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Research ArticleOriginal Research

How Does Prior Experience Pay Off in Large-Scale Quality Improvement Initiatives?

Deborah J. Cohen, Bijal A. Balasubramanian, Stephan Lindner, William L. Miller, Shannon M. Sweeney, Jennifer D. Hall, Rikki Ward, Miguel Marino, Rachel Springer, K. John McConnell, Jennifer R. Hemler, Sarah S. Ono, David Ezekiel-Herrera, Andrea Baron, Benjamin F. Crabtree and Leif I. Solberg
The Journal of the American Board of Family Medicine September 2022, jabfm.2022.AP.220088; DOI: https://doi.org/10.3122/jabfm.2022.AP.220088
Deborah J. Cohen
From Department of Family Medicine and Department of Medical and Clinical Epidemiology, Oregon Health & Science University, Portland, OR (DJC); Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas, Dallas, TX (BAB, RW); Center for Health Systems Effectiveness, Oregon Health & Science University, Portland, OR (SL, KJM); Department of Family Medicine, Lehigh Valley Health Network, Allentown, PA (WLM); Department of Family Medicine, Oregon Health & Science University Portland, OR (SMS, JDH, MM, RS, DE-H, AB); Department of Family Medicine and Community Health, Research Division, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ (JRH, BFC); Department of Psychiatry and Department of Family Medicine, Oregon Health & Science University Portland, OR; VHA Office of Rural Health, Department of Veterans Affairs and VA Portland Health Care System, Portland, OR (SSO); HealthPartners Institute, Minneapolis MN (LIS).
PhD
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Bijal A. Balasubramanian
From Department of Family Medicine and Department of Medical and Clinical Epidemiology, Oregon Health & Science University, Portland, OR (DJC); Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas, Dallas, TX (BAB, RW); Center for Health Systems Effectiveness, Oregon Health & Science University, Portland, OR (SL, KJM); Department of Family Medicine, Lehigh Valley Health Network, Allentown, PA (WLM); Department of Family Medicine, Oregon Health & Science University Portland, OR (SMS, JDH, MM, RS, DE-H, AB); Department of Family Medicine and Community Health, Research Division, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ (JRH, BFC); Department of Psychiatry and Department of Family Medicine, Oregon Health & Science University Portland, OR; VHA Office of Rural Health, Department of Veterans Affairs and VA Portland Health Care System, Portland, OR (SSO); HealthPartners Institute, Minneapolis MN (LIS).
MBBS, PhD
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Stephan Lindner
From Department of Family Medicine and Department of Medical and Clinical Epidemiology, Oregon Health & Science University, Portland, OR (DJC); Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas, Dallas, TX (BAB, RW); Center for Health Systems Effectiveness, Oregon Health & Science University, Portland, OR (SL, KJM); Department of Family Medicine, Lehigh Valley Health Network, Allentown, PA (WLM); Department of Family Medicine, Oregon Health & Science University Portland, OR (SMS, JDH, MM, RS, DE-H, AB); Department of Family Medicine and Community Health, Research Division, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ (JRH, BFC); Department of Psychiatry and Department of Family Medicine, Oregon Health & Science University Portland, OR; VHA Office of Rural Health, Department of Veterans Affairs and VA Portland Health Care System, Portland, OR (SSO); HealthPartners Institute, Minneapolis MN (LIS).
PhD
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William L. Miller
From Department of Family Medicine and Department of Medical and Clinical Epidemiology, Oregon Health & Science University, Portland, OR (DJC); Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas, Dallas, TX (BAB, RW); Center for Health Systems Effectiveness, Oregon Health & Science University, Portland, OR (SL, KJM); Department of Family Medicine, Lehigh Valley Health Network, Allentown, PA (WLM); Department of Family Medicine, Oregon Health & Science University Portland, OR (SMS, JDH, MM, RS, DE-H, AB); Department of Family Medicine and Community Health, Research Division, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ (JRH, BFC); Department of Psychiatry and Department of Family Medicine, Oregon Health & Science University Portland, OR; VHA Office of Rural Health, Department of Veterans Affairs and VA Portland Health Care System, Portland, OR (SSO); HealthPartners Institute, Minneapolis MN (LIS).
MD, MA
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Shannon M. Sweeney
From Department of Family Medicine and Department of Medical and Clinical Epidemiology, Oregon Health & Science University, Portland, OR (DJC); Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas, Dallas, TX (BAB, RW); Center for Health Systems Effectiveness, Oregon Health & Science University, Portland, OR (SL, KJM); Department of Family Medicine, Lehigh Valley Health Network, Allentown, PA (WLM); Department of Family Medicine, Oregon Health & Science University Portland, OR (SMS, JDH, MM, RS, DE-H, AB); Department of Family Medicine and Community Health, Research Division, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ (JRH, BFC); Department of Psychiatry and Department of Family Medicine, Oregon Health & Science University Portland, OR; VHA Office of Rural Health, Department of Veterans Affairs and VA Portland Health Care System, Portland, OR (SSO); HealthPartners Institute, Minneapolis MN (LIS).
