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Research ArticleOriginal Research

Use of Clinical Decision Support to Improve Primary Care Identification and Management of Chronic Kidney Disease (CKD)

Cara B. Litvin, J. Madison Hyer and Steven M. Ornstein
The Journal of the American Board of Family Medicine September 2016, 29 (5) 604-612; DOI: https://doi.org/10.3122/jabfm.2016.05.160020
Cara B. Litvin
From the Division of General Internal Medicine and Geriatrics, Department of Medicine, Medical University of South Carolina, South Carolina (CBL); the Department of Public Health Sciences, Medical University of South Carolina (JMH); and the Department of Family Medicine, Medical University of South Carolina (SMO).
MD, MS
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J. Madison Hyer
From the Division of General Internal Medicine and Geriatrics, Department of Medicine, Medical University of South Carolina, South Carolina (CBL); the Department of Public Health Sciences, Medical University of South Carolina (JMH); and the Department of Family Medicine, Medical University of South Carolina (SMO).
MS
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Steven M. Ornstein
From the Division of General Internal Medicine and Geriatrics, Department of Medicine, Medical University of South Carolina, South Carolina (CBL); the Department of Public Health Sciences, Medical University of South Carolina (JMH); and the Department of Family Medicine, Medical University of South Carolina (SMO).
MD
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    Figure 1.

    Chronic kidney disease (CKD) risk assessment tool. ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; BP, blood pressure; eGFR, estimated glomerular filtration rate; Hgb, hemoglobin; LDL, low-density lipoprotein.

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    Figure 2.

    Chronic kidney disease flowchart.

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    Figure 3.

    Chronic kidney disease patient registry.

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    Table 1.

    Characteristics of Participating Practices

    StateSpecialtyProvidersClinical Staff MembersPatients per Clinician (Mean)
    1WIFamily medicine2 MDs1 RN, 3 MAs933
    2TNFamily medicine2 MDs2 LPNs, 2 MAs1299
    3COInternal medicine1 MD, 1 NP, 1 PA1 RN, 2 MAs2271 (assigned to MD only)
    4WAFamily medicine1 MD1 RN521
    5NJFamily medicine1 MD0636
    6OHInternal medicine3 MDs1 RN, 3 LPNs, 2 MAs886
    7PAFamily medicine4 MDs, 1 NP, 3 PAs5 RN, 11 LPN, 4 MAs723 per MD; 884 per midlevel provider
    8CTFamily medicine4 MDs4 MAs1041
    9AZFamily medicine2 MDs, 1 PA3 MAs2032 per MD; 959 per PA
    10CAFamily medicine3 MDs 3 PAs7 MAs1831 per MD; 147 per PA
    11MIFamily medicine1 MD4 MAs1629
    12MDFamily medicine1 MD, 4 NPs, 1 PA4 MAs3061 per MD; 254 per midlevel provider
    • LPN, licensed practical nurse; MA, medical assistant; MD, medical or osteopathic doctor; NP, nurse practitioner; PA, physician assistant; RN, registered nurse.

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    Table 2.

    Performance on Chronic Kidney Disease Clinical Quality Measures over the 24-Month Intervention

    CKD Clinical Quality MeasureBaseline24 MonthsChange from Baseline to Month 24 (%M24−BL)
    %BLNBL%M24NM24
    Identification of patients with CKD
        eGFR in the past year for patients with DM and/or hypertension87 (80, 93)929 (590, 1303)87.5 (82, 92)795 (608, 1256)0.5 (−2.0, 5.0)
        Screening for albuminuria in the past year for patients with DM and/or hypertension21.5 (16, 26)929.5 (590, 1303)59 (37, 73)795 (608, 1256)30.0 (23.0, 46.0)*
    Monitoring patients with CKD
        eGFR in the past 6 months for patients with stage 3 CKD76 (72, 86)169.5 (122, 279)80.5 (76, 83)205.5 (149, 263)0.5 (−3.0, 3.0)
        eGFR in the past 3 months for patients with stage 4 CKD75 (63, 80)10.5 (5, 16)74 (63, 80)11.5 (7, 15)−3.0 (−7.0, 0.0)
        Monitoring albuminuria in past year for patients with CKD (without prior macroalbuminuria or proteinuria)34.5 (27, 55)190.5 (131, 356)63 (53, 83)325 (201, 420)25.0 (22.0, 31.0)*
    BP management in patients with CKD
        Most recent BP <140/90 mmHg for patients with CKD without macroalbuminuria or proteinuria76 (68, 83)191.5 (131, 357)76.5 (73, 82)325.5 (201, 422)2.5 (0.0, 7.0)
        Most recent BP <130/80 mmHg for patients with CKD with macroalbuminuria or proteinuria33 (30, 57)2.5 (1, 7)29 (13, 33)8.5 (3, 15)−1.5 (−50.5, 9.0)
        ACEI or ARB in the past year for patients with CKD and hypertension with macroalbuminuria or proteinuria53.5 (43, 67)2.5 (1, 7)65.5 (52, 100)8.5 (3, 15)23.5 (−5.5, 43.5)
    Dyslipidemia in patients with CKD
        Lipid panel in the past year for patients with CKD84.5 (82, 87)202.5 (131, 367)84 (82, 88)340.5 (209, 441)−0.5 (−4.0, 1.0)
    Anemia in patients with CKD
        Hemoglobin in the past year for patients with eGFR <4577 (74, 82)60 (34, 110)86 (70, 91)69.5 (39, 91)7.0 (0.0, 8.0)
    Avoidance of potential nephrotoxic drugs
        Avoidance of NSAIDs or COX-2 inhibitors in patients with CKD94 (91, 96)202.5 (131, 367)92 (92, 94)340.5 (209, 441)−1.0 (−2.0, 1.0)
    • Data are median (25th percentile, 75th percentile).

