An Evaluation of Novel Domains for Predicting 30-Day Readmission

The Centers for Medicare and Medicaid Services has proposed to financially penalize hospitals that have 30-day readmission rates above the national mean. As a result hospitals caring for disadvantaged populations with more needs might be penalized by current 30-day readmission models that do not include measures of social risk and functional status of the patients served. These are two important variable domains that directly impact a patient's ability to manage their disease. Social risk factors (e.g. living alone, social support, marginal housing, and alcohol abuse) and functional status (e.g. mobility, fall risk) are rarely present in administrative data, which is why so few readmission models include this data. Yet many of these variables are available in electronic health records (EHR) and the advancement of the field of informatics has made the extraction of these data feasible. These variables may improve the discriminative ability of 30-day readmission models which currently explain little of the variation in readmission rates among patients. We propose to improve 30-day readmission models by extracting measures of social risk and functional status from the EHR using the novel method of Natural Language Processing (NLP)..

Selected Publications, Posters, and Presentations

  • South B, Christensen L, Mowery DL, Tharp M, Vali M, Carter M, Conway M, Gundlapalli A, Chapman WW, Keyhani S. Automatic Extraction of Social Determinants of Health from Veterans Affairs Clinical Documents using Natural Language Processing. 2017 Joint Summits on Translational Science. San Francisco, CA.
  • South BR, Christensen L, Mowery DL, Gundlapalli A, Tharp M, Vali M, Carter M, Conway M, Keyhani S, Chapman WW. Annotating Measures of Functional Status in Veteran Affairs Clinical Documents. Concordium Health 2015. Washington, DC.
  • South BR, Gundlapalli A, Tharp M, Carter M, Vali M, Mowery D, Conway M, Keyhani S, Chapman
    WW. Extracting Social History and Functional Status from Veteran Affairs Clinical Documents.
    AMIA 2015 Joint Summits on Translational Science. San Francisco, CA.
  • South BR, Mowery DL, Gundlapalli AV, Tharp M, Christensen L, Vali M, Carter M, Conway M, Keyhani S, Chapman WW. Annotating ADLs and IADLs in Veterans Affairs Clinical Documents. AMIA Annu Symp. 2015. San Francisco, CA.
  • Grant Number: 5R01HL116522-02

    PI: 
    Salomeh Keyhani
    Co-I: Wendy Chapman