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Research ArticleMethodology

A Technology-Based Quality Innovation to Identify Undiagnosed Hypertension Among Active Primary Care Patients

Michael K. Rakotz, Bernard G. Ewigman, Menaka Sarav, Ruth E. Ross, Ari Robicsek, Chad W. Konchak, Thomas F. Gavagan, David W. Baker, David J. Hyman, Kenneth P. Anderson and Christopher M. Masi
The Annals of Family Medicine July 2014, 12 (4) 352-358; DOI: https://doi.org/10.1370/afm.1665
Michael K. Rakotz
1Feinberg School of Medicine, Northwestern University, Chicago, Illinois
MD
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  • For correspondence: mrakotz@nmh.org
Bernard G. Ewigman
2NorthShore University HealthSystem, Evanston, Illinois
3Pritzker School of Medicine, The University of Chicago, Chicago, Illinois
MD, MSPH
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Menaka Sarav
2NorthShore University HealthSystem, Evanston, Illinois
3Pritzker School of Medicine, The University of Chicago, Chicago, Illinois
MD
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Ruth E. Ross
2NorthShore University HealthSystem, Evanston, Illinois
3Pritzker School of Medicine, The University of Chicago, Chicago, Illinois
PhD
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Ari Robicsek
2NorthShore University HealthSystem, Evanston, Illinois
3Pritzker School of Medicine, The University of Chicago, Chicago, Illinois
MD
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Chad W. Konchak
2NorthShore University HealthSystem, Evanston, Illinois
MBA
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Thomas F. Gavagan
4College of Medicine, University of Illinois, Chicago, Illinois
MD, MPH
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David W. Baker
1Feinberg School of Medicine, Northwestern University, Chicago, Illinois
MD, MPH
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David J. Hyman
5Baylor College of Medicine, Baylor University, Houston, Texas
MD, MPH
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Kenneth P. Anderson
2NorthShore University HealthSystem, Evanston, Illinois
3Pritzker School of Medicine, The University of Chicago, Chicago, Illinois
DO
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Christopher M. Masi
2NorthShore University HealthSystem, Evanston, Illinois
3Pritzker School of Medicine, The University of Chicago, Chicago, Illinois
MD, PhD
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  • Article
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  • Figure 1
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    Figure 1

    Flowchart of procedure for identifying patients at risk for undiagnosed hypertension.

  • Figure 2
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    Figure 2

    Overlap among hypertension screening algorithms.

  • Figure 3
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    Figure 3

    Flowchart of diagnostic resolution among cohort at risk for undiagnosed hypertension.

    AOBP = automated office blood pressure; ICD-9 = International Classification of Disease, 9th edition; PCP=primary care physician.

Tables

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    Table 1

    Number of At-Risk Patients Identified by Each Hypertension Screening Algorithm

    AlgorithmNumber Identified
    1. All patients whose 3 most recent encounters yielded a mean SBP >140 mm Hg or a mean DBP >90 mm. Encounters used were within 12 months before their most recent encounter720
    2. All patients who had 3 encounters with a SBP >140 or DBP >90 mm Hg within 12 months before their most recent encounter968
    3. Patients who had a single encounter with a SBP >180 or a DBP >100 mm Hg within 12 months before their most recent encounter527
    Unique patients identified by algorithms 1, 2, or 31,586
    • SBP = systolic blood pressure; DBP = diastolic blood pressure.

    • Note: All data were obtained from outpatient encounters with a primary care physician or specialist.

    • View popup
    Table 2

    Characteristics of Phase 1 Study Patients Who Completed and Did Not Complete AOBP Measurements, at Baseline

