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Research ArticleOriginal Research

Identifying Practice Facilitation Delays and Barriers in Primary Care Quality Improvement

Jiancheng Ye, Renwen Zhang, Jennifer E. Bannon, Ann A. Wang, Theresa L. Walunas, Abel N. Kho and Nicholas D. Soulakis
The Journal of the American Board of Family Medicine September 2020, 33 (5) 655-664; DOI: https://doi.org/10.3122/jabfm.2020.05.200058
Jiancheng Ye
From the Feinberg School of Medicine, Northwestern University, Chicago, IL (JY, AAW); Center for Health Information Partnerships (CHiP), Institute of Public Health & Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL (JY, JEB, TLW, ANK); Department of Communication Studies, Northwestern University, Evanston, IL (RZ); Division of Health and Biomedical Informatics, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University Chicago, IL (ANK, NDS); Division of General Internal Medicine and Geriatrics, Department of Medicine, Feinberg School of Medicine, Northwestern University Chicago, IL (TLW, ANK).
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Renwen Zhang
From the Feinberg School of Medicine, Northwestern University, Chicago, IL (JY, AAW); Center for Health Information Partnerships (CHiP), Institute of Public Health & Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL (JY, JEB, TLW, ANK); Department of Communication Studies, Northwestern University, Evanston, IL (RZ); Division of Health and Biomedical Informatics, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University Chicago, IL (ANK, NDS); Division of General Internal Medicine and Geriatrics, Department of Medicine, Feinberg School of Medicine, Northwestern University Chicago, IL (TLW, ANK).
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Jennifer E. Bannon
From the Feinberg School of Medicine, Northwestern University, Chicago, IL (JY, AAW); Center for Health Information Partnerships (CHiP), Institute of Public Health & Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL (JY, JEB, TLW, ANK); Department of Communication Studies, Northwestern University, Evanston, IL (RZ); Division of Health and Biomedical Informatics, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University Chicago, IL (ANK, NDS); Division of General Internal Medicine and Geriatrics, Department of Medicine, Feinberg School of Medicine, Northwestern University Chicago, IL (TLW, ANK).
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Ann A. Wang
From the Feinberg School of Medicine, Northwestern University, Chicago, IL (JY, AAW); Center for Health Information Partnerships (CHiP), Institute of Public Health & Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL (JY, JEB, TLW, ANK); Department of Communication Studies, Northwestern University, Evanston, IL (RZ); Division of Health and Biomedical Informatics, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University Chicago, IL (ANK, NDS); Division of General Internal Medicine and Geriatrics, Department of Medicine, Feinberg School of Medicine, Northwestern University Chicago, IL (TLW, ANK).
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Theresa L. Walunas
From the Feinberg School of Medicine, Northwestern University, Chicago, IL (JY, AAW); Center for Health Information Partnerships (CHiP), Institute of Public Health & Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL (JY, JEB, TLW, ANK); Department of Communication Studies, Northwestern University, Evanston, IL (RZ); Division of Health and Biomedical Informatics, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University Chicago, IL (ANK, NDS); Division of General Internal Medicine and Geriatrics, Department of Medicine, Feinberg School of Medicine, Northwestern University Chicago, IL (TLW, ANK).
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Abel N. Kho
From the Feinberg School of Medicine, Northwestern University, Chicago, IL (JY, AAW); Center for Health Information Partnerships (CHiP), Institute of Public Health & Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL (JY, JEB, TLW, ANK); Department of Communication Studies, Northwestern University, Evanston, IL (RZ); Division of Health and Biomedical Informatics, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University Chicago, IL (ANK, NDS); Division of General Internal Medicine and Geriatrics, Department of Medicine, Feinberg School of Medicine, Northwestern University Chicago, IL (TLW, ANK).
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Nicholas D. Soulakis
From the Feinberg School of Medicine, Northwestern University, Chicago, IL (JY, AAW); Center for Health Information Partnerships (CHiP), Institute of Public Health & Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL (JY, JEB, TLW, ANK); Department of Communication Studies, Northwestern University, Evanston, IL (RZ); Division of Health and Biomedical Informatics, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University Chicago, IL (ANK, NDS); Division of General Internal Medicine and Geriatrics, Department of Medicine, Feinberg School of Medicine, Northwestern University Chicago, IL (TLW, ANK).
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  • Figure 1.
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    Figure 1.

