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Research ArticleOriginal Research

Who Is Most Burdened in Health Care? An Analysis of Responses to the ICAN Discussion Aid

Kyle G. Steiger, Kasey R. Boehmer, Molly C. Klanderman, Aamena Mookadam, Sethu Sandeep Koneru, Victor M. Montori and Martina Mookadam
The Journal of the American Board of Family Medicine March 2023, jabfm.2022.220251R1; DOI: https://doi.org/10.3122/jabfm.2022.220251R1
Kyle G. Steiger
From the Mayo Clinic Alix School of Medicine, Mayo Clinic College of Medicine and Science, Scottsdale (KGS); Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, Minnesota (KRB, VMM); Division of Health Care Delivery Research, Mayo Clinic, Rochester, Minnesota (KRB); Department of Quantitative Health Sciences, Mayo Clinic, Scottsdale, Arizona (MCK); Mayo Clinic, Scottsdale, Arizona (AM, SSK); Department of Family Medicine, Mayo Clinic, Scottsdale, Arizona (MM); Arizona State University, Tempe (AM).
BA
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Kasey R. Boehmer
From the Mayo Clinic Alix School of Medicine, Mayo Clinic College of Medicine and Science, Scottsdale (KGS); Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, Minnesota (KRB, VMM); Division of Health Care Delivery Research, Mayo Clinic, Rochester, Minnesota (KRB); Department of Quantitative Health Sciences, Mayo Clinic, Scottsdale, Arizona (MCK); Mayo Clinic, Scottsdale, Arizona (AM, SSK); Department of Family Medicine, Mayo Clinic, Scottsdale, Arizona (MM); Arizona State University, Tempe (AM).
PhD, MPH
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Molly C. Klanderman
From the Mayo Clinic Alix School of Medicine, Mayo Clinic College of Medicine and Science, Scottsdale (KGS); Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, Minnesota (KRB, VMM); Division of Health Care Delivery Research, Mayo Clinic, Rochester, Minnesota (KRB); Department of Quantitative Health Sciences, Mayo Clinic, Scottsdale, Arizona (MCK); Mayo Clinic, Scottsdale, Arizona (AM, SSK); Department of Family Medicine, Mayo Clinic, Scottsdale, Arizona (MM); Arizona State University, Tempe (AM).
PhD, MS
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Aamena Mookadam
From the Mayo Clinic Alix School of Medicine, Mayo Clinic College of Medicine and Science, Scottsdale (KGS); Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, Minnesota (KRB, VMM); Division of Health Care Delivery Research, Mayo Clinic, Rochester, Minnesota (KRB); Department of Quantitative Health Sciences, Mayo Clinic, Scottsdale, Arizona (MCK); Mayo Clinic, Scottsdale, Arizona (AM, SSK); Department of Family Medicine, Mayo Clinic, Scottsdale, Arizona (MM); Arizona State University, Tempe (AM).
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Sethu Sandeep Koneru
From the Mayo Clinic Alix School of Medicine, Mayo Clinic College of Medicine and Science, Scottsdale (KGS); Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, Minnesota (KRB, VMM); Division of Health Care Delivery Research, Mayo Clinic, Rochester, Minnesota (KRB); Department of Quantitative Health Sciences, Mayo Clinic, Scottsdale, Arizona (MCK); Mayo Clinic, Scottsdale, Arizona (AM, SSK); Department of Family Medicine, Mayo Clinic, Scottsdale, Arizona (MM); Arizona State University, Tempe (AM).
MBBS
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Victor M. Montori
From the Mayo Clinic Alix School of Medicine, Mayo Clinic College of Medicine and Science, Scottsdale (KGS); Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, Minnesota (KRB, VMM); Division of Health Care Delivery Research, Mayo Clinic, Rochester, Minnesota (KRB); Department of Quantitative Health Sciences, Mayo Clinic, Scottsdale, Arizona (MCK); Mayo Clinic, Scottsdale, Arizona (AM, SSK); Department of Family Medicine, Mayo Clinic, Scottsdale, Arizona (MM); Arizona State University, Tempe (AM).
MD
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Martina Mookadam
From the Mayo Clinic Alix School of Medicine, Mayo Clinic College of Medicine and Science, Scottsdale (KGS); Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, Minnesota (KRB, VMM); Division of Health Care Delivery Research, Mayo Clinic, Rochester, Minnesota (KRB); Department of Quantitative Health Sciences, Mayo Clinic, Scottsdale, Arizona (MCK); Mayo Clinic, Scottsdale, Arizona (AM, SSK); Department of Family Medicine, Mayo Clinic, Scottsdale, Arizona (MM); Arizona State University, Tempe (AM).
MD
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  • Article
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Article Figures & Data

