EMR Use is Not Associated with Better Diabetes Care Patrick J OConnor, MD, MPH Stephen E Asche, MA A...
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Transcript of EMR Use is Not Associated with Better Diabetes Care Patrick J OConnor, MD, MPH Stephen E Asche, MA A...
![Page 1: EMR Use is Not Associated with Better Diabetes Care Patrick J OConnor, MD, MPH Stephen E Asche, MA A Lauren Crain, PhD Leif I Solberg, MD William A Rush,](https://reader036.fdocuments.us/reader036/viewer/2022082917/5514e8d8550346a80c8b49c3/html5/thumbnails/1.jpg)
EMR Use is Not Associated with Better Diabetes Care
Patrick J O’Connor, MD, MPHStephen E Asche, MAA Lauren Crain, PhDLeif I Solberg, MD William A Rush, PhDRobin R Whitebird, PhD, MSW
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Electronic medical record (EMR)
High expectations that EMRs will improve care quality since 1980; IOM reports 1992
$10+ billion spent in US in last 5 years 400 EMR vendors
Healthcare Information and Management Systems Society
Office EMRs now used by about 35% of physicians nationally
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EMR Core Functions – IOM, 2003
health information and data
results management
order entry
decision support
electronic communication
patient support
administrative processes
reporting, population health mgmt
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Research Question Do patients receiving care at clinics using
EMRs have better quality of diabetes care, compared to patients receiving care at clinics not using EMRs?
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Project Quest Multi-site 3 year study involving 19
medical groups, 85 clinics, 700 providers and 7865 adult DM or CHD patients
Designed to identify patient, physician, clinic and group factors related to quality of care for adults with diabetes or heart disease
Funded by Agency for Healthcare Research and Quality (AHRQ)
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Data Sources Administrative data
Diabetes determination (based on diagnosis & pharmacy codes), limited demographic information
Patient survey (2001) Socio-demographic information
Clinic medical director survey (2001) Report on use of EMR Other clinic variables
Chart audit (2000, 2001, 2002) HbA1c, LDL, SBP (last in each year)
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Project Quest Diabetes Sample
Diabetes patients in 1999 (based on ICD-9 and pharmacy codes), N=4802
HealthPartners insurance, 19+ years old in 1999 Returned patient survey, N=2838 Self-report confirmed having diabetes, N=2754 Consented to chart audit, N=2019 Linked to a clinic in which a clinic medical director
completed a survey N=1491 DM patients from N=60 clinics
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EMR item “Does your clinic use computerized medical
record systems that include provider entry of data” 60 clinic medical directors responded 14 (23.3%) replied “yes” n=441 patients in EMR clinics n=1050 patients in non-EMR clinics
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Diabetes patients at clinics with and without an EMR
EMR (n=441) Non-EMR (n=1050)
Age (mean)* 64.2 60.7
Female (%)* 51.5 43.8
Duration DM (mean)*
11.5 10.3
Charlson (mean)
1.6 1.4
* p < .05
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Diabetes patients at clinics with and without an EMR
EMR Non-EMR
A1c (mean, sd)
7.3 (1.21)(n=359)
7.3 (1.34)(n=877)
LDL (mean, sd)
101.4 (30.1)(n=246)
101.8 (30.0)(n=680)
SBP(mean, sd)
132.5 (17.6)(n=397)
130.8 (17.3)(n=934)
Year 2002 clinical values. Bivariate analysis.
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Multilevel analysis Used MLwiN
adjusted treatment, and adjusted time by treatment analysis Used up to 3 clinical values per patient Nested yearly values within person within provider
within clinic (“clean” hierarchy) Predict clinical values, and change in clinical values
over time, as a function of EMR Patient covariates: age, sex, education, duration of DM,
Charlson score, CHD status, BMI Provider covariate: physician specialty
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Multilevel analysis: HbA1c and change in HbA1c
Coeff SE p
Intercept 7.31 - -
EMR present
-0.07 .11 .56
Patient and provider covariates included
Time by treatment analysis: LR test p=.14
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Multilevel analysis: LDL and change in LDL
Coeff SE p
Intercept 106.4 - -
EMR present
0.1 1.7 .95
Patient and provider covariates included
Time by treatment analysis: LR test p=.37
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Multilevel analysis: SBP and change in SBP
Coeff SE p
Intercept 128.8 - -
EMR present
1.18 .82 .15
Patient and provider covariates included
Time by treatment analysis: LR test p=.90
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Conclusions EMR use not associated with better
glucose, BP, or lipid control in adults with diabetes
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Strengths of Study
Large number of patients with diabetes Multiple levels of data collection (patient,
provider, clinic medical director) Uniform data collection procedures and
standards at all clinics Use of hierarchical analytic models to
accommodate nested data
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Potential Limitations Generalizability to other regions or patient populations is
uncertain; 60 clinics in one state Observational study precludes causal inference
Clinic systems already in place vs. pre-post design No information on 1) EMR features / functionality, 2) extent to
which EMR is used, 3) extent to which practitioners are trained to use the EMR
Clinic EMR examined in isolation (no other clinic variables considered in same analysis)
Some patients link to multiple providers and clinics, but we have simplified the hierarchy to link to one clinic
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Compare to Other Studies Meigs ’02 at Mass General Clinics—EMR
increased A1c tests but did not improve A1c level
Montori ’02 at Mayo—EMR improved number of A1c tests but did not improve A1c or LDL level
O’Connor ’01 at HPMG—EMR use led to more A1c tests, but worse A1c levels
Crabtree ’06 at NJ clinics—EMR using clinics no better than non-EMR for DM care
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Implications
Anticipated benefits of very expensive EMRs for improving diabetes (and other chronic disease) care have yet to be realized
Office systems not yet redesigned to take advantage of EMR potential
Physician training to use EMRs not standardized or optimized
More research needed if the potential of very expensive EMRs to support better care is to be realized
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Questions or comments?
Patrick O’Connor MD MPHHealthPartners Research FoundationPatrick.J.Oconnor@HealthPartners.
Com