PhD, MPH
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Jennifer D. Hall
From Department of Family Medicine and Department of Medical and Clinical Epidemiology, Oregon Health & Science University, Portland, OR (DJC); Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas, Dallas, TX (BAB, RW); Center for Health Systems Effectiveness, Oregon Health & Science University, Portland, OR (SL, KJM); Department of Family Medicine, Lehigh Valley Health Network, Allentown, PA (WLM); Department of Family Medicine, Oregon Health & Science University Portland, OR (SMS, JDH, MM, RS, DE-H, AB); Department of Family Medicine and Community Health, Research Division, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ (JRH, BFC); Department of Psychiatry and Department of Family Medicine, Oregon Health & Science University Portland, OR; VHA Office of Rural Health, Department of Veterans Affairs and VA Portland Health Care System, Portland, OR (SSO); HealthPartners Institute, Minneapolis MN (LIS).
MPH
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Rikki Ward
From Department of Family Medicine and Department of Medical and Clinical Epidemiology, Oregon Health & Science University, Portland, OR (DJC); Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas, Dallas, TX (BAB, RW); Center for Health Systems Effectiveness, Oregon Health & Science University, Portland, OR (SL, KJM); Department of Family Medicine, Lehigh Valley Health Network, Allentown, PA (WLM); Department of Family Medicine, Oregon Health & Science University Portland, OR (SMS, JDH, MM, RS, DE-H, AB); Department of Family Medicine and Community Health, Research Division, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ (JRH, BFC); Department of Psychiatry and Department of Family Medicine, Oregon Health & Science University Portland, OR; VHA Office of Rural Health, Department of Veterans Affairs and VA Portland Health Care System, Portland, OR (SSO); HealthPartners Institute, Minneapolis MN (LIS).
MPH
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Miguel Marino
From Department of Family Medicine and Department of Medical and Clinical Epidemiology, Oregon Health & Science University, Portland, OR (DJC); Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas, Dallas, TX (BAB, RW); Center for Health Systems Effectiveness, Oregon Health & Science University, Portland, OR (SL, KJM); Department of Family Medicine, Lehigh Valley Health Network, Allentown, PA (WLM); Department of Family Medicine, Oregon Health & Science University Portland, OR (SMS, JDH, MM, RS, DE-H, AB); Department of Family Medicine and Community Health, Research Division, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ (JRH, BFC); Department of Psychiatry and Department of Family Medicine, Oregon Health & Science University Portland, OR; VHA Office of Rural Health, Department of Veterans Affairs and VA Portland Health Care System, Portland, OR (SSO); HealthPartners Institute, Minneapolis MN (LIS).
PhD
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Rachel Springer
From Department of Family Medicine and Department of Medical and Clinical Epidemiology, Oregon Health & Science University, Portland, OR (DJC); Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas, Dallas, TX (BAB, RW); Center for Health Systems Effectiveness, Oregon Health & Science University, Portland, OR (SL, KJM); Department of Family Medicine, Lehigh Valley Health Network, Allentown, PA (WLM); Department of Family Medicine, Oregon Health & Science University Portland, OR (SMS, JDH, MM, RS, DE-H, AB); Department of Family Medicine and Community Health, Research Division, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ (JRH, BFC); Department of Psychiatry and Department of Family Medicine, Oregon Health & Science University Portland, OR; VHA Office of Rural Health, Department of Veterans Affairs and VA Portland Health Care System, Portland, OR (SSO); HealthPartners Institute, Minneapolis MN (LIS).
MS
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K. John McConnell
From Department of Family Medicine and Department of Medical and Clinical Epidemiology, Oregon Health & Science University, Portland, OR (DJC); Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas, Dallas, TX (BAB, RW); Center for Health Systems Effectiveness, Oregon Health & Science University, Portland, OR (SL, KJM); Department of Family Medicine, Lehigh Valley Health Network, Allentown, PA (WLM); Department of Family Medicine, Oregon Health & Science University Portland, OR (SMS, JDH, MM, RS, DE-H, AB); Department of Family Medicine and Community Health, Research Division, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ (JRH, BFC); Department of Psychiatry and Department of Family Medicine, Oregon Health & Science University Portland, OR; VHA Office of Rural Health, Department of Veterans Affairs and VA Portland Health Care System, Portland, OR (SSO); HealthPartners Institute, Minneapolis MN (LIS).
PhD
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Jennifer R. Hemler