Figures

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  • Figure 1.
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    Figure 1.

    ICAN Discussion Aid. Inside bifold of flyer lists selections for sources of satisfaction and burdens. Labs indicates laboratory tests. (Used with permission of Mayo Foundation for Medical Education and Research)

  • Figure 2.
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    Figure 2.

    Final regression tree model for the number of overall patient burdens. Values in each leaf are as follows: top value, mean number of burdens; center values, total number of burdens reported/total number of patients (observations); and bottom value, percentage of all observations. Abbreviations: BMI, body mass index; MDD, major depressive disorder. (Used with permission of Mayo Foundation for Medical Education and Research)

  • Figure 3.
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    Figure 3.

    Final regression tree model for the number of personal burdens. Values in each leaf are as follows: top value, mean number of burdens; center values, total number of burdens reported/total number of patients (observations); and bottom value, percentage of all observations. Abbreviations: BMI, body mass index; GAD, generalized anxiety disorder; MDD, major depressive disorder. (Used with permission of Mayo Foundation for Medical Education and Research)

  • Appendix Figure 1.
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    Appendix Figure 1.

    Instrument for Patient Capacity Assessment (ICAN) discussion aid. Outside bifold of flyer lists open-ended questions. (Used with permission of Mayo Foundation for Medical Education and Research)

  • Appendix Figure 2.
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    Appendix Figure 2.

    Histogram of number of patient burdens. (Used with permission of Mayo Foundation for Medical Education and Research)

  • Appendix Figure 3.
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    Appendix Figure 3.

    Average Root Mean Squared Error (RMSE) across the complexity parameter for the total number of burdens. (Used with permission of Mayo Foundation for Medical Education and Research)

  • Appendix Figure 4.
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    Appendix Figure 4.

    Variable importance plot for the regression tree modeling the number of overall burdens. Abbreviations: BMI indicates body mass index; HLD, hyperlipidemia; HTN, hypertension; MDD, major depressive disorder; OTC, over-the-counter; RX, prescription. (Used with permission of Mayo Foundation for Medical Education and Research)

  • Appendix Figure 5.
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    Appendix Figure 5.

    Variable importance plot for the regression tree modeling the number of burdens in the patient's personal life. Abbreviations: BMI indicates body mass index; GAD, generalized anxiety disorder; HLD, hyperlipidemia; HTN, hypertension; MDD, major depressive disorder; OSA, obstructive sleep apnea; OTC, over-the-counter; RX, prescription. (Used with permission of Mayo Foundation for Medical Education and Research)

  • Appendix Figure 6.
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    Appendix Figure 6.

    Final regression tree model for the number of health care burdens. Values in each leaf are as follows: Top row, Mean No. of burdens; Center, Total No. of burdens reported/total No. of observations (patients); and Bottom, Percentage of all observations. Abbreviations: BMI indicates body mass index. (Used with permission of Mayo Foundation for Medical Education and Research)

  • Appendix Figure 7.
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    Appendix Figure 7.

    Variable importance plot for the regression tree modeling the number of health care burdens. Abbreviations: BMI indicates body mass index; HLD, hyperlipidemia; HTN, hypertension; OSA, obstructive sleep apnea; RX, prescription. (Used with permission of Mayo Foundation for Medical Education and Research)

Tables

  • Figures
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    Table 1.

