364 results on '"Lenert, Leslie"'
Search Results
2. Using implementation science to evaluate a population-wide genomic screening program: Findings from the first 20,000 In Our DNA SC participants
- Author
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Allen, Caitlin G., Hunt, Kelly J., McMahon, Lori L., Thornhill, Clay, Jackson, Amy, Clark, John T., Kirchoff, Katie, Garrison, Kelli L., Foil, Kimberly, Malphrus, Libby, Norman, Samantha, Ramos, Paula S., Perritt, Kelly, Brown, Caroline, Lenert, Leslie, and Judge, Daniel P.
- Published
- 2024
- Full Text
- View/download PDF
3. Is hospitalization a missed opportunity to intervene on tobacco cessation?
- Author
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Nielsen, Ellen M., Zhang, Jingwen, Marsden, Justin, Bays, Chloe, Moran, William P., Mauldin, Patrick D., Lenert, Leslie A., Toll, Benjamin A., Schreiner, Andrew D., and Heincelman, Marc
- Published
- 2024
- Full Text
- View/download PDF
4. Initial development of tools to identify child abuse and neglect in pediatric primary care
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Hanson, Rochelle F., Zhu, Vivienne, Are, Funlola, Espeleta, Hannah, Wallis, Elizabeth, Heider, Paul, Kautz, Marin, and Lenert, Leslie
- Published
- 2023
- Full Text
- View/download PDF
5. Clinical phenotypes and outcomes in children with multisystem inflammatory syndrome across SARS-CoV-2 variant eras: a multinational study from the 4CE consortium
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Aaron, James R., Adam, Atif, Agapito, Giuseppe, Albayrak, Adem, Albi, Giuseppe, Alessiani, Mario, Alloni, Anna, Amendola, Danilo F., Angoulvant, François, Anthony, Li LLJ., Aronow, Bruce J., Ashraf, Fatima, Atz, Andrew, Avillach, Paul, Panickan, Vidul Ayakulangara, Azevedo, Paula S., Badenes, Rafael, Balshi, James, Batugo, Ashley, Beaulieu-Jones, Brendin R., Beaulieu-Jones, Brett K., Bell, Douglas S., Bellasi, Antonio, Bellazzi, Riccardo, Benoit, Vincent, Beraghi, Michele, Bernal-Sobrino, José Luis, Bernaux, Mélodie, Bey, Romain, Bhatnagar, Surbhi, Blanco-Martínez, Alvar, Boeker, Martin, Bonzel, Clara-Lea, Booth, John, Bosari, Silvano, Bourgeois, Florence T., Bradford, Robert L., Brat, Gabriel A., Bréant, Stéphane, Brown, Nicholas W., Bruno, Raffaele, Bryant, William A., Bucalo, Mauro, Bucholz, Emily, Burgun, Anita, Cai, Tianxi, Cannataro, Mario, Carmona, Aldo, Cattelan, Anna Maria, Caucheteux, Charlotte, Champ, Julien, Chen, Jin, Chen, Krista Y., Chiovato, Luca, Chiudinelli, Lorenzo, Cho, Kelly, Cimino, James J., Colicchio, Tiago K., Cormont, Sylvie, Cossin, Sébastien, Craig, Jean B., Cruz-Bermúdez, Juan Luis, Cruz-Rojo, Jaime, Dagliati, Arianna, Daniar, Mohamad, Daniel, Christel, Das, Priyam, Devkota, Batsal, Dionne, Audrey, Duan, Rui, Dubiel, Julien, DuVall, Scott L., Esteve, Loic, Estiri, Hossein, Fan, Shirley, Follett, Robert W., Ganslandt, Thomas, García-Barrio, Noelia, Garmire, Lana X., Gehlenborg, Nils, Getzen, Emily J., Geva, Alon, Goh, Rachel SJ., González, Tomás