Measures of Clinic Systems in Clinic Surveys

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Marion Wilkins
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1 use is not associated with better diabetes care Patrick J. O Connor, MD, MPH, A. Lauren Crain, PhD, Leif I. Solberg, MD, Stehen E. Asche, MA, William A. Rush, PhD, Robin R. Whitebird, PhD, MSW Electronic medical record () $10+ billion sent on in last 5 years 300 vendors ( Institute) Office s now used by > 35% of hysicians Tyical features of an : High exectations that s will imrove care quality since 1980; IOM reorts 1992 Research Question Do atients receiving care at clinics using s have better quality of diabetes care, comared to atients receiving care at clinics not using s? Project Quest Multisite 3 year study involving 19 medical grous, 85 clinics, 700 roviders and 7865 adult DM or CHD atients Designed to identify atient, hysician, and clinic factors related to quality of care for adults with diabetes or heart disease Funded by Agency for Healthcare Research and Quality (AHRQ) Project Quest Diabetes Samle Diabetes atients in 1998 (based on ICD9 and harmacy codes) HealthPartners insurance in yrs old in 1998 Returned atient survey Selfreort confirmed having diabetes Consented to chart audit Linked to a clinic in which a clinic medical director comleted a survey N=1,491 DM atients from N=60 clinics Data Sources Administrative data Diabetes determination (based on diagnosis & harmacy codes), limited demograhic information Patient survey (2000) Sociodemograhic information Clinic medical director survey (2000) Reort on use of Other clinic variables Chart audit (1999, 2000, 2001) HbA1c, LDL, SBP (last in each year) 1

will imrove care quality since 1980; IOM reorts 1992 Research Question Do atients receiving care at clinics using s have better quality of diabetes care, comared to atients receiving care at clinics

2 item Does your clinic use comuterized medical record systems that include rovider entry of data Asked of 60 clinic medical directors 14 / 60 (23.3%) relied yes Diabetes atients at clinics with and without an Age (mean)* Female (%)* Duration DM (mean)* Charlson (mean) * <.05 (n=441) Non (n=1050) Diabetes atients at clinics with and without an A1c LDL SBP 7.3 (1.21) (n=359) (30.1) (n=246) (17.6) (n=397) Non 7.3 (1.34) (n=877) (30.0) (n=680) (17.3) (n=934) Year 2001 clinical values. Bivariate analysis. * <.05 Multilevel analysis Uses clinical values in all 3 years Models clinical value ooled across all 3 years, and change in clinical values over time Models time within erson within rovider within clinic ( clean hierarchy) Used MLWin Patient covariates: age, sex, education, duration of DM, Charlson score, CHD disease, BMI Provider covariate: hysician secialty Multilevel analysis: HbA1c and change in HbA1c resent Patient and rovider covariates included Change over time analysis: LR test =.14 Multilevel analysis: LDL and change in LDL resent Patient and rovider covariates included Change over time analysis: LR test =.37 2

4 Diabetes atients at clinics with and without an A1c LDL SBP 7.3 (1.21) (n=359) 101.4 (30.1) (n=246) 132.5 (17.6) (n=397) Non 7.3 (1.34) (n=877) 101.8 (30.0) (n=680) 130.8 (17.

3 Multilevel analysis: SBP and change in SBP resent Patient and rovider covariates included Change over time analysis: LR test =.90 Strengths of Study Large number of atients with diabetes Multile data sources (atient, rovider, clinic medical director) Use of hierarchical analytic models to accommodate nested data Uniform data collection rocedures and standards at all clinics Potential Limitations Study only involved 60 clinics in one state, generalizability to other regions or atient oulations is uncertain Observational study recludes causal inference Clinic systems already in lace Didn t examine rocess measures as deendent variables (e.g., test rates) Clinic use examined in isolation (no other clinic variables considered in same analysis) We don t have information on 1) features / functionality of the, 2) extent to which is used, 3) extent to which ractitioners are trained to use the Some atients may link to multile doctors, who link to multile clinics, but we have simlified the hierarchy Conclusions use not associated with better glucose, BP, or liid control in adults with diabetes Comare to Other Studies Meigs 02 at Mass General Clinics increased A1c tests but did not imrove A1c level Montori 02 at Mayo imroved number of A1c tests but did not imrove A1c or LDL level O Connor 01 at HPMG use led to more A1c tests, but worse A1c levels Crabtree 06 at NJ clinics using clinics no better than non for DM care Imlications Anticiated benefits of very exensive s for imroving diabetes (and other chronic disease) care have yet to be realized Office systems not yet redesigned to take advantage of otential Physician training to use s not standardized or otimized More research needed if the otential of very exensive s to suort better care is to be realized 3

collection rocedures and standards at all clinics Potential Limitations Study only involved 60 clinics in one state, generalizability to other regions or atient oulations is uncertain Observational

4 Questions or Comments HealthPartners.com Aendix slides Diabetes identification Diabetes identified using a method with estimated sensitively of 0.91 and ositive redictive value of Data on A1c and CHD were obtained from a medial record review. See aer draft for detail Recruitment rates, samling QUEST successfully recruited: 19 of 22 eligible medical grous 85 of 86 eligible clinics within those medical grous See aer draft for details on samling: 19 MG, all clinics in these MG, minimum of 10 ts er clinic (DM and CHD samle) Survey resonse rates Survey resonse rates of medical grous (100%), clinics (98%), roviders (55%) and chart audit consent rate of atients resonding to surveys (about 80%) exceeded levels needed to ower the analysis. Patient Factors Analyzed *Age *Educational Level *Duration of Diabetes *Comorbidity *Gender *BMI 4

See aer draft for detail Recruitment rates, samling QUEST successfully recruited: 19 of 22 eligible medical grous 85 of 86 eligible clinics within those medical grous See aer draft for details on

5 Physician Factors Analyzed Years Exerience (Post Residency) Gender Secialty (FP, IM) Measures of Clinic Systems in Clinic Surveys Exanded Roles for Nurses/Teams Registries Electronic Medical Records Monitoring of Clinical Status Prioritization based on Risk, RTC Active Interventions: Visit Planning Active Outreach Patient Activation Where is the Variance? 8090% of variance at Patient/Time level 5% of variance at Physician level 5% of variance at Clinic level 24% of variance at Medical Grou level 5

based on Risk, RTC Active Interventions: Visit Planning Active Outreach Patient Activation Where is the Variance?

Outpatient EMR-Based Clinical Decision Support: Challenges and Opportunities

Outpatient EMR-Based Clinical Decision Support: Challenges and Opportunities Patrick J. O Connor MD MA MPH Senior Clinical Investigator HealthPartners Research Foundation 1 Disclosures Employed full time