Electronic Health Records in an Integrated Delivery System: Effects on Diabetes Care Quality

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1 Electronic Health Records in an Integrated Delivery System: Effects on Diabetes Care Quality Mary Reed, DrPH 1 Jie Huang, PhD 1 Ilana Graetz 1 Richard Brand, PhD 2 Marc Jaffe, MD 3 Bruce Fireman, MA 1 John Hsu, MD MBA MSCE 4 1 Kaiser Permanente Division of Research 2 University of California at San Francisco, Department of Epidemiology and Biostatistics 3 Kaiser Permanente Northern California 4 Mongan Institute for Health Policy, Massachusetts General Hospital & Department of Health Care Policy, Harvard Medical School Funded by National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) R01DK AcademyHealth Annual Research Meeting June 2011

2 Introduction Health Information Technology (IT) use has the potential to increase care quality for patients with chronic conditions Federal incentive payments require Meaningful Use of certified electronic health records (EHR) Limited research evidence from large, controlled, longitudinal studies on effects of integrated, commercially available EHR systems on chronic disease care Need to examine effect of EHR on specific care pathways

3 Research Questions Does using an EHR improve diabetes care quality? Treatment: Does use of an EHR increase rates of treatment intensification after an A1c test? Specifically among those with a high initial value? Monitoring: Does use of an EHR increase rates of A1c testing? Overall retesting rates Retesting within 1 year (HEDIS) or 90 days (ADA)

4 Setting Integrated Delivery System Kaiser Permanente Northern California (KPNC) 17 Medical centers >1000 Primary care providers in ~110 teams EHR Epic-based integrated complete EHR' Integrated outpatient clinical information and documentation EHR accessible across all clinicians and all settings Order entry for labs and prescription medications Alerts A1c testing Diabetes treatment recommendations

5 Study Design Longitudinal follow-up of all patients with diabetes Staggered EHR implementation across 17 medical centers Quasi-experimental with concurrent controls Population: IDS diabetes registry at the end of 2003 Excluded after first disenrollment from IDS

6 Testing and Treatment Measures All A1c tests for patients with diabetes ( ) N=1,372, % of tests were done after EHR was implemented Treatment intensification Compare diabetes-related oral prescription drugs (non-insulin users) 60 days post-test vs.180 days pre-test Increase classes, dose, switch within a class or to new class Adding Insulin Time to Retest: Re-test Intervals Time from one A1c test (index test) to the next

7 Analysis Treatment intensification Multivariate logistic regression Interaction between EHR and index test level Time to Re-test Survival analysis: Stratified by test level Multivariate logistic regression: 1 year, 90 days Covariates Socio-demographic: age, gender, race/ethnicity, neighborhood SES, adherence history, chronic conditions Secular trend: calendar month and calendar year Medical center

8 Table 1. Baseline Patient Characteristics Total N % Characteristic 169,711 Age , , , , , , Gender Male 88, Race/ethnicity White 82, Black 17, Hispanic 22, Asian 24, Other 6, Unknown 15, Neighborhood Higher SES 120, SES Low SES 45, Unknown 4, Existing chronic Asthma 23, diseases CAD 28, HF 13, HTN 108,

9 Table 2. Association between EHR and Treatment Intensification by A1c Test Level Index test value Treatment Intensification (%) Pre-EHR EHR Treatment Intensification EHR vs. Pre-EHR Adjusted OR* 95% CI A1c<7 4.73% 4.82% , 1.01 A1c % 26.94% , 1.15 A1c % 46.34% , 1.14 *Logistic regression with clustering at patient level, also adjusted for patient characteristics (age, gender, neighborhood SES, race/ethnicity, prior adherence to drug, existing chronic conditions), calendar month, calendar year and medical center with an interaction term of EHR and index test level

10 Figure 2. Histogram of A1c Test Re-test Intervals Density Re-test within 365 days Re-test within 90 days Pre-EHR (%) EHR (%) Pre-EHR (%) EHR (%) A1c< % 89.56% 15.80% 14.66% A1c % 93.88% 24.60% 25.18% A1c % 91.54% 27.46% 28.26% days Note: Histogram of the inter test interval (days) between all HbA1c tests conducted between 2004 and 2009 among patients with diabetes in the end of 2003 and is restricted to the 98.8% of A1c tests with a re-test interval of <730 days

11 Table 3. Association between EHR and Time to Re-test Overall Rate of testing* Retest within 365 days** Retest within 90 days** HR 95% CI OR 95% CI OR 95% CI A1c<7 EHR vs pre-ehr , , , 0.93 A1c EHR vs pre-ehr , , , 1.06 A1c 9+ EHR vs pre-ehr , , , 1.07 *Cox model stratified by index test level with clustering at patient level, also adjusted for patient characteristics,calendar month, calendar year and medical center with an interaction term of EHR and index test level. Censored at first disenrollment or end of study. **Logistic regression with clustering at patient level, also adjusted for patient characteristics, calendar month, calendar year and medical center with an interaction term of EHR and index test level

12 Limitations Single IDS with set delivery options and non-experimental allocation of EHR High levels of baseline HbA1c testing Baseline non-integrated Health IT tools Baseline disease management programs

13 Conclusions Implementation of an integrated EHR: Increased rates of treatment intensification among patients with high HbA1c values Increased overall rates and rates of re-testing at 1 year 1 year retesting rate increase was greatest among patients with the highest HbA1c values More appropriate rates of re-testing at 90 days 90 day retesting increased among patients with the highest HbA1c values 90 day retesting rate decreased among patients already at goal

14 Implications EHR can be a powerful tool to deliver well-targeted and high quality chronic disease care The EHR helps target specific patient subgroups throughout the diabetes care pathway Increased testing and treatment for those with the greatest needs Decreasing aggressive testing for those already under good control Next steps: A1c levels

15

16 Appendix

17 Table 4. Association between EHR and HbA1c results (log transformed) All Baseline HbA1c levels Baseline HbA1c < Coef. 95% CI Coef. 95% CI Coef. 95% CI Coef. 95% CI pre-ehr (Ref) (Ref) (Ref) (Ref) First test after EHR , , , , or more tests after EHR , , , , Baseline HbA1c: defined as the last measure in Excluded patients with no baseline: 12.5% for A1c - Log transformed A1c Model: linear regression on log a1c with fixed effect at patient level, adjusted for calendar quarter and calendar year

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