Browsing by Author "Casarett, David"

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    Can metaphors and analogies improve communication with seriously ill patients?
    (J Palliat Med, 2010-03) Casarett, David; Pickard, Amy; Fishman, Jessica M; Alexander, Stewart C; Arnold, Robert M; Pollak, Kathryn I; Tulsky, James A
    OBJECTIVE: It is not known how often physicians use metaphors and analogies, or whether they improve patients' perceptions of their physicians' ability to communicate effectively. Therefore, the objective of this study was to determine whether the use of metaphors and analogies in difficult conversations is associated with better patient ratings of their physicians' communication skills. DESIGN: Cross-sectional observational study of audio-recorded conversations between patients and physicians. SETTING: Three outpatient oncology practices. PATIENTS: Ninety-four patients with advanced cancer and 52 physicians. INTERVENTION: None. MAIN OUTCOME MEASURES: Conversations were reviewed and coded for the presence of metaphors and analogies. Patients also completed a 6-item rating of their physician's ability to communicate. RESULTS: In a sample of 101 conversations, coders identified 193 metaphors and 75 analogies. Metaphors appeared in approximately twice as many conversations as analogies did (65/101, 64% versus 31/101, 31%; sign test p < 0.001). Conversations also contained more metaphors than analogies (mean 1.6, range 0-11 versus mean 0.6, range 0-5; sign rank test p < 0.001). Physicians who used more metaphors elicited better patient ratings of communication (rho = 0.27; p = 0.006), as did physicians who used more analogies (Spearman rho = 0.34; p < 0.001). CONCLUSIONS: The use of metaphors and analogies may enhance physicians' ability to communicate.
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    Machine learning functional impairment classification with electronic health record data.
    (Journal of the American Geriatrics Society, 2023-09) Pavon, Juliessa M; Previll, Laura; Woo, Myung; Henao, Ricardo; Solomon, Mary; Rogers, Ursula; Olson, Andrew; Fischer, Jonathan; Leo, Christopher; Fillenbaum, Gerda; Hoenig, Helen; Casarett, David

    Background

    Poor functional status is a key marker of morbidity, yet is not routinely captured in clinical encounters. We developed and evaluated the accuracy of a machine learning algorithm that leveraged electronic health record (EHR) data to provide a scalable process for identification of functional impairment.

    Methods

    We identified a cohort of patients with an electronically captured screening measure of functional status (Older Americans Resources and Services ADL/IADL) between 2018 and 2020 (N = 6484). Patients were classified using unsupervised learning K means and t-distributed Stochastic Neighbor Embedding into normal function (NF), mild to moderate functional impairment (MFI), and severe functional impairment (SFI) states. Using 11 EHR clinical variable domains (832 variable input features), we trained an Extreme Gradient Boosting supervised machine learning algorithm to distinguish functional status states, and measured prediction accuracies. Data were randomly split into training (80%) and test (20%) sets. The SHapley Additive Explanations (SHAP) feature importance analysis was used to list the EHR features in rank order of their contribution to the outcome.

    Results

    Median age was 75.3 years, 62% female, 60% White. Patients were classified as 53% NF (n = 3453), 30% MFI (n = 1947), and 17% SFI (n = 1084). Summary of model performance for identifying functional status state (NF, MFI, SFI) was AUROC (area under the receiving operating characteristic curve) 0.92, 0.89, and 0.87, respectively. Age, falls, hospitalization, home health use, labs (e.g., albumin), comorbidities (e.g., dementia, heart failure, chronic kidney disease, chronic pain), and social determinants of health (e.g., alcohol use) were highly ranked features in predicting functional status states.

    Conclusion

    A machine learning algorithm run on EHR clinical data has potential utility for differentiating functional status in the clinical setting. Through further validation and refinement, such algorithms can complement traditional screening methods and result in a population-based strategy for identifying patients with poor functional status who need additional health resources.