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The American Journal of Managed Care September 2011
Disparities in Antidepressant Adherence in Primary Care: Report From Israel
Liat Ayalon, PhD; Revital Gross, PhD; Aviv Yaari, MD; Elan Feldhamer, BA; Ran D. Balicer, MD, PhD, MPH; and Margalit Goldfracht, MD
Mental Illness and Warfarin Use in Atrial Fibrillation
Graham A. Walker, MD; Paul A. Heidenreich, MD, MS; Ciaran S. Phibbs, PhD; Alan S. Go, MD; Victor Y. Chiu, BA; Susan K. Schmitt, PhD; Lakshmi Ananth, MS; and Susan M. Frayne, MD, MPH
Predictive Model for Emergency Hospital Admission and 6-Month Readmission
Silvia Lopez-Aguila, MPH; Joan Carles Contel, MPH; Josep Farre, CIS; Jose Luis Campuzano, MSTAT; and Luis Rajmil, PhD
Compliance, Persistence, and Switching Patterns for ACE Inhibitors and ARBs
Stefan Vegter, PharmD; Nhu Ho Nguyen, MSc; Sipke T. Visser, MSc; Lolkje TW de Jong-van den Berg, PhD; Maarten J. Postma, PhD; and Cornelis Boersma, PhD
Quality Measurement of Medication Monitoring in the Meaningful Use Era
Jennifer Tjia, MD, MSCE; Terry S. Field, DSc; Shira H. Fischer, AB; Shawn J. Gagne, BA; Daniel J. Peterson, MA, MS; Lawrence D. Garber, MD; and Jerry H. Gurwitz, MD
Electronic Health Records, Clinical Decision Support, and Blood Pressure Control
Lipika Samal, MD, MPH; Jeffrey A. Linder, MD, MPH; Stuart R. Lipsitz, ScD; and LeRoi S. Hicks, MD, MPH
Compliance, Persistence, and Switching Patterns for ACE Inhibitors and ARBs
Stefan Vegter, PharmD; Nhu Ho Nguyen, MSc; Sipke T. Visser, MSc; Lolkje TW de Jong-van den Berg, PhD; Maarten J. Postma, PhD; and Cornelis Boersma, PhD
Mental Illness and Warfarin Use in Atrial Fibrillation
Graham A. Walker, MD; Paul A. Heidenreich, MD, MS; Ciaran S. Phibbs, PhD; Alan S. Go, MD; Victor Y. Chiu, BA; Susan K. Schmitt, PhD; Lakshmi Ananth, MS; and Susan M. Frayne, MD, MPH
Inappropriate Antibiotic Prescribing in Managed Care Subjects With Influenza
Derek A. Misurski, RPh, PhD; David A. Lipson, MD; and Arun K. Changolkar, PhD
Quality Measurement of Medication Monitoring in the Meaningful Use Era
Jennifer Tjia, MD, MSCE; Terry S. Field, DSc; Shira H. Fischer, AB; Shawn J. Gagne, BA; Daniel J. Peterson, MA, MS; Lawrence D. Garber, MD; and Jerry H. Gurwitz, MD
Cost-Offset Analysis: Bimatoprost Versus Other Prostaglandin Analogues in Open-Angle Glaucoma
Karina L. Berenson, MPH; Steven Kymes, PhD; David A. Hollander, MD; Richard Fiscella, PharmD, MPH; Caroline Burk, PharmD, MS; and Vaishali D. Patel, PharmD, MS
Potential Role of Comanagement in Rescue of Surgical Patients
Keiki Hinami, MD, MS; Joe Feinglass, PhD; Darlene E. Ferranti, BS; and Mark V. Williams, MD
Depression Care Following Psychiatric Hospitalization in the Veterans Health Administration
Paul N. Pfeiffer, MD; Dara Ganoczy, MPH; Nicholas W. Bowersox, PhD; John F. McCarthy, PhD; Frederic C. Blow, PhD; and Marcia Valenstein, MD
Currently Reading
Electronic Health Records, Clinical Decision Support, and Blood Pressure Control
Lipika Samal, MD, MPH; Jeffrey A. Linder, MD, MPH; Stuart R. Lipsitz, ScD; and LeRoi S. Hicks, MD, MPH
Predictive Model for Emergency Hospital Admission and 6-Month Readmission
Silvia Lopez-Aguila, MPH; Joan Carles Contel, MPH; Josep Farre, CIS; Jose Luis Campuzano, MSTAT; and Luis Rajmil, PhD
Cost-Offset Analysis: Bimatoprost Versus Other Prostaglandin Analogues in Open-Angle Glaucoma
Karina L. Berenson, MPH; Steven Kymes, PhD; David A. Hollander, MD; Richard Fiscella, PharmD, MPH; Caroline Burk, PharmD, MS; and Vaishali D. Patel, PharmD, MS
Disparities in Antidepressant Adherence in Primary Care: Report From Israel
Liat Ayalon, PhD; Revital Gross, PhD; Aviv Yaari, MD; Elan Feldhamer, BA; Ran D. Balicer, MD, PhD, MPH; and Margalit Goldfracht, MD
Potential Role of Comanagement in Rescue of Surgical Patients
Keiki Hinami, MD, MS; Joe Feinglass, PhD; Darlene E. Ferranti, BS; and Mark V. Williams, MD