From Department of Family Medicine and Department of Medical and Clinical Epidemiology, Oregon Health & Science University, Portland, OR (DJC); Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas, Dallas, TX (BAB, RW); Center for Health Systems Effectiveness, Oregon Health & Science University, Portland, OR (SL, KJM); Department of Family Medicine, Lehigh Valley Health Network, Allentown, PA (WLM); Department of Family Medicine, Oregon Health & Science University Portland, OR (SMS, JDH, MM, RS, DE-H, AB); Department of Family Medicine and Community Health, Research Division, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ (JRH, BFC); Department of Psychiatry and Department of Family Medicine, Oregon Health & Science University Portland, OR; VHA Office of Rural Health, Department of Veterans Affairs and VA Portland Health Care System, Portland, OR (SSO); HealthPartners Institute, Minneapolis MN (LIS).
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Sarah S. Ono
From Department of Family Medicine and Department of Medical and Clinical Epidemiology, Oregon Health & Science University, Portland, OR (DJC); Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas, Dallas, TX (BAB, RW); Center for Health Systems Effectiveness, Oregon Health & Science University, Portland, OR (SL, KJM); Department of Family Medicine, Lehigh Valley Health Network, Allentown, PA (WLM); Department of Family Medicine, Oregon Health & Science University Portland, OR (SMS, JDH, MM, RS, DE-H, AB); Department of Family Medicine and Community Health, Research Division, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ (JRH, BFC); Department of Psychiatry and Department of Family Medicine, Oregon Health & Science University Portland, OR; VHA Office of Rural Health, Department of Veterans Affairs and VA Portland Health Care System, Portland, OR (SSO); HealthPartners Institute, Minneapolis MN (LIS).
PhD
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David Ezekiel-Herrera
From Department of Family Medicine and Department of Medical and Clinical Epidemiology, Oregon Health & Science University, Portland, OR (DJC); Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas, Dallas, TX (BAB, RW); Center for Health Systems Effectiveness, Oregon Health & Science University, Portland, OR (SL, KJM); Department of Family Medicine, Lehigh Valley Health Network, Allentown, PA (WLM); Department of Family Medicine, Oregon Health & Science University Portland, OR (SMS, JDH, MM, RS, DE-H, AB); Department of Family Medicine and Community Health, Research Division, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ (JRH, BFC); Department of Psychiatry and Department of Family Medicine, Oregon Health & Science University Portland, OR; VHA Office of Rural Health, Department of Veterans Affairs and VA Portland Health Care System, Portland, OR (SSO); HealthPartners Institute, Minneapolis MN (LIS).
MS
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Andrea Baron
From Department of Family Medicine and Department of Medical and Clinical Epidemiology, Oregon Health & Science University, Portland, OR (DJC); Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas, Dallas, TX (BAB, RW); Center for Health Systems Effectiveness, Oregon Health & Science University, Portland, OR (SL, KJM); Department of Family Medicine, Lehigh Valley Health Network, Allentown, PA (WLM); Department of Family Medicine, Oregon Health & Science University Portland, OR (SMS, JDH, MM, RS, DE-H, AB); Department of Family Medicine and Community Health, Research Division, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ (JRH, BFC); Department of Psychiatry and Department of Family Medicine, Oregon Health & Science University Portland, OR; VHA Office of Rural Health, Department of Veterans Affairs and VA Portland Health Care System, Portland, OR (SSO); HealthPartners Institute, Minneapolis MN (LIS).
MPH
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Benjamin F. Crabtree
From Department of Family Medicine and Department of Medical and Clinical Epidemiology, Oregon Health & Science University, Portland, OR (DJC); Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas, Dallas, TX (BAB, RW); Center for Health Systems Effectiveness, Oregon Health & Science University, Portland, OR (SL, KJM); Department of Family Medicine, Lehigh Valley Health Network, Allentown, PA (WLM); Department of Family Medicine, Oregon Health & Science University Portland, OR (SMS, JDH, MM, RS, DE-H, AB); Department of Family Medicine and Community Health, Research Division, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ (JRH, BFC); Department of Psychiatry and Department of Family Medicine, Oregon Health & Science University Portland, OR; VHA Office of Rural Health, Department of Veterans Affairs and VA Portland Health Care System, Portland, OR (SSO); HealthPartners Institute, Minneapolis MN (LIS).
PhD
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Leif I. Solberg
From Department of Family Medicine and Department of Medical and Clinical Epidemiology, Oregon Health & Science University, Portland, OR (DJC); Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas, Dallas, TX (BAB, RW); Center for Health Systems Effectiveness, Oregon Health & Science University, Portland, OR (SL, KJM); Department of Family Medicine, Lehigh Valley Health Network, Allentown, PA (WLM); Department of Family Medicine, Oregon Health & Science University Portland, OR (SMS, JDH, MM, RS, DE-H, AB); Department of Family Medicine and Community Health, Research Division, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ (JRH, BFC); Department of Psychiatry and Department of Family Medicine, Oregon Health & Science University Portland, OR; VHA Office of Rural Health, Department of Veterans Affairs and VA Portland Health Care System, Portland, OR (SSO); HealthPartners Institute, Minneapolis MN (LIS).
MD
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Article Figures & Data

