Browsing by Author "Griffith, Ashley"

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    An Automated Line-of-Therapy Algorithm for Adults With Metastatic Non-Small Cell Lung Cancer: Validation Study Using Blinded Manual Chart Review
    (JMIR Publications, 2021-10-12) Meng, Weilin; Mosesso, Kelly M.; Lane, Kathleen A.; Roberts, Anna R.; Griffith, Ashley; Ou, Wanmei; Dexter, Paul R.; Biostatistics & Health Data Science, School of Medicine
    Background: Extraction of line-of-therapy (LOT) information from electronic health record and claims data is essential for determining longitudinal changes in systemic anticancer therapy in real-world clinical settings. Objective: The aim of this retrospective cohort analysis is to validate and refine our previously described open-source LOT algorithm by comparing the output of the algorithm with results obtained through blinded manual chart review. Methods: We used structured electronic health record data and clinical documents to identify 500 adult patients treated for metastatic non-small cell lung cancer with systemic anticancer therapy from 2011 to mid-2018; we assigned patients to training (n=350) and test (n=150) cohorts, randomly divided proportional to the overall ratio of simple:complex cases (n=254:246). Simple cases were patients who received one LOT and no maintenance therapy; complex cases were patients who received more than one LOT and/or maintenance therapy. Algorithmic changes were performed using the training cohort data, after which the refined algorithm was evaluated against the test cohort. Results: For simple cases, 16 instances of discordance between the LOT algorithm and chart review prerefinement were reduced to 8 instances postrefinement; in the test cohort, there was no discordance between algorithm and chart review. For complex cases, algorithm refinement reduced the discordance from 68 to 62 instances, with 37 instances in the test cohort. The percentage agreement between LOT algorithm output and chart review for patients who received one LOT was 89% prerefinement, 93% postrefinement, and 93% for the test cohort, whereas the likelihood of precise matching between algorithm output and chart review decreased with an increasing number of unique regimens. Several areas of discordance that arose from differing definitions of LOTs and maintenance therapy could not be objectively resolved because of a lack of precise definitions in the medical literature. Conclusions: Our findings identify common sources of discordance between the LOT algorithm and clinician documentation, providing the possibility of targeted algorithm refinement.
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    Identifying and Characterizing a Chronic Cough Cohort Through Electronic Health Records
    (Elsevier, 2021-06) Weiner, Michael; Dexter, Paul R.; Heithoff, Kim; Roberts, Anna R.; Liu, Ziyue; Griffith, Ashley; Hui, Siu; Schelfhout, Jonathan; Dicpinigaitis, Peter; Doshi, Ishita; Weaver, Jessica P.; Medicine, School of Medicine
    Background Chronic cough (CC) of 8 weeks or more affects about 10% of adults and may lead to expensive treatments and reduced quality of life. Incomplete diagnostic coding complicates identifying CC in electronic health records (EHRs). Natural language processing (NLP) of EHR text could improve detection. Research Question Can NLP be used to identify cough in EHRs, and to characterize adults and encounters with CC? Study Design and Methods A Midwestern EHR system identified patients aged 18 to 85 years during 2005 to 2015. NLP was used to evaluate text notes, except prescriptions and instructions, for mentions of cough. Two physicians and a biostatistician reviewed 12 sets of 50 encounters each, with iterative refinements, until the positive predictive value for cough encounters exceeded 90%. NLP, International Classification of Diseases, 10th revision, or medication was used to identify cough. Three encounters spanning 56 to 120 days defined CC. Descriptive statistics summarized patients and encounters, including referrals. Results Optimizing NLP required identifying and eliminating cough denials, instructions, and historical references. Of 235,457 cough encounters, 23% had a relevant diagnostic code or medication. Applying chronicity to cough encounters identified 23,371 patients (61% women) with CC. NLP alone identified 74% of these patients; diagnoses or medications alone identified 15%. The positive predictive value of NLP in the reviewed sample was 97%. Referrals for cough occurred for 3.0% of patients; pulmonary medicine was most common initially (64% of referrals). Limitations Some patients with diagnosis codes for cough, encounters at intervals greater than 4 months, or multiple acute cough episodes may have been misclassified. Interpretation NLP successfully identified a large cohort with CC. Most patients were identified through NLP alone, rather than diagnoses or medications. NLP improved detection of patients nearly sevenfold, addressing the gap in ability to identify and characterize CC disease burden. Nearly all cases appeared to be managed in primary care. Identifying these patients is important for characterizing treatment and unmet needs.
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    Management of Chronic Cough in Adult Primary Care: A Qualitative Study
    (Springer, 2021-09) Gowan, Tayler M.; Huffman, Monica; Weiner, Michael; Talib, Tasneem L.; Schelfhout, Jonathan; Weaver, Jessica; Griffith, Ashley; Doshi, Ishita; Dexter, Paul; Bali, Vishal; Medicine, School of Medicine
    This study is the first to describe, qualitatively, PCPs’ experiences evaluating and treating CC in adults. By interviewing clinicians, we sought to understand reasons for referrals, accessibility and use of clinical guidelines, confidence in evaluation and treatment, perceptions and attitudes, and desired resources. Findings may help in elucidating clinical decision-making and could indicate areas for improvement in dissemination and use of guidelines.
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    Prescriptions of opioid-containing drugs in patients with chronic cough
    (Sage, 2024) Weiner, Michael; Liu, Ziyue; Schelfhout, Jonathan; Dexter, Paul; Roberts, Anna R.; Griffith, Ashley; Bali, Vishal; Weaver, Jessica; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Background: Chronic cough (CC) affects about 10% of adults, but opioid use in CC is not well understood. Objectives: To determine the use of opioid-containing cough suppressant (OCCS) prescriptions in patients with CC using electronic health records. Design: Retrospective cohort study. Methods: Through retrospective analysis of Midwestern U.S. electronic health records, diagnoses, prescriptions, and natural language processing identified CC - at least three medical encounters with cough, with 56-120 days between first and last encounter - and a 'non-chronic cohort'. Student's t-test, Pearson's chi-square, and zero-inflated Poisson models were used. Results: About 20% of 23,210 patients with CC were prescribed OCCS; odds of an OCCS prescription were twice as great in CC. In CC, OCCS drugs were ordered in 38% with Medicaid insurance and 15% with commercial insurance. Conclusion: Findings identify an important role for opioids in CC, and opportunity to learn more about the drugs' effectiveness.