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Research ArticleOriginal Research

Prevalence and Characterization of Yoga Mentions in the Electronic Health Record

Nadia M. Penrod, Selah Lynch, Sunil Thomas, Nithya Seshadri and Jason H. Moore
The Journal of the American Board of Family Medicine November 2019, 32 (6) 790-800; DOI: https://doi.org/10.3122/jabfm.2019.06.190115
Nadia M. Penrod
From the Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA (NMP, SL, JHM); Department of Biostatics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA (NMP, JHM); Clinical Research Informatics Core, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA (SL); Department of Information Services, Data Analytics Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA (ST, NS)
PhD
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Selah Lynch
From the Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA (NMP, SL, JHM); Department of Biostatics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA (NMP, JHM); Clinical Research Informatics Core, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA (SL); Department of Information Services, Data Analytics Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA (ST, NS)
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Sunil Thomas
From the Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA (NMP, SL, JHM); Department of Biostatics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA (NMP, JHM); Clinical Research Informatics Core, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA (SL); Department of Information Services, Data Analytics Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA (ST, NS)
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Nithya Seshadri
From the Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA (NMP, SL, JHM); Department of Biostatics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA (NMP, JHM); Clinical Research Informatics Core, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA (SL); Department of Information Services, Data Analytics Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA (ST, NS)
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Jason H. Moore
From the Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA (NMP, SL, JHM); Department of Biostatics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA (NMP, JHM); Clinical Research Informatics Core, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA (SL); Department of Information Services, Data Analytics Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA (ST, NS)
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Article Figures & Data

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    Figure 1.

    Documentation of yoga in the electronic health record (EHR) shows quadratic growth. Quadratic models fit to count data for the number of unique yoga note(s) per year, the number of unique patients with yoga notes, the number of unique clinicians writing yoga notes, and the number of unique clinical service departments containing yoga notes. Quadratic models were selected over linear models based on the Akaike Information Criterion.

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    Figure 2.

    Clinician-documented yoga notes and clinician-recommended yoga notes increase in proportion to the total number of notes mentioning yoga each year. Bar plots show (A) counts of unique clinical chart notes mentioning yoga by class each year and (B) the proportion of clinical chart notes mentioning yoga by class each year.

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    Figure 3.

    Clinician-recommended yoga notes are associated with 9 medical conditions. Odds ratios (OR) and P-values were calculated with Fisher's exact test using the counts of patients in the clinician-recommended yoga class for each medical condition (based on International Classification of Diseases (ICD) codes) and their controls, patients without a chart note mentioning yoga matched on age, sex, and race. Significance threshold for inclusion was a false discovery rate (FDR)-adjusted P-value ≤ .05.

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    Table 1.

    Demographic Characteristics

    CharacteristicPatients, No (%)Health System (n = 1,210,228)P-Value† (Control, Penn Med)
    Yoga Cohort (n = 30,976)Control Cohort (n = 92,919)
    Age, y
        19 to 4414,069 (45.4)42,158 (45.4)478,886 (39.6)
        45 to 6411,843 (38.2)35,554 (38.3)425,661 (35.2)
        65 to 844838 (15.6)14,535 (15.6)266,283 (22.0)
        85+226 (0.7)672 (0.7)39,398 (3.3)
    Median age (IQR), y47 (34 to 60)47 (34 to 60)51 (34 to 65)P = .996, < .001
    Sex
        Female25,688 (82.9)77,063 (82.9)721,180 (59.6)
        Male5288 (17.1)15,856 (17.1)489,048 (40.4)P = .984, < .001
    Race
        White23,259 (75.1)69,777 (75.1)785,163 (64.9)
        Black or African American3625 (11.7)10,874 (11.7)243,639 (20.1)
        Other*2744 (8.9)8231 (8.9)135,081 (11.2)
        Asian1289 (4.2)3867 (4.2)43,514 (3.6)
        American Indian or Alaskan Native35 (0.1)98 (0.1)1155 (0.1)
        Native Hawaiian or Other Pacific Islander24 (0.1)72 (0.1)1676 (0.1)P = 1.00, < .001
    Ethnicity
        Non-Hispanic30,189 (97.5)89,272 (96.1)1,167,368 (96.5)
        White, Hispanic513 (1.7)2913 (3.1)28,732 (2.4)
        Black or African American, Hispanic128 (0.4)338 (0.4)8414 (0.7)
        Other*, Hispanic74 (0.2)263 (0.3)3933 (0.3)
        Asian, Hispanic70 (0.2)129 (0.1)1658 (0.1)
        American Indian or Alaskan Native, Hispanic1 (0.0)4 (0.0)31 (0.0)
        Native Hawaiian or Other Pacific Islander, Hispanic1 (0.0)0 (0.0)92 (0.0)P < .001, < .001
    Financial
        Commercial19,978 (64.5)54,634 (58.8)619,077 (51.2)
        Not recorded5826 (18.8)18,770 (20.2)246,673 (20.4)
        Medicare4127 (13.3)13,246 (14.3)254,220 (21.0)
        Medicaid1042 (3.4)6229 (6.7)89,469 (7.4)
        Self-pay3 (0.0)40 (0.0)789 (0.1)P < .001, < .001
    • IQR, interquartile range.

    • ↵* Other includes mixed race, other, and unknown.

    • ↵† Variables were compared between groups using a t-test for the continuous variable age, and the χ2 test for the categorical variables: sex, race, ethnicity, and financial class. Comparisons were made between the yoga cohort and the control cohort (P-values before the comma) and between the yoga cohort and the broader patient population (P-values after the comma).

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The Journal of the American Board of Family     Medicine: 32 (6)
The Journal of the American Board of Family Medicine
Vol. 32, Issue 6
November-December 2019
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Prevalence and Characterization of Yoga Mentions in the Electronic Health Record
Nadia M. Penrod, Selah Lynch, Sunil Thomas, Nithya Seshadri, Jason H. Moore
The Journal of the American Board of Family Medicine Nov 2019, 32 (6) 790-800; DOI: 10.3122/jabfm.2019.06.190115

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Prevalence and Characterization of Yoga Mentions in the Electronic Health Record
Nadia M. Penrod, Selah Lynch, Sunil Thomas, Nithya Seshadri, Jason H. Moore
The Journal of the American Board of Family Medicine Nov 2019, 32 (6) 790-800; DOI: 10.3122/jabfm.2019.06.190115
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