    Workflow of practice facilitation in the Healthy Hearts in the Heartland (H3) initiative. Abbreviations: PF, practice facilitator; QB, QuickBase; NU, Northwestern University; ABCS, aspirin use, blood pressure, cholesterol, and smoking.

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    Figure 2.

    Example practice (#117) activities and delays. This Figure illustrates the mechanism by which the 2 types of delays are identified. If the interval between activities (IBA) is higher than the Z-score (described in Methods, Step 2), a delay will be detected. X-axis is the sequence number of each activity; Y-axis is the number of days between activities; Mov_Avg is the moving average results of IBA; Mov_Avg 95% CI is the 95% confidence interval of the Mov_Avg results.

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Tables

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    Table 1.

    Characteristics of 226 Practices in Illinois, Wisconsin, and Indiana Participating in the Healthy Hearts in the Heartland (H3) Initiative across 4 Waves in 2016

    CharacteristicsN (%)
    Number of practices by wave
        Wave 142 (18.6)
        Wave 240 (17.7)
        Wave 367 (29.6)
        Wave 477 (34.1)
    Clinicians, n
        Solo practice67 (29.6)
        2−5103 (45.6)
        6−1034 (15.0)
        11−1510 (4.40)
        16−2012 (5.30)
    State
        Illinois152 (67.3)
        Wisconsin22 (9.7)
        Indiana52 (23.0)
    • The 4 waves were the randomization conditions of the study. The waves determined when a practice started receiving the 12-month intervention. Practices in Wave 1 started from the first quarter of 2016; wave 2 started from the second quarter of 2016, and so on. The four waves were reported separately because the starting and end dates were different.

    • View popup
    Table 2.

    Characteristics of Two Types of Delays across the 4 Waves with a 12-Month Period

    Wave 1Wave 2Wave 3Wave 4Total
    Total number of Delay I*8444142131401
    Total number of Delay II†45258463217
    Median No. of Delay I per practice (IQR)2 (1 to 3)1 (0 to 2)2 (1 to 3)2 (1 to 2)2 (1 to 3)
    Median No. of Delay II per practice (IQR)1 (0 to 2)0 (0 to 1)1 (0 to 2)1 (0 to 1)1(0 to 1)
    • IQR, interquartile range.

    • The 4 waves were reported separately because the starting and end dates were different.

    • ↵* Delay I was calculated based on Z score > 1.282, which corresponds to a one-tailed 90% confidence interval. Delay I is identified if the interval between activities (IBA) deviates from the practice's normal pattern of facilitation activities.

    • ↵† Delay II was calculated based on Z-score > 1.645, which corresponds to a one-tailed 95% confidence interval. Delay II captures a larger deviation from the normal pattern compared to Delay I. Delay I is more sensitive to detecting delays than Delay II and could capture more instances of infrequent facilitation activities.

    • View popup
    Table 3.

    Barriers to Practice Facilitation and Quality Improvement Intervention (Source: Practice Facilitators' Notes)

    DomainBarriersN (%)
    Practice-related barriersLack of time and staff378 (44.37)
    EHR-related issues136 (15.96)
    Lack of buy-in/engagement133 (15.61)
    Other*59 (6.92)
    Staff turnover51 (5.99)
    Workflow issues29 (3.40)
    Implementation-related barriersTechnical issues37 (4.34)
    Lack of guidelines12 (1.41)
    Lack of reimbursement8 (0.94)
    Lack of language diversity of intervention materials9 (1.06)
    • ↵* Examples include low resources, lack of investment, clinic construction, patient issues, and logistic issues.

    • EHR, electronic health record.

    • View popup
    Table 4.

