific comorbidities on health and outcomes, or for those who are interested in quality improvement or public reporting on patients with comorbid conditions. Results could be utilized to inform policy development, provision of care, and allocation of resources.
What does the toolkit contain?
This toolkit contains an Excel file that you can download and then import into your statistical program. It contains ICD-9 diagnostic codes mapped to AHRQ Clinical Classification Software (CCS) codes that have been identified as chronic conditions per the AHRQ chronic condition indicator. The CCS codes are then bundled into 69 clinically relevant chronic condition categories.
The chronic conditions diagnostic codes can be assessed in any patient timeframe desired. Past work has identified multiple chronic conditions during the baseline year and during both baseline and reporting years.
Funding
The Algorithm for Identifying Patients with Multiple Chronic Conditions (Multimorbidity) was developed by researchers and clinicians (Principal Investigator: Elizabeth Magnan, MD, PhD) at the University of Wisconsin-Madison School of Medicine & Public Health and the University of California, Davis.
This project was supported by grant R21 HS021899 from the Agency for Healthcare Research and Quality, the University of Wisconsin School of Medicine and Public Health’s Health Innovation Program (HIP), the Wisconsin Partnership Program, and the Community-Academic Partnerships core of the University of Wisconsin Institute for Clinical and Translational Research (UW ICTR), grant 9 U54 TR000021 from the National Center for Advancing Translational Sciences (previously grant 1 UL1 RR025011 from the National Center for Research Resources). Dr. Magnan completed a portion of this work while was a Primary Care Research Fellow supported by a National Research Service Award (T32HP10010) from the Health Resources and Services Administration to the University of Wisconsin Department of Family Medicine. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or other funders.
References
- Magnan EM, Gittelson R, Bartels CM, Johnson HM, Pandhi N, Jacobs EA, Smith MA. Establishing chronic condition concordance and discordance with diabetes: a Delphi study. BMC Fam Pract. 2015 Mar 28;16:42.
- Magnan EM, Bolt DM, Greenlee RT, Fink J, Smith MA. Stratifying Patients with Diabetes into Clinically Relevant Groups by Combination of Chronic Conditions to Identify Gaps in Quality of Care. Health Serv Res. 2018 Feb;53(1):450-468.
- Smith MA, Bednarz L, Nordby PA, Fink J, Greenlee RT, Bolt D, Magnan EM. Increasing Consumer Engagement by Tailoring a Public Reporting Website on the Quality of Diabetes Care: A Qualitative Study. J Med Internet Res. 2016 Dec 21;18(12):e332.
Toolkit Citation
Magnan E. Algorithm for Identifying Patients with Multiple Chronic Conditions (Multimorbidity). University of Wisconsin – Madison Department of Family Medicine, the University of California – Davis Department of Family and Community Medicine, and the UW Health Innovation Program; 2015. Available at: http://www.hipxchange.org/comorbidities.
About the Developer
Elizabeth Magnan, MD, PhD is a Family Physician and Assistant Professor (tenure-track) in the Department of Family and Community Medicine at the University of California – Davis. Her research focuses on improving quality of care and outcomes in patients with diabetes and multi-morbidity. Her dissertation work focuses on the impact of patient comorbidity profiles on diabetes quality measures. She also examines how patient and health system factors influence care received for patients with diabetes and multiple chronic conditions.