Figures

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  • Appendix Figure 1.
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    Appendix Figure 1.

    Map of EvidenceNOW cooperatives.

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    Appendix Figure 2.
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    Appendix Figure 2.

    EvidenceNOW recruitment heat maps comparing two cooperatives, (A) One with high and (B) One with low experience and prior infrastructure investment.

    The figures below depict two Cooperative regions with major citied marked with a red star. Light grey areas depict regions classified as non-rural; hatched lines depict regions classified as rural. Cooperative organizational locations are also indicated – the home institution of the Principal Investigator is marked with a green hexagon and the location of each Practice Facilitator Organization is marked with a green cross. These maps also overlay the density of practices recruited by each Cooperative – with regions where fewer practices were engaged indicated in light blue and regions where more practices were engaged indicated in yellow.

Tables

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    Table 1.

    Description and Assignment of Cooperative Level of Prior Experience (Independent Variable)

    Embedded Image
    • Abbreviations: AHEC, Area Health Education Centers; EHR, electronic health record.

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    Table 2.

    Measures of Effectiveness (Dependent Variables)

    Concept/DefinitionMeasure / How ScoredHow and when collected
    Ability to deliver Facilitation
    Amount of facilitation deliveredTotal number of hours and months of facilitation a Cooperative delivered to each practiceCollected by each Cooperatives’ facilitators.
    Collected from facilitators first contact with practices to their last contact.
    Clinical Capacity
    Adaptive Reserve (AR)14-item measure assessing practice capacity for adapting to change; individual practice members assessed experience of organization’s communication, teamwork, mindfulness, leadership, heedful interaction, sensemaking, work environment, learning culture and trust. Scores from (0 to 1).Collected by survey distributed to clinical practice members by Cooperatives.
    Collected at baseline and at end of the intervention
    Change Process Capacity Questionnaire (CPCQ)14-items of CPCQ; we selected the measures focused on the extent to which practices used different types of quality improvement strategies for cardiovascular disease prevention. Scores from (−28 to 28).Collected by survey that was completed by lead clinician or office manager.
    Collected at baseline and at end of the intervention.
    Clinical Quality
    Aspirin Therapy (CMS164v4) Percentage of patients 18 years of age and older with ischemic vascular disease with documented use of aspirin or another antithrombotic. Scored from (0 to 100%). Collected by Cooperatives through EHR-generated reports, EHR chart review, and health information exchange reports.
    Quarterly rolling 12-month performance on each measure at the practice level
    Blood Pressure Management (CMS165v4) Percentage of patients 18 to 85 years of age with a diagnosis of hypertension whose BP was adequately controlled (<140/90 mm Hg). Scored from (0 to 100%). 
    Cholesterol Management (CMS347v1) Percentage of adult patients at high risk for a cardiovascular event who were using or prescribed statin therapy. Scored from (0 to 100%). 
    Smoking Cessation (CMS138v4) Percentage of patients aged 18 years and older who were screened for tobacco use one or more times within 24 months AND who received cessation counseling, if identified as a tobacco user. Scored from (0 to 100%). 
    • For the ABCS data, we reviewed data, including trajectories of change over time, computing descriptive statistics related to variability and removing practices that exhibited extreme, implausible jumps or otherwise implausible trajectories that could not be addressed by Cooperatives. Practices were also removed that had submissions with non-standard measurement periods. For all other variables and outcomes, data quality checks were performed in an iterative manner, with our team identifying anomalies or irregularities (e.g., excessive missingness, implausible or non-sensical inputs) and Cooperatives addressing those on their end and resubmitting corrected data.

    • Abbreviations: ABCS, Aspirin, Blood Pressure, Cholesterol, and Smoking; EHR, electronic health record.

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    Table 3.