    Regression Analysis of Barriers on Two Types of Delays

    VariableDelay IDelay II
    Beta (95% CI)Beta (95% CI)
    Practice-related barriers
    Lack of time & staff0.16 (0.08, 0.24)*0.08 (0.02, 0.14)*
    EHR-related issues0.29 (0.14, 0.44)*0.13 (0.02, 0.24)†
    Lack of buy-in/engagement0.19 (0.01, 0.36)†0.15 (0.02, 0.27)†
    Staff turnover0.48 (0.15, 0.82)*0.31 (0.06, 0.55)†
    Workflow issues0.06 (−0.37, 0.48)0.20 (−0.10, 0.51)
    Other0.26 (−0.04, 0.56)0.15 (−0.07, 0.36)
    Implementation-related barriers
    Technical issues0.18 (−0.23, 0.60)0.04 (−0.36, 0.34)
    Lack of guidelines0.55 (−0.25, 1.34)0.10 (−0.48, 0.67)
    Lack of reimbursement0.59 (−0.46, 1.64)0.54 (−0.21, 1.29)
    Lack of language diversity of intervention materials0.61 (−0.20, 1.43)0.25 (−0.34, 0.84)
    • EHR, electronic health record; CI, confidence interval.

    • ↵* Significant at P < .01.

    • ↵† Significant at P < .05.

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    Appendix A.

    Practice Intervention Tracking Items

    Intervention IDCategoryMeasureComponent
    1A. Point-of-Care Clinical Decision SupportAspirinReminder to order aspirin/antiplatelet drug for pts with IV.D (or CVD)
    2A. Point-of-Care Clinical Decision SupportBPAlert staff to a patient with uncontrolled blood pressure
    3A. Point-of-Care Clinical Decision SupportCholesterolAlert for a lipid panel (or cholesterol) in ASCVD (or IV.D, or CVD)
    4A. Point-of-Care Clinical Decision SupportCholesterolAlert for a lipid panel (or cholesterol) in diabetes mellitus
    5A. Point-of-Care Clinical Decision SupportCholesterolAlert for a lipid panel in general population (low risk patients)
    6A. Point-of-Care Clinical Decision SupportCholesterolReminder to order a statin in ASCVD (or IV.D, or CVD)
    7A. Point-of-Care Clinical Decision SupportCholesterolReminder to order statin in diabetic patients
    8A. Point-of-Care Clinical Decision SupportCholesterolAlert to order a statin in pts with LDL 190
    9A. Point-of-Care Clinical Decision SupportCholesterolAlert to order statin in general population with increased risk (based on a risk calculator)
    10A. Point-of-Care Clinical Decision SupportAspirinReminder to order aspirin for primary prevention in appropriate patients
    11A. Point-of-Care Clinical Decision SupportSmokingReminder for intervention in tobacco users or smokers
    12B. Other Clinical Decision Support ActivitiesBPOrders/patient instructions/patient education for home BP monitoring
    13B. Other Clinical Decision Support ActivitiesCholesterolPatient education on cholesterol and/or cholesterol treatment
    14B. Other Clinical Decision Support ActivitiesCholesterolStanding orders for lipid profiles
    15B. Other Clinical Decision Support ActivitiesSmokingPatient education on tobacco cessation
    16C. Practice WorkflowsBPBlood pressure measurement protocol
    17C. Practice WorkflowsBPBlood pressure treatment protocol
    18C. Practice WorkflowsBPWorkflow for patient to report home blood pressures
    19C. Practice WorkflowsSmokingTobacco use/smoking assessment part of intake or rooming process
    20C. Practice WorkflowsSmokingClinic based tobacco use/smoking interventions
    21D. Reports on ABCS PerformanceAspirinMetric for use of aspirin or another antithrombotic therapy in IV.D
    22D. Reports on ABCS PerformanceBPMetric for blood pressure control among patients with hypertension
    23D. Reports on ABCS PerformanceCholesterolMetric for cholesterol treatment or control
    24D. Reports on ABCS PerformanceSmokingMetric for tobacco use assessment and brief intervention
    25E. Lists of Patients Not Meeting ABCS MeasuresAspirinList of patients with IV.D not meeting aspirin/antithrombotic measure
    26E. Lists of Patients Not Meeting ABCS MeasuresBPList of patients with uncontrolled blood pressure
    27E. Lists of Patients Not Meeting ABCS MeasuresCholesterolList of patients needing cholesterol measurement and/or treatment
    28E. Lists of Patients Not Meeting ABCS MeasuresSmokingList of tobacco users/smokers
    29F. Population Management OutreachAspirinOutreach to patients with IV.D not on aspirin or another antithrombotic
    30F. Population Management OutreachBPOutreach to patients with uncontrolled hypertension
    31F. Population Management OutreachCholesterolOutreach to patients who need cholesterol measurement or statin prescription
    32F. Population Management OutreachSmokingOutreach to tobacco users or smokers
    33F. Population Management OutreachCholesterolOutreach to patients with increased CVD risk who are not on a statin for primary prevention
    34G. Population Management Community ResourcesBPReferral to community pharmacist for hypertension medication management
    35G. Population Management Community ResourcesOtherReferral to HealtheRx resource
    • BP, blood pressure; LDL, low-density lipoprotein; CVD, cardiovascular disease; ASCVD, atherosclerotic cardiovascular disease; IVD, Ischemic vascular disease.

