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Natural language processing (NLP) for medication adherence: complex semantics and negation
Investigator (PI): Roberts, Kirk
Performing Organization (PO): (Current): University of Texas Health Science Center at Houston, School of Biomedical Informatics / (713) 500-3900
Supporting Agency (SA): Patient-Centered Outcomes Research Institute (PCORI)
Initial Year: 2019
Final Year: 2023
Record Source/Award ID: PCORI/ME-2018C1-10963
Funding: Total Award Amount: $954,189
Award Type: Contract
Award Information: PCORI: More information and project results (when completed)
Abstract: Medication adherence is an important issue, as non-adherence all too frequently results in significant negative outcomes of interest to patients (for example, hospitalization, seizures, loss of function and independence). It is also largely preventable, and partly within the patient's control. Unfortunately, studying non-adherence and its impact on outcomes can be quite difficult: not only do most medical studies require strict adherence, but studies that intentionally deprive patients of their treatments may be unethical and, besides, would not necessarily reflect the non-adherence patterns of real individuals. Fortunately, electronic health record (EHR) data, stored to keep track of an individual patient's medical records, as a byproduct offers a way to study typical patients and the impact of their non-adherence on outcomes of interest to patients. EHR-based studies can even achieve a very large scale, up to millions of patients. However, adherence information in EHR is largely stored in free-text fields (i.e., in English as opposed to a structured database). This requires the development of a natural language processing (NLP) system to extract and represent the adherence-related information. This project will develop such an NLP tool based on state-of-the-art machine learning methods. This adherence NLP tool will enable future large-scale studies on the relationship between non-adherence and patient-centered outcomes. The tool will not be limited to "binary" studies of adherence (does take medications vs. does not), but will capture granular details (for example, frequency, certainty, reasons for non-adherence) that will enable large "big data" studies to leverage their strength in scale to determine how precise non-adherence patterns impact outcomes. The fundamental philosophy guiding this study is that in order to build a successful tool for extracting fine-grained adherence information from EHR notes, patients must be involved in the research process to craft a model of adherence. Simply put, in order to know what patterns of non-adherence to look for, one must engage with patients who understand the difficulties of adherence to treatment plans. We will convene an advisory group of patients, clinicians, and stakeholders with a focus on two chronic conditions known for non-adherence impacting patient-centered outcomes: diabetes and depression. These patient-stakeholders will identify the most salient aspects of non-adherence, including those they typically communicate with their provider and those aspects they typically withhold. With the adherence model created with the help of patient-stakeholders, the NLP tool created in this project will enable valuable research studies in this understudied area that nonetheless has enormous impacts on the quality of patients' lives.
MeSH Terms:
  • Chronic Disease
  • Databases, Factual
  • Electronic Health Records
  • Humans
  • Machine Learning
  • Medical Informatics /*methods
  • * Medication Adherence
  • * Natural Language Processing
  • Outcome Assessment, Health Care
  • Patient-Centered Care
  • Quality of Life
  • * Semantics
Country: United States
State: Texas
Zip Code: 77030
UI: 20193303
Project Status: Ongoing