    Qualitative Data Elements and Their Collection

    Qualitative Data CollectedWhat Was CollectedHow Were Data ObtainedWhen Were Data Collected
    ArtifactsDocuments related to Cooperatives’ work (e.g., grant application, training materials)Obtained from CooperativesThroughout initiative
    Online DiariesPlatform to share real-time implementation experiencesOnline text entries from Cooperative teams prompted by ESCALATESThroughout initiative
    Field ObservationVisits to learn about each Cooperative team, and understand the work they were doing (e.g., startup, recruitment, implementation activities, including observing facilitators work with practices)Fieldnotes, including
    observation of 41 facilitators
    with 54 unique practices
    August 2015 – March 2016;
    July 2016 – April 2017
    Semi-structured InterviewsInterviewed Cooperative leadership, members of partner organizations, and facilitators to explore start-up and implementation experiences39 interviews with Cooperative leadership and partners; 89 unique facilitator interviews; 66 interviews with practice membersThroughout the initiative
    Context AssessmentsCooperatives completed assessments to provide information about their local contexts, including team’s experience, regional attributes, experiences with recruitment and implementation of external supportTwo written assessments;
    Cooperative teams answered
    5 to 6 broad questions
    Recruitment and implementation phases
    • Abbreviations: ESCALATES, Evaluating System Change to Advance Learning and Take Evidence to Scale.

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    Table 4.

    Characteristics of the EvidenceNOW Cooperatives’ Participating Practices

    Experience LevelLowMediumHigh
    Cooperative2546371
    Number of Practices226251209211315263245
    Practice Characteristics, n (col %)
    Location1
     Rural10 (4.4)30 (12.0)*40 (19.1)46 (21.8)0 (0.0)72 (27.4)37 (15.1)*
     Large Town18 (8.0)5 (2.0)*54 (25.8)16 (7.6)0 (0.0)76 (28.9)33 (13.5)*
     Suburban3 (1.3)22 (8.8)*21 (10.0)13 (6.2)0 (0.0)20 (7.6)28 (11.4)*
     Urban Core195 (86.3)151 (60.2)*94 (45.0)136 (64.5)315 (98.4)90 (34.2)91 (37.1)*
    Practice Ownership
     Clinician owned84 (37.2)63 (25.1)*96 (45.9)72 (34.1)144 (45.7)*104 (39.5)93 (38.0)*
     Hospital/Health System59 (26.1)118 (47.0)*81 (38.8)31 (14.7)1 (0.3)*75 (28.5)30 (12.2)*
     Safety Net258 (25.7)25 (10.0)*32 (15.3)90 (42.7)16 (5.1)*71 (27.0)42 (17.1)*
     Other31 (0.4)2 (0.8)*0 (0.0)15 (7.1)1 (0.3)*8 (3.0)5 (2.0)*
    Practice Size
     Solo67 (29.6)13 (5.2)*19 (9.1)43 (20.4)101 (32.1)*78 (29.7)36 (14.7)*
     2 to 5 clinicians103 (45.6)123 (49.0)*105 (50.2)124 (58.8)34 (10.8)*137 (52.1)73 (29.8)*
     6 to 10 clinicians34 (15.0)33 (13.1)*36 (17.2)34 (16.1)13 (4.1)*29 (11.0)26 (10.6)*
     11 + clinicians22 (9.7)28 (11.2)*49 (23.4)4 (1.9)7 (2.2)*14 (5.3)35 (14.3)*
    Patient Characteristics
     ≥50% patients over 40 years old136 (60.2)129 (51.4)*119 (56.9)*0 (0.0)*113 (35.9)*160 (60.8)174 (71.0)*
     ≤50% patients classified as white91 (40.3)57 (22.7)*16 (7.7)*33 (15.6)87 (27.6)*54 (20.5)43 (17.6)*
     >30% Medicaid patients77 (34.1)13 (5.2)*45 (21.5)*78 (37.0)51 (16.2)*72 (27.4)15 (6.1)*
     >10% uninsured patients59 (26.1)41 (16.3)*27 (12.9)*68 (32.2)10 (3.2)*63 (24.0)43 (17.6)*
    • Notes: Percentages may not add up to 100% due to missing data. Variables with >15% missing data indicated with an.* 1Location designation determined using rural-urban commuting area codes. 2Safety net includes Federally Qualitied Health Centers, rural health clinics, Indian Health Services clinics, and other federally owned clinics. 3Other ownership includes nonfederal, private/non-clinician, and those indicating “other” without specifying an ownership type.

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    Table 5.

