Maureen A Smith1,2,3, Mary S Vaughan-Sarrazin4, Menggang Yu5, Xinyi Wang3, Peter A Nordby3, Christine Vogeli6,7, Jonathan Jaffery8,9, Joshua P Metlay6,7. 1. Department of Population Health Sciences, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA. 2. Department of Family Medicine and Community Health, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA. 3. Health Innovation Program, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA. 4. Department of Internal Medicine, College of Medicine, University of Iowa, Iowa City, Iowa, USA. 5. Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA. 6. Harvard Medical School, Harvard University, Boston, Massachusetts, USA. 7. Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA. 8. Office of Population Health, UW Health, Madison, Wisconsin, USA. 9. Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA.
Abstract
OBJECTIVE: Case management programs for high-need high-cost patients are spreading rapidly among health systems. PCORNet has substantial potential to support learning health systems in rapidly evaluating these programs, but access to complete patient data on health care utilization is limited as PCORNet is based on electronic health records not health insurance claims data. Because matching cases to comparison patients on baseline utilization is often a critical component of high-quality observational comparative effectiveness research for high-need high-cost patients, limited access to claims may negatively affect the quality of the matching process. We sought to determine whether the evaluation of programs for high-need high-cost patients required claims data to match cases to comparison patients. MATERIALS AND METHODS: A retrospective cohort study design with multiple measures of before-and-after health care utilization for 1935 case management patients and 3833 matched comparison patients aged 18 years and older from 2011 to 2015. EHR and claims data were extracted from 3 health systems participating in PCORNet. RESULTS: Without matching on claims-based health care utilization, the case management programs at 2 of 3 health systems were associated with fewer hospital admissions and emergency visits over the subsequent 12 months. With matching on claims-based health care utilization, case management was no longer associated with admissions and emergency visits at those 2 programs. DISCUSSION: The results of a PCORNet-facilitated evaluation of 3 programs for high-need high-cost patients differed substantially depending on whether claims data were available for matching cases to comparison patients. CONCLUSIONS: Partnering with learning health systems to rapidly evaluate programs for high-need high-cost patients will require that PCORNet facilitates comprehensive and timely access to both electronic health records and health insurance claims data.
OBJECTIVE: Case management programs for high-need high-cost patients are spreading rapidly among health systems. PCORNet has substantial potential to support learning health systems in rapidly evaluating these programs, but access to complete patient data on health care utilization is limited as PCORNet is based on electronic health records not health insurance claims data. Because matching cases to comparison patients on baseline utilization is often a critical component of high-quality observational comparative effectiveness research for high-need high-cost patients, limited access to claims may negatively affect the quality of the matching process. We sought to determine whether the evaluation of programs for high-need high-cost patients required claims data to match cases to comparison patients. MATERIALS AND METHODS: A retrospective cohort study design with multiple measures of before-and-after health care utilization for 1935 case management patients and 3833 matched comparison patients aged 18 years and older from 2011 to 2015. EHR and claims data were extracted from 3 health systems participating in PCORNet. RESULTS: Without matching on claims-based health care utilization, the case management programs at 2 of 3 health systems were associated with fewer hospital admissions and emergency visits over the subsequent 12 months. With matching on claims-based health care utilization, case management was no longer associated with admissions and emergency visits at those 2 programs. DISCUSSION: The results of a PCORNet-facilitated evaluation of 3 programs for high-need high-cost patients differed substantially depending on whether claims data were available for matching cases to comparison patients. CONCLUSIONS: Partnering with learning health systems to rapidly evaluate programs for high-need high-cost patients will require that PCORNet facilitates comprehensive and timely access to both electronic health records and health insurance claims data.
Authors: Jeremy A Rassen; Abhi A Shelat; Jessica Myers; Robert J Glynn; Kenneth J Rothman; Sebastian Schneeweiss Journal: Pharmacoepidemiol Drug Saf Date: 2012-05 Impact factor: 2.890
Authors: Gerard F Anderson; Jeromie Ballreich; Sara Bleich; Cynthia Boyd; Eva DuGoff; Bruce Leff; Claudia Salzburg; Jennifer Wolff Journal: Am J Manag Care Date: 2015-11-01 Impact factor: 2.229
Authors: Jeanne M Madden; Matthew D Lakoma; Donna Rusinak; Christine Y Lu; Stephen B Soumerai Journal: J Am Med Inform Assoc Date: 2016-04-14 Impact factor: 4.497
Authors: Karen Donelan; Esteban A Barreto; Carie U Michael; Peter Nordby; Maureen Smith; Joshua P Metlay Journal: J Gen Intern Med Date: 2018-12 Impact factor: 6.473
Authors: Kari R Gillmeyer; Eduardo R Nunez; Seppo T Rinne; Shirley X Qian; Elizabeth S Klings; Renda Soylemez Wiener Journal: Chest Date: 2020-12-17 Impact factor: 9.410
Authors: Maureen A Smith; Menggang Yu; Jared D Huling; Xinyi Wang; Allie DeLonay; Jonathan Jaffery Journal: J Med Internet Res Date: 2022-06-13 Impact factor: 7.076