Electronic Health Records, Clinical Decision Support, and Blood Pressure Control

Lipika Samal, MD, MPH; Jeffrey A. Linder, MD, MPH; Stuart R. Lipsitz, ScD; and LeRoi S. Hicks, MD, MPH
Physician use of EHRs with CDS is associated with increased blood pressure control and lower mean systolic blood pressure across US primary care visits.

Objectives: Adding clinical decision support (CDS) to electronic health records (EHRs) is required under meaningful use legislation, but there has been little national data on effectiveness in improving clinical outcomes. We sought to determine whether EHRs with CDS improved blood pressure control in US primary care visits.


Study Design: We used a cross-sectional, nationally representative survey.


Methods: We examined adult visits to primary care physicians using the 2007 and 2008 National Ambulatory Medical Care Survey (NAMCS).


Results: We found that patients had a mean age of 52 years, 34% were male, 15% had diabetes, and 70% were white. Rates of blood pressure control were significantly higher in visits where both an EHR and CDS (79%) were used, compared with visits where physicians used neither tool (74%; P = .004). Blood pressure control rates
remained higher after adjusting for potential confounders. In unadjusted analyses, mean systolic blood pressure was 2 mm Hg lower in visits with the use of both an EHR and CDS, compared with visits where physicians used neither tool (P = .03), and this difference remained significant after adjustment.


Conclusions: The NAMCS shows that physician use of an EHR with CDS is associated with improved blood pressure control. These findings are important because small improvements in blood pressure control are associated with reductions in cardiovascular morbidity and mortality.


(Am J Manag Care. 2011;17(9):626-632)

We used nationally representative data from the National Center for Health Statistics to compare blood pressure control in practices where physicians reported using electronic health records (EHRs), EHRs with clinical decision support (CDS), both, or neither in 2007 and 2008.

 

  • Rates of blood pressure control were significantly higher in visits where both an EHR and CDS were used compared with visits where physicians used neither tool.

 

  • Mean systolic blood pressure was 2 mm Hg lower in visits where both an EHR and CDS were used compared with visits where physicians used neither tool.
Hypertension contributes to over 50,000 deaths each year in the United States, with combined direct and indirect costs of $73.4 billion.1 Despite widespread adoption of behavioral and case management approaches for hypertension and other chronic diseases that benefit from blood pressure control, such as diabetes, ischemic heart disease, cerebrovascular disease, and chronic kidney disease, national studies currently estimate that less than half of Americans with hypertension are controlled to less than 140/90 mm Hg.2-6 The clinical relevance of blood pressure control in preventing devastating cardiovascular and cerebrovascular events is indisputable, and lack of control has been linked with disproportionate morbidity and mortality in underserved populations.7,8 Hence, effective strategies to control hypertension are needed.