    Facilitation Outcomes, Cardiovascular Disease Preventive Services Delivery Performance, and Practice Capacity Outcomes at Baseline and Post-Intervention, Overall, and by Level of Cooperatives’ Experience

    Level of Cooperatives’ Experience
    OverallLowMediumHigh
    Mean (SD)Mean (SD)Mean (SD)Mean (SD)
    Hours of Facilitation18.1 (18.4)6.2 (5.1)18.9 (17.0)26.5 (21.9)
     Difference Between GroupsReference+12.7+20.3
    Months of Facilitation7.2 (3.5)4.0 (2.1)8.2 (3.2)8.4 (3.3)
     Difference Between GroupsReference+4.2+4.3
    Aspirin (%)
     Baseline63.9 (24.2)67.5 (23.9)69.6 (19.2)48.7 (26.5)
     Follow-up66.6 (22.8)69.4 (24.1)71.8 (17.2)53.5 (25.4)
     Difference from baseline to follow-up+2.7 (8.5)+1.9 (6.7)+2.2 (9.3)+4.8 (8.5)
    Blood pressure (%)
     Baseline64.7 (13.7)64.8 (15.6)66.1 (13.2)62.0 (12.1)
     Follow-up66.5 (13.6)67.2 (14.6)67.5 (13.4)64.1 (12.4)
     Difference from baseline to follow-up+1.8 (6.5)+2.4 (5.6)+1.4 (6.8)+2.1 (6.9)
    Cholesterol (%)
     Baseline61.9 (19.3)65.7 (19.8)66.4 (13.0)48.3 (21.3)
     Follow-up65.8 (17.7)68.2 (19.3)68.9 (12.5)56.8 (19.9)
     Difference from baseline to follow-up+3.9 (7.4)+2.5 (6.2)+2.5 (6.2)+8.5 (8.8)
    Smoking (%)
     Baseline60.0 (32.4)79.4 (21.2)61.2 (30.9)37.9 (30.8)
     Follow-up65.6 (30.8)82.0 (20.5)66.6 (29.2)47.0 (32.3)
     Difference from baseline to follow-up+5.6 (11.3)+2.6 (6.9)+5.4 (11.8)+9.1 (12.9)
    Adaptive Reserve (AR) (score)
     Baseline0.703 (0.118)0.678 (0.118)0.710 (0.117)0.715 (0.117)
     Follow-up0.721 (0.124)0.705 (0.129)0.734 (0.115)0.719 (0.128)
     Difference from baseline to follow-up+0.018 (0.117)+0.027 (0.122)+0.024 (0.112)+0.004 (0.118)
    CPCQ (score)
     Baseline8.8 (12.6)10.7 (12.1)7.9 (12.7)8.3 (12.7)
     Follow-up14.9 (9.3)15.7 (10.2)14.0 (8.8)15.3 (9.1)
     Difference from baseline to follow-up+6.1 (13.8)+5.0 (14.5)+6.1 (13.4)+7.0 (13.7)
    • Notes: The table shows mean baseline and post-intervention levels, and standard deviations of ABCS, AR, and CPCQ as well as levels of hours and months of facilitation during the intervention for all practices in the sample as well as stratified by Cooperative level of experience. For clinical measures, preliminary data quality assessment revealed large increases or decreases for some practices. To eliminate the influence of such outliers, we excluded practices with outcome change below the 5th percentile or above the 95th percentile from all our analysis. For overall pre-post ABCS, AR, and CPCQ changes, bold denotes statistical significance at the 5% level.

    • Sources: EvidenceNOW EHR records.

    • Abbreviations: SD, standard deviation; CPCQ, Change Process Capacity Questionnaire; ABCS, Aspirin, Blood Pressure, Cholesterol, and Smoking; EHR, electronic health record.

    • View popup
    Table 6.

    Changes in Outcomes from Baseline to Follow-up by Level of Cooperative Experience

