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975 results on '"Elmore, Joann G."'

1. Race, Ethnicity, and Gender Differences in Patient Reported Well-Being and Cognitive Functioning Within 3 Months of Symptomatic Illness During COVID-19 Pandemic

Author

Hill, Mandy J., Huebinger, Ryan M., Ebna Mannan, Imtiaz, Yu, Huihui, Wisk, Lauren E., O’Laughlin, Kelli N., Gentile, Nicole L., Stephens, Kari A., Gottlieb, Michael, Weinstein, Robert A., Koo, Katherine, Santangelo, Michelle, Saydah, Sharon, Spatz, Erica S., Lin, Zhenqiu, Schaeffer, Kevin, Kean, Efrat, Montoy, Juan Carlos C., Rodriguez, Robert M., Idris, Ahamed H., McDonald, Samuel, Elmore, Joann G., and Venkatesh, Arjun

Published
2024
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24. The association between prolonged SARS-CoV-2 symptoms and work outcomes.

Author

Venkatesh, Arjun K., Yu, Huihui, Malicki, Caitlin, Gottlieb, Michael, Elmore, Joann G., Hill, Mandy J., Idris, Ahamed H., Montoy, Juan Carlos C., O'Laughlin, Kelli N., Rising, Kristin L., Stephens, Kari A., Spatz, Erica S., and Weinstein, Robert A.

Subjects
SARS-CoV-2, POST-acute COVID-19 syndrome, SYMPTOMS, COVID-19, COVID-19 pandemic, ECONOMIC impact
Abstract

While the early effects of the COVID-19 pandemic on the United States labor market are well-established, less is known about the long-term impact of SARS-CoV-2 infection and Long COVID on employment. To address this gap, we analyzed self-reported data from a prospective, national cohort study to estimate the effects of SARS-CoV-2 symptoms at three months post-infection on missed workdays and return to work. The analysis included 2,939 adults in the Innovative Support for Patients with SARS-CoV-2 Infections Registry (INSPIRE) study who tested positive for their initial SARS-CoV-2 infection at the time of enrollment, were employed before the pandemic, and completed a baseline and three-month electronic survey. At three months post-infection, 40.8% of participants reported at least one SARS-CoV-2 symptom and 9.6% of participants reported five or more SARS-CoV-2 symptoms. When asked about missed work due to their SARS-CoV-2 infection at three months, 7.2% of participants reported missing ≥10 workdays and 13.9% of participants reported not returning to work since their infection. At three months, participants with ≥5 symptoms had a higher adjusted odds ratio of missing ≥10 workdays (2.96, 95% CI 1.81–4.83) and not returning to work (2.44, 95% CI 1.58–3.76) compared to those with no symptoms. Prolonged SARS-CoV-2 symptoms were common, affecting 4-in-10 participants at three-months post-infection, and were associated with increased odds of work loss, most pronounced among adults with ≥5 symptoms at three months. Despite the end of the federal Public Health Emergency for COVID-19 and efforts to "return to normal", policymakers must consider the clinical and economic implications of the COVID-19 pandemic on people's employment status and work absenteeism, particularly as data characterizing the numerous health and well-being impacts of Long COVID continue to emerge. Improved understanding of risk factors for lost work time may guide efforts to support people in returning to work. [ABSTRACT FROM AUTHOR]

Published
2024
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30. Excess Patient Visits for Cough and Pulmonary Disease at a Large US Health System in the Months Prior to the COVID-19 Pandemic: Time-Series Analysis

Author

Elmore, Joann G, Wang, Pin-Chieh, Kerr, Kathleen F, Schriger, David L, Morrison, Douglas E, Brookmeyer, Ron, Pfeffer, Michael A, Payne, Thomas H, and Currier, Judith S

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundAccurately assessing the regional activity of diseases such as COVID-19 is important in guiding public health interventions. Leveraging electronic health records (EHRs) to monitor outpatient clinical encounters may lead to the identification of emerging outbreaks. ObjectiveThe aim of this study is to investigate whether excess visits where the word “cough” was present in the EHR reason for visit, and hospitalizations with acute respiratory failure were more frequent from December 2019 to February 2020 compared with the preceding 5 years. MethodsA retrospective observational cohort was identified from a large US health system with 3 hospitals, over 180 clinics, and 2.5 million patient encounters annually. Data from patient encounters from July 1, 2014, to February 29, 2020, were included. Seasonal autoregressive integrated moving average (SARIMA) time-series models were used to evaluate if the observed winter 2019/2020 rates were higher than the forecast 95% prediction intervals. The estimated excess number of visits and hospitalizations in winter 2019/2020 were calculated compared to previous seasons. ResultsThe percentage of patients presenting with an EHR reason for visit containing the word “cough” to clinics exceeded the 95% prediction interval the week of December 22, 2019, and was consistently above the 95% prediction interval all 10 weeks through the end of February 2020. Similar trends were noted for emergency department visits and hospitalizations starting December 22, 2019, where observed data exceeded the 95% prediction interval in 6 and 7 of the 10 weeks, respectively. The estimated excess over the 3-month 2019/2020 winter season, obtained by either subtracting the maximum or subtracting the average of the five previous seasons from the current season, was 1.6 or 2.0 excess visits for cough per 1000 outpatient visits, 11.0 or 19.2 excess visits for cough per 1000 emergency department visits, and 21.4 or 39.1 excess visits per 1000 hospitalizations with acute respiratory failure, respectively. The total numbers of excess cases above the 95% predicted forecast interval were 168 cases in the outpatient clinics, 56 cases for the emergency department, and 18 hospitalized with acute respiratory failure. ConclusionsA significantly higher number of patients with respiratory complaints and diseases starting in late December 2019 and continuing through February 2020 suggests community spread of SARS-CoV-2 prior to established clinical awareness and testing capabilities. This provides a case example of how health system analytics combined with EHR data can provide powerful and agile tools for identifying when future trends in patient populations are outside of the expected ranges.