One possible intervention to improve blood pressure control is the expanded use of health information technology. Electronic health records (EHRs) with clinical decision support (CDS) have been touted as a solution to many deficiencies in the US healthcare system.9 Current policy stipulates CDS as a criterion for “meaningful use” of EHRs.10 However, to date there has been little rigorous evaluation of the impact of CDS on hypertension and none evaluating this outcome for CDS as implemented across the country.11,12 Clinical decision support could theoretically impact hypertension management, because many effective, medical therapies exist but are currently underutilized by physicians and patients.13 One example of CDS for hypertension is an electronic guideline– based reminder triggered when a patient’s blood pressure is entered, although actual CDS varies widely among EHRs.14,15

National studies of EHRs and CDS have shown poor correlation between use and healthcare quality.16,17 However, prior national studies have assessed only process measures, such as the process of checking blood pressure.17 To address this limitation, we assessed the relationship of EHRs and CDS with a clinical outcome, blood pressure control, in order to determine whether EHRs and CDS improve the quality of hypertensive management in a nationally representative sample of patients.

METHODS

Overview

We categorized physicians by use of: 1) EHRs; 2) CDS; 3) both; or 4) neither. We determined whether EHR and CDS use was associated with blood pressure control and mean blood pressure after adjusting for potential confounders. Our main outcomes of interest were differences in mean blood pressure and rate of blood pressure control (defined as both systolic blood pressure [SBP] <140 mm Hg and diastolic blood pressure [DBP] <90 mm Hg).

Data Source

We analyzed the National Ambulatory Medical Care Survey (NAMCS), a nationally representative survey of US ambulatory visits administered by the Centers for Disease Control and Prevention, National Center for Health Statistics (NCHS).18 The NAMCS is a nationally representative, multistage probability survey of all ambulatory visits in the United States that weights each sampled visit to account for selection probability and nonresponse.18 The NAMCS protocol has been approved by the NCHS Research Ethics Review Board, including a waiver of the requirement for informed consent of participating patients.

In 2007, 32,778 patient record forms were collected, of which 10,573 were for visits made by adults to primary care specialty physicians. In 2008, there were 28,741 visits with 10,351 primary care visits made by adults. The physician response rate was 72.7% in 2007 and 64% in 2008. In 2007, 7.8% of induction forms were missing a response to the question about CDS. In 2008, the figure was 7.2%. A large amount of race/ethnicity data were missing and the NCHS uses validated imputation methods to provide race and ethnicity data for all sampled visits.19

During recruitment in both years, trained interviewers asked physicians about EHR use using the following question: “Does your practice use electronic medical records (not including billing records)?” We considered an answer of “Yes, all electronic” or “Yes, part paper and part electronic” as a positive response for EHR use. To represent this question, we use the term “electronic health record” because it is more widely used throughout the medical literature and by national incentive programs, and connotes the maintenance of health, not just the treatment of illness.20 Regardless of the answer to the EHR question, interviewers also asked, “Does your practice have a computerized system for reminders for guideline-based interventions and/or screening tests?” A physician who does not have an EHR may still have a computerized system providing CDS.

Information about individual physician demographic characteristics (eg, age and sex) and professional characteristics (eg, years in practice) is not available; however, physician specialty and employment status information is available. “Primary care specialty,” as defined by the NCHS, includes Family Practice, General Practice, Internal Medicine, Pediatrics, Obstetrics, Gynecology, Adolescent Medicine, Sports Medicine, Geriatric Medicine, and Maternal/Fetal Medicine.19 Practice type, ownership, and designation as a solo practice are also available.

Patient visit data include patient vital signs with separate SBP and DBP, patient date of birth, sex, race, ethnicity, insurance type, diabetes, and hypertension indicated through a diagnosis code, reason for visit, or chronic condition.

Data Analysis

We performed a retrospective, cross-sectional analysis of primary care visits. We used data collected during 2007 and 2008. Patients less than 20 years of age were excluded. We limited our analysis to primary care visits because blood pressure was missing in 44% of medical specialty visits and 82% of surgical specialty visits, as opposed to 9% of primary care specialty visits. We excluded visits with missing blood pressure from the analysis.

