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Research ArticleOriginal Research

An Innovative Community-based Model for Improving Preventive Care in Rural Counties

Zsolt J. Nagykaldi, Dewey Scheid, Daniel Zhao, Bhawani Mishra and Tracy Greever-Rice
The Journal of the American Board of Family Medicine September 2017, 30 (5) 583-591; DOI: https://doi.org/10.3122/jabfm.2017.05.170035
Zsolt J. Nagykaldi
From the College of Medicine, Department of Family and Preventive Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK (ZJN, DS); College of Public Health, Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City (DZ); Division of Applied Social Sciences, Office of Social and Economic Data Analysis, University of Missouri, Columbia, MO (BM, TGR)
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Dewey Scheid
From the College of Medicine, Department of Family and Preventive Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK (ZJN, DS); College of Public Health, Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City (DZ); Division of Applied Social Sciences, Office of Social and Economic Data Analysis, University of Missouri, Columbia, MO (BM, TGR)
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Daniel Zhao
From the College of Medicine, Department of Family and Preventive Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK (ZJN, DS); College of Public Health, Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City (DZ); Division of Applied Social Sciences, Office of Social and Economic Data Analysis, University of Missouri, Columbia, MO (BM, TGR)
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Bhawani Mishra
From the College of Medicine, Department of Family and Preventive Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK (ZJN, DS); College of Public Health, Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City (DZ); Division of Applied Social Sciences, Office of Social and Economic Data Analysis, University of Missouri, Columbia, MO (BM, TGR)
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Tracy Greever-Rice
From the College of Medicine, Department of Family and Preventive Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK (ZJN, DS); College of Public Health, Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City (DZ); Division of Applied Social Sciences, Office of Social and Economic Data Analysis, University of Missouri, Columbia, MO (BM, TGR)
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    Figure 1.

    The “Healthier Together” Project County Partnership. CHIO, County Health Improvement Organization; HIE, Health Information Exchange; PCP, Primary Care Physician/Provider; PEA, Practice Enhancement Assistant; PSRS, Preventive Services Reminder System; Pts, Patients; WC, Wellness Coordinator.

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    Table 1.

    Demographics of the Patient Population Reached by Oklahoma County Wellness Coordinators During the Intervention Period

    Demographic ParameterOutreach Effort OutcomeCounty Population Reference
    Number of Individuals9,138∼15,000
    Age (years), mean (SD)38 (21)Mean: 38
    Sex (female)50.5%52.1%
    Race/ethnicityInsufficient data available in health records84% White
    12.4% Hispanic/Latino
    Spatial distribution of population (by town)
        Town A62%30%
        Town B8%15%
        Town C7%3%
    Median household incomeData not available in health records$37,000
    • View popup
    Table 2.

    Rates of Delivering Preventive Services Before and After Outreach Program Implementation, Grouped by Service Type and Care Delivery Domain

    Preventive Service TypeBaseline Service Rate*Intervention Service Rate*PShare of Total ROI†
    Primary care practice domain
        Smoking cessation counseling33%71%<.0114%
        Adult immunizations (influenza and pneumococcal vaccine)63%78%<.053%
        Diabetes management (diabetes checkup visits & HbA1c measurement)48%75%<.0118%
        Well child visits51%60%<.0513%
        Physical activity counseling27%38%<.0114%
        All practice-based services44%64%<.0162%
    County hospital and health system domain
        Colonoscopy (referred)38%43%.07‡31%
        Mammography (referred)55%63%<.056%
        Bone density screening (referred)24%30%<.051%
        All referred services39%45%.0538%
    County healthcare domain
        All services combined42%57%<.01100%
    • ↵* Service rates were measured by combining HIE record analyses and medical record abstractions. HIE records and health system-level service reports were essential to estimate the rate of referred services (mammography, colonoscopy, and bone density screening).

    • ↵† Total ROI includes all returns generated across the project, including all organizations and services.

    • ↵‡ Although this trend did not reach statistical significance across the county, there was an increase in the absolute number of colonoscopies that were referred to the health system that employed the wellness coordinator during the intervention period compared to the baseline period. Most of these patients received a call from the wellness coordinator.

    • View popup
    Table 3.

    Types, Frequency and Consequences of Problems with the Availability or Quality of Health Information and Solutions Implemented in the Healthier Together Pilot Study

    HIE Data Quality Problem TypeFrequency of Data Quality Problem in HIE RecordsConsequences of Data Quality Problem & ▸Mitigation Strategies
    Patient is not attributed to a PCP (no PCP listed)Decreased from 94% to 80% over the pilotServices & quality metrics can't be linked to PCP ▸Additional patient matching using EHR data
    Low actionable* data contribution from PCPsDecreased from 89% to 66% over the pilotLess information on PCP services ▸ Improving data interfaces & manual extractions from EHRs
    Missing or wrong patient phone numbersDecreased from 49% to 38% over the pilotPatient reach barriers ▸ Better documentation & additional data extraction from billing records
    Risk factor rate lower or higher than expected†Smoker (5% vs. 16%‡)Inaccurate care gap predictions ▸ Better chart documentation and calibrating data interfaces
    Diabetic (21% vs. 10%‡)
    Preventive service rates are lower than expected†Mammography (5% vs. 11%‡; increased to 13% over time)Inaccurate care gap predictions ▸ Better chart documentation and calibrating data interfaces
    Race or ethnicity information not availableAbout 98% to 99% of records (remained unchanged)Less tailored care recommendations ▸ Improve documentation of race in patient chart
    Skewed data contribution among organizations30% of HIE records are over-concentrated in SE of countySome organizations dominate as data source ▸ Oversample records in northern county region
    • EHR, Electronic Health Record; HIE, Health Information Exchange (patient records aggregated regionally); PCP, primary care practice.

    • ↵* Actionable data include health risk factors (e.g., smoking status), reports, and laboratory findings pertinent to prevention, and history of preventive services. Low-value data include administrative visit information and free text notes that often “bloat” interoperable records causing excessive transmission and processing times.

    • ↵† Rate means the frequency of the occurrence of health risk factors or preventive services in HIE datasets relative to the known prevalence of these factors in the population.

    • ↵‡ State of the State's Health Report 2014, Oklahoma State Department of Health.

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The Journal of the American Board of Family     Medicine: 30 (5)
The Journal of the American Board of Family Medicine
Vol. 30, Issue 5
September-October 2017
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An Innovative Community-based Model for Improving Preventive Care in Rural Counties
Zsolt J. Nagykaldi, Dewey Scheid, Daniel Zhao, Bhawani Mishra, Tracy Greever-Rice
The Journal of the American Board of Family Medicine Sep 2017, 30 (5) 583-591; DOI: 10.3122/jabfm.2017.05.170035

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An Innovative Community-based Model for Improving Preventive Care in Rural Counties
Zsolt J. Nagykaldi, Dewey Scheid, Daniel Zhao, Bhawani Mishra, Tracy Greever-Rice
The Journal of the American Board of Family Medicine Sep 2017, 30 (5) 583-591; DOI: 10.3122/jabfm.2017.05.170035
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