    Level of Cooperatives’ experience
    LowMediumHigh
    Coef. (p-Value)Coef. (p-Value)Coef. (p-Value)
    Hours of Facilitation
     Unadjusted: Difference Between GroupsReference+12.68 (0.180)+20.33 (0.039)
     Adjusted: Difference Between GroupsReference+13.96 (0.205)+24.12 (0.022)
    Months of Facilitation
     Unadjusted: Difference Between GroupsReference+4.20 (0.036)+4.33 (0.002)
     Adjusted: Difference Between GroupsReference+4.37 (0.029)+4.86 (<0.001)
    Aspirin (%)
     UnadjustedReference+0.33 (0.885)+2.89 (0.081)
     Adjusted: Full SampleReference−0.29 (0.897)+2.14 (0.179)
     Adjusted: Practices below the median at baselineReference+2.59 (0.604)+2.05 (0.683)
     Adjusted: Practices above the median at baselineReference−1.67 (0.054)−0.16 (0.848)
    Blood pressure (%)
     UnadjustedReference−0.99 (0.270)−0.19 (0.852)
     Adjusted: Full SampleReference−0.87 (0.560)+0.64 (0.582)
     Adjusted: Practices below the median at baselineReference−1.02 (0.684)+0.61 (0.791)
     Adjusted: Practices above the median at baselineReference−0.96 (0.155)+0.02 (0.978)
    Cholesterol (%)
     UnadjustedReference−0.02 (0.991)+5.99 (0.014)
     Adjusted: Full SampleReference+0.40 (0.842)+4.59 (0.142)
     Adjusted: Practices below the median at baselineReference+1.85 (0.682)+3.33 (0.527)
     Adjusted: Practices above the median at baselineReference−1.07 (0.570)+2.78 (0.069)
    Smoking (%)
     UnadjustedReference+2.73 (0.277)+6.43 (0.003)
     Adjusted: Full SampleReference+3.33 (0.245)+7.07 (0.007)
     Adjusted: Practices below the median at baselineReference+5.16 (0.462)+3.97 (0.524)
     Adjusted: Practices above the median at baselineReference−0.16 (0.953)+5.55 (0.019)
    Adaptive reserve (score)
     UnadjustedReference+0.00 (0.898)−0.02 (0.267)
     Adjusted: Full SampleReference+0.00 (0.907)−0.02 (0.260)
     Adjusted: Practices below the median at baselineReference+0.021 (0.356)−0.016 (0.513)
     Adjusted: Practices above the median at baselineReference−0.006 (0.812)−0.017 (0.286)
    CPCQ (score)
     UnadjustedReference+1.18 (0.367)+2.04 (<0.001)
     Adjusted: Full SampleReference+1.93 (0.248)+1.32 (0.346)
     Adjusted: Practices below the median at baselineReference−2.23 (0.493)−2.29 (0.317)
     Adjusted: Practices above the median at baselineReference+1.15 (0.589)+2.92 (0.092)
    • Notes: The table shows unadjusted and adjusted regression estimates and p-values (in parentheses) of differences in outcome changes between Cooperative groups. Practices from Cooperatives with low experience are the reference group. Adjusted estimates are based on regressions that include practice characteristics (practice location; practice ownership; practice size; practice patient characteristics). Bold denotes statistical significance at the 5% level. All standard errors are clustered at the Cooperative level using bootstrapping.

    • Sources: EvidenceNOW EHR records and practice survey.

    • Abbreviations: EHR, electronic health record; CPCQ, Change Process Capacity Questionnaire.

    • View popup
    Appendix Table 1.

    Description of Practice Level Covariates

    MeasureDescription
    Practice locationWe used information given on zip code to classify practice locations as either Rural, Large Town, Suburban, or Urban Core, based on Rural-Urban Commuting Areas (RUCA) using 2010 Census data.
    Practice ownershipThe survey question was “Which of the following best describes your practice’s ownership? (Check all that apply)” The following categories were possible responses: clinician-owned solo or group practice; hospital/health system owned; Health Maintenance Organization (e.g., Kaiser Permanente); Federally Qualified Health Center or look-alike; non-federal government clinic (e.g., state, county, city, public health clinic, etc.); academic health center / faculty practice; federal (military, Veterans Administration, Department of Defense); Rural Health Clinic; Indian Health Service; other (please specify). Based on a hierarchical logic using these responses, including the other-specify response, and in some cases soliciting additional information from cooperatives, we recoded each practice into one of the following: Clinician-owned, Hospital/Health System, Safety Net (including FQHCs, academic health centers, federal, RHS, and IHS) or Other.
    Practice sizeThe survey asked respondents to choose which of the following best describes their practice’s size: solo practice, 2 to 5 clinicians, 6 to 10 clinicians, 11 to 15 clinicians, or 16 or more clinicians. We recoded the 11 to 15 clinicians and 16 or more clinicians’ categories into one, 11 + clinicians.
    ≥50% patients over 40 years oldThe survey asked for the percent of patients at the practice who fell into the following age categories: 0 to 17, 18 to 39, 40 to 59, 60 to 75, and 76 and over. We grouped percentages for the latter three groups into one 40 + percentage, then recoded this summed value into an indicator variable for whether this percentage was at least 50%.
    ≤50% white patientsThe survey asked for the percent of patients at the practice who were white, and we recoded this percent into an indicator variable for whether this percentage was at least 50%.
    >30% Medicaid patientsThe survey asked for the percent of patients at the practice receiving Medicaid, including those eligible for both Medicaid and Medicare, and we recoded this percent into an indicator variable for whether this percentage was above 30%, representing a high proportion of Medicaid beneficiaries.
    >10% uninsured patientsThe survey asked for the percent of patients at the practice who had no insurance, and we recoded this percent into an indicator variable for whether this percentage was above 10%, representing a high proportion of uninsured patients.
    Number of hoursTotal number of facilitation hours
    Number of encountersTotal number of facilitation encounters
    Months with encounterNumber of months with a facilitation encounter
    • Abbreviations: FQHCs, Federally qualified health centers; RHS, Rural health services; IHS, Rural health services.

    • View popup
    Appendix Table 2.

