A “quest for answers” in the emerging field of postdeployment respiratory health

Abstract: "So, what's wrong with me?" asks James, as I finish reviewing his test results in our Veterans Affairs (VA) Post-Deployment Respiratory Health (PDRH) clinic. He is prepared to ask a series of follow-up questions: "What caused this? Do I need a lung biopsy? Will I get worse? Can I obtain VA benefits? Is there treatment? Will I need oxygen or a lung transplant?" And then there is the most pressing question: "Will I die from this?" As a VA physician-scientist in the emerging field of PDRH, I know well the tension that permeates the exam room in response to these questions. The pillars of our physician-patient relationship— confidence, trust, and hope-hang in the balance. Yet, at present, most of these questions lack satisfying answers. I often ask myself, "How do I answer these questions honestly without eroding my patients trust in me and the institution I represent?" In this Perspective, I will contextualize these questions as I have experienced them and describe the journey that ensues when answers prove elusive.

Read the full article
Report a problem with this article

Related articles

  • More for Researchers

    Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data

    Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.