Karl Hammermeister1, Michael Bronsert, William G Henderson, Letoynia Coombs, Patrick Hosokawa, Elias Brandt, Cathy Bryan, Robert Valuck, David West, Winston Liaw, Michael Ho, Wilson Pace. 1. the Colorado Health Outcomes Program, the Division of Cardiology, and the Department of Family Medicine, University of Colorado School of Medicine, Aurora; the Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora; the National Research Network, American Academy of Family Physicians, Leawood, KS; DI Consulting, Dallas, TX; the Department of Clinical Pharmacy, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado, Aurora; Fairfax Family Medicine Residency Program, Virginia Commonwealth University, Fairfax; and the Denver VA Medical Center, Denver, CO.
Abstract
OBJECTIVES: Population-level control of modifiable cardiovascular disease (CVD) risk factors is suboptimal. The objectives of this study were (1) to demonstrate the use of electronically downloaded electronic health record (EHR) data to assess guideline concordance in a large cohort of primary care patients, (2) to provide a contemporary assessment of blood pressure (BP) and low-density lipoprotein (LDL) noncontrol in primary care, and (3) to demonstrate the effect of risk adjustment of rates of noncontrol of BP and LDL for differences in patient mix on these clinic-level performance measures. METHODS: This was an observational comparative effectiveness study that included 232,172 adult patients ≥18 years old with ≥1 visit within 2 years in 33 primary care clinics with EHRs. The main measures were rates of BP and LDL noncontrol based on current guidelines and were calculated from electronically downloaded EHR data. Rates of noncontrol were risk-adjusted using multivariable models of patient-level variables. RESULTS: Overall, 16.0% of the 227,122 patients with known BP and 14.9% of the 136,771 patients with known LDL were uncontrolled. Clinic-level, risk-adjusted BP noncontrol ranged from 7.7% to 26.5%, whereas that for LDL ranged from 5.8% to 23.6%. Rates of noncontrol exceeded an achievable benchmark for 85% (n = 28) and 79% (n = 26) of the 33 clinics for BP and LDL, respectively. Risk adjustment significantly influences clinic rank order for rate of noncontrol. CONCLUSIONS: We demonstrated that the use of electronic collection of data from a large cohort of patients from fee-for-service primary care clinics is feasible for the audit of and feedback on BP and LDL noncontrol. Rates of noncontrol for most clinics are substantially higher than those achievable. Risk adjustment of noncontrol rates results in a rank-order of clinics very different from that achieved with nonadjusted data.
OBJECTIVES: Population-level control of modifiable cardiovascular disease (CVD) risk factors is suboptimal. The objectives of this study were (1) to demonstrate the use of electronically downloaded electronic health record (EHR) data to assess guideline concordance in a large cohort of primary care patients, (2) to provide a contemporary assessment of blood pressure (BP) and low-density lipoprotein (LDL) noncontrol in primary care, and (3) to demonstrate the effect of risk adjustment of rates of noncontrol of BP and LDL for differences in patient mix on these clinic-level performance measures. METHODS: This was an observational comparative effectiveness study that included 232,172 adult patients ≥18 years old with ≥1 visit within 2 years in 33 primary care clinics with EHRs. The main measures were rates of BP and LDL noncontrol based on current guidelines and were calculated from electronically downloaded EHR data. Rates of noncontrol were risk-adjusted using multivariable models of patient-level variables. RESULTS: Overall, 16.0% of the 227,122 patients with known BP and 14.9% of the 136,771 patients with known LDL were uncontrolled. Clinic-level, risk-adjusted BP noncontrol ranged from 7.7% to 26.5%, whereas that for LDL ranged from 5.8% to 23.6%. Rates of noncontrol exceeded an achievable benchmark for 85% (n = 28) and 79% (n = 26) of the 33 clinics for BP and LDL, respectively. Risk adjustment significantly influences clinic rank order for rate of noncontrol. CONCLUSIONS: We demonstrated that the use of electronic collection of data from a large cohort of patients from fee-for-service primary care clinics is feasible for the audit of and feedback on BP and LDL noncontrol. Rates of noncontrol for most clinics are substantially higher than those achievable. Risk adjustment of noncontrol rates results in a rank-order of clinics very different from that achieved with nonadjusted data.
Entities:
Keywords:
Blood Pressure; Cholesterol; Clinical Practice Guideline; Electronic Health Records; Feedback; Health Information Management
Authors: Véronique L Roger; Alan S Go; Donald M Lloyd-Jones; Emelia J Benjamin; Jarett D Berry; William B Borden; Dawn M Bravata; Shifan Dai; Earl S Ford; Caroline S Fox; Heather J Fullerton; Cathleen Gillespie; Susan M Hailpern; John A Heit; Virginia J Howard; Brett M Kissela; Steven J Kittner; Daniel T Lackland; Judith H Lichtman; Lynda D Lisabeth; Diane M Makuc; Gregory M Marcus; Ariane Marelli; David B Matchar; Claudia S Moy; Dariush Mozaffarian; Michael E Mussolino; Graham Nichol; Nina P Paynter; Elsayed Z Soliman; Paul D Sorlie; Nona Sotoodehnia; Tanya N Turan; Salim S Virani; Nathan D Wong; Daniel Woo; Melanie B Turner Journal: Circulation Date: 2011-12-15 Impact factor: 29.690
Authors: Anne M Libby; Wilson Pace; Cathy Bryan; Heather Orton Anderson; Samuel L Ellis; Richard Read Allen; Elias Brandt; Amy G Huebschmann; David West; Robert J Valuck Journal: Med Care Date: 2010-06 Impact factor: 2.983
Authors: Wilson D Pace; Maribel Cifuentes; Robert J Valuck; Elizabeth W Staton; Elias C Brandt; David R West Journal: Ann Intern Med Date: 2009-07-28 Impact factor: 25.391
Authors: C Baigent; A Keech; P M Kearney; L Blackwell; G Buck; C Pollicino; A Kirby; T Sourjina; R Peto; R Collins; R Simes Journal: Lancet Date: 2005-09-27 Impact factor: 79.321
Authors: Paul A Heidenreich; Justin G Trogdon; Olga A Khavjou; Javed Butler; Kathleen Dracup; Michael D Ezekowitz; Eric Andrew Finkelstein; Yuling Hong; S Claiborne Johnston; Amit Khera; Donald M Lloyd-Jones; Sue A Nelson; Graham Nichol; Diane Orenstein; Peter W F Wilson; Y Joseph Woo Journal: Circulation Date: 2011-01-24 Impact factor: 29.690
Authors: Ann S O'Malley; Eugene C Rich; Lisa Shang; Tyler Rose; Arkadipta Ghosh; Dmitriy Poznyak; Deborah Peikes Journal: Health Serv Res Date: 2019-01-06 Impact factor: 3.402
Authors: Miguel Marino; Heather Angier; Rachel Springer; Steele Valenzuela; Megan Hoopes; Jean O'Malley; Andrew Suchocki; John Heintzman; Jennifer DeVoe; Nathalie Huguet Journal: Diabetes Care Date: 2020-07-01 Impact factor: 19.112
Authors: Miguel Marino; Heather Angier; Katie Fankhauser; Steele Valenzuela; Megan Hoopes; John Heintzman; Jennifer DeVoe; Laura Moreno; Nathalie Huguet Journal: Med Care Date: 2020-06 Impact factor: 3.178