    • View popup
    Appendix B.

    Codebook for Qualitative Analysis

    IdCodeSub-Codes
    1–Practice-related barriers
    1-1Lack of time/staffScheduling issues
    • Providers/Staff were busy/on vacation/personal issues (e.g., family emergency)

    • Competing demands/priorities Limited time for QI activities Lack of staff/staff burnout

    1-2Lack of buy-in/engagementProviders negative attitudes/belief
    Lack of engagement/leader support/interest
    1-3EHR-related issuesEHR update/change
    Outdated EHR/data infrastructure
    Facilitators had no access to EHR
    1-4OtherLow resources
    Lack of investment
    Patient issue
    Clinic construction
    Logistic issues
    1-5Staff turnoverStaff turnover
    1-6Workflow issuesClinical workflow
    Teamwork/Collaboration issues
    Communication inefficiency
    2–Implementation-related barriers
    2-1Technical issuesDelays in data extraction/access
    Lack of proper document, such as BAA
    2-2Lack of guidelinesLack of guidance on EHR documentation
    Insufficient guidelines on data extraction
    2 to 3Lack of reimbursementLack of reimbursement
    2 to 4Lack of language diversity of intervention materialsMost intervention materials are in English
    • EHR, electronic health record; BAA, business associate agreement.

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The Journal of the American Board of Family     Medicine: 33 (5)
The Journal of the American Board of Family Medicine
Vol. 33, Issue 5
September/October 2020
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Identifying Practice Facilitation Delays and Barriers in Primary Care Quality Improvement
Jiancheng Ye, Renwen Zhang, Jennifer E. Bannon, Ann A. Wang, Theresa L. Walunas, Abel N. Kho, Nicholas D. Soulakis
The Journal of the American Board of Family Medicine Sep 2020, 33 (5) 655-664; DOI: 10.3122/jabfm.2020.05.200058

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Identifying Practice Facilitation Delays and Barriers in Primary Care Quality Improvement
Jiancheng Ye, Renwen Zhang, Jennifer E. Bannon, Ann A. Wang, Theresa L. Walunas, Abel N. Kho, Nicholas D. Soulakis
The Journal of the American Board of Family Medicine Sep 2020, 33 (5) 655-664; DOI: 10.3122/jabfm.2020.05.200058
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    • Appendix C. Wave 1 Practice Activity Trend (in Descending Total Number of Activities)
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Keywords

  • Cardiovascular Diseases
  • Personnel Turnover
  • Practice-based Research
  • Practice Facilitation
  • Primary Health Care
  • Quality Improvement
  • Regression Analysis
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  • Work Engagement
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