    CharacteristicPatients Who Completed AOBP (n=475)Patients Who Did Not Complete AOPB (n=957)P Value
    Age, median y (IQR)54.4 (44.5–64.9)50.1 (38.9–60.4)<.01
    Blood pressure, mean mm Hg (SD)
     Systolic136.5 (9.35)136.1 (9.76).46
     Diastolic82.3 (7.05)82.5 (7.25).52
    BMI, median kg/m2 (IQR)29.6 (26.3–33.8)30.1 (26.1–34.6).13
    Sex, female, No. (%)226 (47.6)459 (48).89
    Ethnicity, No. (%).36
     African American29 (6.1)42 (4.4)
     Asian13 (2.7)26 (2.7)
     White337 (70.9)655 (68.5)
     Hispanic/Latino16 (3.4)42 (4.4)
     Other80 (16.8)192 (20.1)
    GERD, No. (%)72 (15.2)129 (13.5).39
    Asthma, No. (%)36 (7.6)104 (10.9).05
    Depression, No. (%)36 (7.6)75 (7.8).89
    Diabetes mellitus, No. (%)29 (6.1)62 (6.5).77
    COPD, No. (%)10 (2.1)16 (1.7).59
    Coronary artery disease, No. (%)5 (1.1)13 (1.4).64
    Congestive heart failure, No. (%)2 (0.4)7 (0.7).49
    Prior myocardial infarction, No. (%)1 (0.2)2 (0.2).99
    • AOPB =ambulatory office blood pressure; PBMI = body mass index; COPD = chronic obstructive pulmonary disease; GERD = gastroesophageal reflux disease; IQR = interquartile range.

    • View popup
    Table 3

    Positive Predictive Values of Algorithms for Identifying Patients at Risk of Undiagnosed Hypertension Screening

    AlgorithmPatient Identified as at Risk and Completed AOBPPatient Hypertensive by AOBPaPPV %95% CI %
    12341365851–65
    23211685247–58
    3138705142–59
    Any4752495248–57
    • AOBP = automated office blood pressure; PPV = positive predictive value.

    • ↵a Systolic blood pressure ≥135 mm Hg or diastolic blood pressure ≥85 mm Hg.

Additional Files

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  • In Brief

    A Technology-Based Quality Innovation to Identify Undiagnosed Hypertension Among Active Primary Care Patients

    Michael K. Rakotz , and colleagues

    Background Hypertension (high blood pressure) can be difficult to diagnose. This study describes the development and evaluation of a technology-based strategy to screen for undiagnosed hypertension and the implementation of a continuous quality improvement process to improve the accuracy of hypertension diagnosis.

    What This Study Found 1,432 patients at risk for undiagnosed hypertension were invited to complete an automated office blood pressure protocol to obtain multiple blood pressure measurements. A quality improvement process was then implemented, including regular physician feedback and office-based computer alerts to further evaluate the 1,033 at-risk patients not screened in phase one. The initiative successfully identified patients at risk for undiagnosed hypertension and classified most patients based on their automated office blood pressure reading. Specifically, this process reduced the rate of being at risk for undiagnosed hypertension over a 30-month follow-up period by more than 72%. By the end of the follow-up period, 293 patients (28%) had not yet been classified and remained at risk for undiagnosed hypertension.

    Implications

    • The authors suggest that these strategies not only have the potential to eliminate undiagnosed hypertension, they also may be applicable to other common undiagnosed chronic diseases. In addition, similar methods can be adapted to inform clinicians and patients on blood pressure control after the diagnosis of hypertension.
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The Annals of Family Medicine: 12 (4)
The Annals of Family Medicine: 12 (4)
Vol. 12, Issue 4
July/August 2014
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A Technology-Based Quality Innovation to Identify Undiagnosed Hypertension Among Active Primary Care Patients
Michael K. Rakotz, Bernard G. Ewigman, Menaka Sarav, Ruth E. Ross, Ari Robicsek, Chad W. Konchak, Thomas F. Gavagan, David W. Baker, David J. Hyman, Kenneth P. Anderson, Christopher M. Masi
The Annals of Family Medicine Jul 2014, 12 (4) 352-358; DOI: 10.1370/afm.1665

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A Technology-Based Quality Innovation to Identify Undiagnosed Hypertension Among Active Primary Care Patients
Michael K. Rakotz, Bernard G. Ewigman, Menaka Sarav, Ruth E. Ross, Ari Robicsek, Chad W. Konchak, Thomas F. Gavagan, David W. Baker, David J. Hyman, Kenneth P. Anderson, Christopher M. Masi
The Annals of Family Medicine Jul 2014, 12 (4) 352-358; DOI: 10.1370/afm.1665
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  • 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines
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