There were a small number of visits (11%) with missing data for either the exposure or outcome. To determine how sensitive our complete case analysis was to the missing data, we estimated the regression parameters using weighted estimating equations. This is an approach that uses all subjects’ data and has been shown to be less biased than the typical complete case analysis.21 The weighted estimating equation reweights visits depending on the probability ofmissing exposure and outcome data. Our complete case results were not substantially different with the weighted estimating equations approach (<5%); therefore, for simplicity we present only the complete case analysis.

We treated age as a continuous variable. Due to missing race/ethnicity data, we used the imputed race/ethnicity dataprovided by NCHS.19 We examined the association between EHR and/or CDS use with patient demographics that are known to be associated with the blood pressure outcome, as well as diabetes due to evidence that diabetes comorbidity is associated with poor blood pressure control.22 We collapsed insurance type into 5 categories (private, Medicare, Medicaid, self pay/no charge, workers’ comp/other). We examined the association between physician EHR and/or CDS use with potentially confounding physician and practice characteristics (ie, physician is owner, employee, or contractor). We collapsed practice ownership into 5 categories (owned by physicians, health maintenance organization [HMO], community health center [CHC], hospital, other).

Our main outcomes of interest were difference in mean SBP, difference in mean DBP, and difference in rate of blood pressure control (defined as <140/90 mm Hg). In secondary analyses, we performed a subgroup analysis for visits with a diagnosis of hypertension (indicated by diagnosis code, reason for visit, or chronic condition).

Statistical Analysis

Blood pressure was evaluated as both a continuous variable (mm Hg) and a binary variable (<140/90 mm Hg or >140/90 mm Hg).

We followed recommendations by the NCHS regarding the complex, clustered sampling design, and reliability of estimates. For all analyses, we used SAS-callable SUDAAN software (SAS v9.2 SUDAAN 10.0 RT I, Research Triangle Park, North Carolina) procedures to account for weighting, stratification, and clustering. We considered P <.05 statistically significant.

Bivariate analyses to explore differences in patient and practice characteristics between exposure groups were performedusing χ2 tests (for categorical variables) and Wald F tests (for continuous variables). From these bivariate analyses, we report a weighted, unadjusted proportion of visits and mean age in each exposure group.

We performed unadjusted analyses (unadjusted for covariates, but accounting for the complex survey design) between blood pressure control and patient and practice characteristics, as well as significant associations and P values for the t test for each category of patient characteristics compared with the reference group.

We report mean SBP and DBP in each exposure group and the P value from the Wald F test. Similar unadjusted analyses were performed between the exposure of interest and blood pressure as a dichotomous outcome. We report P values for the t test for each exposure category compared with the reference group.

For multivariable linear regression analyses for the continuous blood pressure outcome, we included covariates that we found to be significantly associated with the exposure (patient age, sex, race, and insurance type, as well as practice ownership) and we included diabetes due to a priori knowledge. We examined the relationship with blood pressure control utilizing multivariable logistic regression and adjusting for the same factors.

Last, we repeated all analyses in the subgroup of visits where hypertension was indicated as a diagnosis.

RESULTS

Visit Characteristics

In 2007 and 2008, we sampled 20,924 visits by adults to primary care physicians, representing 826 million visits nationally. The mean age of patients was 52 years, 34% of visits were made by men, 15% had diabetes, 70% were white, 11% were black, and 12% were Hispanic (Table 1). Private insurance paid for 59% of visits, 24% were paid by Medicare, and 9% by Medicaid. Physician-owned practices represented 79% of visits, while 6% of visits were to practices owned by hospitals, 4% were to practices owned by CHCs, and 3% were to practices owned by HMOs. Physicians using only an EHR constituted 15% of visits, while 10% of visits were to physicians using only CDS, 27% were to physicians using both an EHR and CDS, and 48% were to physicians using neither.

Blood Pressure Control

 
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