    Adaptive Reserve (AR) Questionnaire

    Please rate your level of agreement with the following statements about your practice
    Strongly disagree. . . . . . . . 1
    Disagree. . . . . . . . . . . . . . . 2
    Neutral. . . . . . . . . . . . . . . . 3
    Agree. . . . . . . . . . . . . . . . . .4
    Strongly agree. . . . . . . . . . .5
    AR1Mistakes have led to positive changes here
    AR2I have many opportunities to grow in my work
    AR3People in our practice actively seek new ways to improve how we do things
    AR4People at all levels in this office openly talk about what is and isn’t working
    AR5Leadership strongly supports practice change efforts
    AR6After trying something new, we take time to think about how it worked
    AR7Most of the people who work in our practice seem to enjoy their work
    AR8It is hard to get things to change in our practice
    AR9This practice is a place of joy and hope
    AR10This practice learns from its mistakes
    AR11Practice leadership promotes an environment that is an enjoyable place to work
    AR12People in this practice operate as a real team
    AR13When we experience a problem in the practice, we make a serious effort to figure out what’s really going on
    AR14Leadership in this practice creates an environment where things can be accomplished
    • Question 8 was reverse-coded, then responses were rescaled to range from 0 to 1 and averaged to produce the practice-level AR score, ranging from 0 to 1.

    • View popup
    Appendix Table 3.

    Change Process Capacity Questionnaire (CPCQ)

    Indicate the extent to which you agree or disagree that your practice has used the following strategies to improve cardiovascular preventive care
    Strongly disagree. . . . . . . . 1
    Disagree. . . . . . . . . . . . . . . 2
    Neutral. . . . . . . . . . . . . . . . 3
    Agree. . . . . . . . . . . . . . . . . .4
    Strongly agree. . . . . . . . . . .5
    CPCQ1Providing information and skills-training
    CPCQ2Using opinion leaders, role modeling, or other vehicles to encourage support for changes
    CPCQ3Changing or creating systems in the practice that make it easier to provide high quality care
    CPCQ4Removal or reduction of barriers to better quality of care
    CPCQ5Using teams focused on accomplishing the change process for improved care
    CPCQ6Delegating to non-clinician staff the responsibility to carry out aspects of care that are normally the responsibility of physicians
    CPCQ7Providing to those who are charged with implementing improved care the power to authorize and make the desired changes
    CPCQ8Period measurement of care quality for assessing compliance with any new approach to care
    CPCQ9Reporting measurements of practice performance on cardiovascular disease prevention measures (such as aspirin for patients at risk for ischemic vascular disease) for comparison with their peers
    CPCQ10Setting goals and benchmarking rates of performance quality on cardiovascular disease prevention measures at least yearly
    CPCQ11Customizing the implementation of cardiovascular disease prevention care changes to the practice
    CPCQ12Using rapid cycling, piloting, pre-testing, or other vehicles for reducing the risk of negative results for introducing organization-wide change in care
    CPCQ13Deliberately designing care improvements so as to make clinician participation less work than before
    CPCQ14Deliberately designing care improvements to make the care process more beneficial to the patient
    • Reponses were rescaled to range from −2 to 2 and summed to produce the practice-level CPCQ score, ranging from −28 to 28.

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The Journal of the American Board of Family     Medicine: 38 (1)
The Journal of the American Board of Family Medicine
Vol. 38, Issue 1
January-February 2025
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How Does Prior Experience Pay Off in Large-Scale Quality Improvement Initiatives?
Deborah J. Cohen, Bijal A. Balasubramanian, Stephan Lindner, William L. Miller, Shannon M. Sweeney, Jennifer D. Hall, Rikki Ward, Miguel Marino, Rachel Springer, K. John McConnell, Jennifer R. Hemler, Sarah S. Ono, David Ezekiel-Herrera, Andrea Baron, Benjamin F. Crabtree, Leif I. Solberg
The Journal of the American Board of Family Medicine Sep 2022, jabfm.2022.AP.220088; DOI: 10.3122/jabfm.2022.AP.220088

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How Does Prior Experience Pay Off in Large-Scale Quality Improvement Initiatives?
Deborah J. Cohen, Bijal A. Balasubramanian, Stephan Lindner, William L. Miller, Shannon M. Sweeney, Jennifer D. Hall, Rikki Ward, Miguel Marino, Rachel Springer, K. John McConnell, Jennifer R. Hemler, Sarah S. Ono, David Ezekiel-Herrera, Andrea Baron, Benjamin F. Crabtree, Leif I. Solberg
The Journal of the American Board of Family Medicine Sep 2022, jabfm.2022.AP.220088; DOI: 10.3122/jabfm.2022.AP.220088
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