    • ↵* P < .0005.

    • %BL, Proportion of testing at baseline; %M24, proportion of testing at month 24; %M24−BL, absolute change in proportion; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; CKD, chronic kidney disease; COX-2, cyclooxygenase 2; eGFR, estimated glomerular filtration rate; DM, diabetes mellitus; BP, blood pressure; NBL, number of patients eligible for testing at baseline; NM24, number of patients eligible for testing at month 24; NSAID, nonsteroidal anti-inflammatory drug.

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    Table 3.

    Reported Facilitators and Barriers to Use of Clinical Decision Support Tools to Improve Chronic Kidney Disease Management

    FacilitatorsBarriers
    Provider factors
    • ∙ CDS helps focus provider attention on CKD

    • ∙ Perception by users that CDS improves care

    • ∙ Disagreement about CKD guidelines

    • ∙ Confusion about CKD guidelines

    • ∙ Concerns about data validity

    • ∙ Patients comanaged by nephrologists

    • ∙ Lack of awareness of CDS tools

    Organizational factors
    • ∙ Practice-wide prioritization of identifying patients with CKD

    • ∙ In-office urine collection and/or albumin testing

    • ∙ Standing orders for laboratory tests

    • ∙ Patient registry used by staff for patient outreach

    • ∙ Staff turnover

    • ∙ Competing obligations (other incentive programs)

    • ∙ Failure to fully implement standing orders

    Technical factors
    • ∙ CDS tools customized to workflow of practices by research team during site visits

    • ∙ Research team able to troubleshoot CDS at site visits

    • ∙ Research team demonstrated use of patient registry during site visits

    • ∙ CDS tools required “extra clicks”

    • ∙ Risk assessment tool did not always work

    • ∙ Reports and tools did not capture labs ordered by specialists

    • ∙ Use of registry required re-identifying patients

    Patient factors
    • ∙ Patient education about CKD (including educational handouts)

    • ∙ Changing patients' expectations to adhere to urine testing

    • ∙ Concern about patients seeing diagnosis of CKD

    • ∙ Concern about overdiagnosis of CKD

    • CKD, chronic kidney disease; CDS, clinical decision support.

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The Journal of the American Board of Family     Medicine: 29 (5)
The Journal of the American Board of Family Medicine
Vol. 29, Issue 5
September-October 2016
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Use of Clinical Decision Support to Improve Primary Care Identification and Management of Chronic Kidney Disease (CKD)
Cara B. Litvin, J. Madison Hyer, Steven M. Ornstein
The Journal of the American Board of Family Medicine Sep 2016, 29 (5) 604-612; DOI: 10.3122/jabfm.2016.05.160020

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Use of Clinical Decision Support to Improve Primary Care Identification and Management of Chronic Kidney Disease (CKD)
Cara B. Litvin, J. Madison Hyer, Steven M. Ornstein
The Journal of the American Board of Family Medicine Sep 2016, 29 (5) 604-612; DOI: 10.3122/jabfm.2016.05.160020
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Keywords

  • Albuminuria
  • Angiotensin Receptor Antagonists
  • Angiotensin-Converting Enzyme Inhibitors
  • Chronic Kidney Disease
  • Clinical Decision Support Systems
  • Hemoglobins
  • Hypertension
  • Practice-based Research
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