Published
2020
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31. Correction: OpenNotes After 7 Years: Patient Experiences With Ongoing Access to Their Clinicians’ Outpatient Visit Notes

Author

Walker, Jan, Leveille, Suzanne, Bell, Sigall, Chimowitz, Hannah, Dong, Zhiyong, Elmore, Joann G, Fernandez, Leonor, Fossa, Alan, Gerard, Macda, Fitzgerald, Patricia, Harcourt, Kendall, Jackson, Sara, Payne, Thomas H, Perez, Jocelyn, Shucard, Hannah, Stametz, Rebecca, DesRoches, Catherine, and Delbanco, Tom

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Published
2020
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41. Machine learning classification of diagnostic accuracy in pathologists interpreting breast biopsies.

Author

Brunyé, Tad T, Booth, Kelsey, Hendel, Dalit, Kerr, Kathleen F, Shucard, Hannah, Weaver, Donald L, and Elmore, Joann G

Abstract

Objective This study explores the feasibility of using machine learning to predict accurate versus inaccurate diagnoses made by pathologists based on their spatiotemporal viewing behavior when evaluating digital breast biopsy images. Materials and Methods The study gathered data from 140 pathologists of varying experience levels who each reviewed a set of 14 digital whole slide images of breast biopsy tissue. Pathologists' viewing behavior, including zooming and panning actions, was recorded during image evaluation. A total of 30 features were extracted from the viewing behavior data, and 4 machine learning algorithms were used to build classifiers for predicting diagnostic accuracy. Results The Random Forest classifier demonstrated the best overall performance, achieving a test accuracy of 0.81 and area under the receiver-operator characteristic curve of 0.86. Features related to attention distribution and focus on critical regions of interest were found to be important predictors of diagnostic accuracy. Further including case-level and pathologist-level information incrementally improved classifier performance. Discussion Results suggest that pathologists' viewing behavior during digital image evaluation can be leveraged to predict diagnostic accuracy, affording automated feedback and decision support systems based on viewing behavior to aid in training and, ultimately, clinical practice. They also carry implications for basic research examining the interplay between perception, thought, and action in diagnostic decision-making. Conclusion The classifiers developed herein have potential applications in training and clinical settings to provide timely feedback and support to pathologists during diagnostic decision-making. Further research could explore the generalizability of these findings to other medical domains and varied levels of expertise. [ABSTRACT FROM AUTHOR]

Published
2024
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42. Ethnic and racial differences in self-reported symptoms, health status, activity level, and missed work at 3 and 6 months following SARS-CoV-2 infection.

Author

O'Laughlin, Kelli N., Klabbers, Robin E., Mannan, Imtiaz Ebna, Gentile, Nicole L., Geyer, Rachel E., Zihan Zheng, Huihui Yu, Shu-Xia Li, Chan, Kwun C. G., Spatz, Erica S., Wang, Ralph C., L'Hommedieu, Michelle, Weinstein, Robert A., Plumb, Ian D., Gottlieb, Michael, Huebinger, Ryan M., Hagen, Melissa, Elmore, Joann G., Hill, Mandy J., and Kelly, Morgan

Published
2024
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49. Evaluation of the Melanocytic Pathology Assessment Tool and Hierarchy for Diagnosis (MPATH-Dx) classification scheme for diagnosis of cutaneous melanocytic neoplasms: Results from the International Melanoma Pathology Study Group

Author

Lott, Jason P., Elmore, Joann G., Zhao, Ge A., Knezevich, Stevan R., Frederick, Paul D., Reisch, Lisa M., Chu, Emily Y., Cook, Martin G., Duncan, Lyn M., Elenitsas, Rosalie, Gerami, Pedram, Landman, Gilles, Lowe, Lori, Messina, Jane L., Mihm, Martin C., van den Oord, Joost J., Rabkin, Michael S., Schmidt, Birgitta, Shea, Christopher R., Yun, Sook Jung, Xu, George X., Piepkorn, Michael W., Elder, David E., and Barnhill, Raymond L.

Published
2016
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