    Patient and Clinical Characteristics

    Number (%) of Patients (n = 635)*
    Age, y
    Mean (SD)60.9 (15.6)
    Range18.0 to 95.0
    Gender
    Men182 (28.7)
    Women453 (71.3)
    BMI†
    Mean (SD)27.8 (6.0)
    Range14.9 to 49.0
    Chronic health conditions‡
    CAD56 (8.8)
    Cancer35 (5.5)
    CHF35 (5.5)
    GAD153 (24.1)
    Hyperlipidemia318 (50.1)
    Hypertension312 (49.1)
    MDD137 (21.6)
    OSA103 (16.2)
    Prediabetes66 (10.4)
    Type 2 diabetes68 (10.7)
    OTC medications, number
    Mean (SD)3.2 (2.5)
    Range0.0 to 15.0
    Rx medications, number
    Mean (SD)4.5 (3.5)
    Range0.0 to 19.0
    Medications, type
    Anticoagulant50 (7.9)
    Antidepressant118 (18.6)
    Antihypertensive280 (44.1)
    Anxiolytic80 (12.6)
    BPH medicine19 (3.0)
    GERD medicine149 (23.5)
    Insulin25 (3.9)
    Opioid51 (8.0)
    Oral hypoglycemic59 (9.3)
    Sleep aid76 (12.0)
    Statin211 (33.2)
    • ↵* Unless indicated otherwise.

    • ↵† Calculated as weight in kilograms divided by height in meters squared.

    • ↵‡ Some patients had more than 1 chronic condition.

    • Abbreviations: BMI, body mass index; BPH, benign prostatic hyperplasia; CAD, coronary artery disease; CHF, congestive heart failure; GAD, generalized anxiety disorder; GERD, gastroesophageal reflux disease; MDD, major depressive disorder; OSA, obstructive sleep apnea; OTC, over-the-counter; Rx, prescription; SD, standard deviation.

    • View popup
    Table 2.

    Patient Self-Assessed Burdens Using the ICAN Discussion Aid

    Number (%) of Patients (n = 635)*
    Burden or Both Burden and SatisfactionSatisfaction or Help
    Family and friends84 (13.2)546 (86.0)
    Work or finances97 (15.2)503 (79.2)
    Free time, relaxation, fun41 (6.4)580 (91.3)
    Spirituality and life purpose25 (3.9)567 (89.3)
    Where I live43 (6.7)582 (91.7)
    Getting out and transportation57 (9.0)568 (89.4)
    Being active102 (16.1)523 (82.4)
    Social media, TV or screen watching71 (11.1)539 (84.9)
    Emotional life99 (15.6)518 (81.6)
    Memory or attention104 (16.4)514 (80.9)
    The food I eat95 (15.0)531 (83.6)
    Take medication34 (5.3)510 (80.3)
    Monitor symptoms31 (4.9)452 (71.2)
    Manage diet and exercise98 (15.4)444 (69.9)
    Get enough sleep100 (15.8)428 (67.4)
    Come in for appointments or labs40 (6.3)512 (80.6)
    Reduce alcohol use, smoking, and so forth11 (1.7)320 (50.4)
    Insurance or support services44 (6.9)379 (59.7)
    Manage stress55 (8.7)401 (63.1)
    • ↵* Unless indicated otherwise. Percentages may not total to 100% because some responses were left blank or were unclear.

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The Journal of the American Board of Family     Medicine: 37 (6)
The Journal of the American Board of Family Medicine
Vol. 37, Issue 6
November-December 2024
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Who Is Most Burdened in Health Care? An Analysis of Responses to the ICAN Discussion Aid
Kyle G. Steiger, Kasey R. Boehmer, Molly C. Klanderman, Aamena Mookadam, Sethu Sandeep Koneru, Victor M. Montori, Martina Mookadam
The Journal of the American Board of Family Medicine Mar 2023, jabfm.2022.220251R1; DOI: 10.3122/jabfm.2022.220251R1

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Who Is Most Burdened in Health Care? An Analysis of Responses to the ICAN Discussion Aid
Kyle G. Steiger, Kasey R. Boehmer, Molly C. Klanderman, Aamena Mookadam, Sethu Sandeep Koneru, Victor M. Montori, Martina Mookadam
The Journal of the American Board of Family Medicine Mar 2023, jabfm.2022.220251R1; DOI: 10.3122/jabfm.2022.220251R1
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