González, Gradinger, Tobias, Gramfort, Alexandre, Griffier, Romain, Griffon, Nicolas, Grisel, Olivier, Gutiérrez-Sacristán, Alba, Guzzi, Pietro H., Han, Larry, Hanauer, David A., Haverkamp, Christian, Hazard, Derek Y., He, Bing, Henderson, Darren W., Hilka, Martin, Ho, Yuk-Lam, Holmes, John H., Honerlaw, Jacqueline P., Hong, Chuan, Huling, Kenneth M., Hutch, Meghan R., Issitt, Richard W., Jannot, Anne Sophie, Jouhet, Vianney, Kainth, Mundeep K., Kate, Kernan F., Kavuluru, Ramakanth, Keller, Mark S., Kennedy, Chris J., Kernan, Kate F., Key, Daniel A., Kirchoff, Katie, Klann, Jeffrey G., Kohane, Isaac S., Krantz, Ian D., Kraska, Detlef, Krishnamurthy, Ashok K., L'Yi, Sehi, Leblanc, Judith, Lemaitre, Guillaume, Lenert, Leslie, Leprovost, Damien, Liu, Molei, Will Loh, Ne Hooi, Long, Qi, Lozano-Zahonero, Sara, Luo, Yuan, Lynch, Kristine E., Mahmood, Sadiqa, Maidlow, Sarah E., Makoudjou, Adeline, Makwana, Simran, Malovini, Alberto, Mandl, Kenneth D., Mao, Chengsheng, Maram, Anupama, Maripuri, Monika, Martel, Patricia, Martins, Marcelo R., Marwaha, Jayson S., Masino, Aaron J., Mazzitelli, Maria, Mazzotti, Diego R., Mensch, Arthur, Milano, Marianna, Minicucci, Marcos F., Moal, Bertrand, Ahooyi, Taha Mohseni, Moore, Jason H., Moraleda, Cinta, Morris, Jeffrey S., Morris, Michele, Moshal, Karyn L., Mousavi, Sajad, Mowery, Danielle L., Murad, Douglas A., Murphy, Shawn N., Naughton, Thomas P., Breda Neto, Carlos Tadeu, Neuraz, Antoine, Newburger, Jane, Ngiam, Kee Yuan, Njoroge, Wanjiku FM., Norman, James B., Obeid, Jihad, Okoshi, Marina P., Olson, Karen L., Omenn, Gilbert S., Orlova, Nina, Ostasiewski, Brian D., Palmer, Nathan P., Paris, Nicolas, Patel, Lav P., Pedrera-Jiménez, Miguel, Pfaff, Ashley C., Pfaff, Emily R., Pillion, Danielle, Pizzimenti, Sara, Priya, Tanu, Prokosch, Hans U., Prudente, Robson A., Prunotto, Andrea, Quirós-González, Víctor, Ramoni, Rachel B., Raskin, Maryna, Rieg, Siegbert, Roig-Domínguez, Gustavo, Rojo, Pablo, Romero-Garcia, Nekane, Rubio-Mayo, Paula, Sacchi, Paolo, Sáez, Carlos, Salamanca, Elisa, Samayamuthu, Malarkodi Jebathilagam, Sanchez-Pinto, L. Nelson, Sandrin, Arnaud, Santhanam, Nandhini, Santos, Janaina C.C., Sanz Vidorreta, Fernando J., Savino, Maria, Schriver, Emily R., Schubert, Petra, Schuettler, Juergen, Scudeller, Luigia, Sebire, Neil J., Serrano-Balazote, Pablo, Serre, Patricia, Serret-Larmande, Arnaud, Shah, Mohsin A., Hossein Abad, Zahra Shakeri, Silvio, Domenick, Sliz, Piotr, Son, Jiyeon, Sonday, Charles, South, Andrew M., Sperotto, Francesca, Spiridou, Anastasia, Strasser, Zachary H., Tan, Amelia LM., Tan, Bryce W.Q., Tan, Byorn W.L., Tanni, Suzana E., Taylor, Deanne M., Terriza-Torres, Ana I., Tibollo, Valentina, Tippmann, Patric, Toh, Emma MS., Torti, Carlo, Trecarichi, Enrico M., Vallejos, Andrew K., Varoquaux, Gael, Vella, Margaret E., Verdy, Guillaume, Vie, Jill-Jênn, Visweswaran, Shyam, Vitacca, Michele, Wagholikar, Kavishwar B., Waitman, Lemuel R., Wang, Xuan, Wassermann, Demian, Weber, Griffin M., Wolkewitz, Martin, Wong, Scott, Xia, Zongqi, Xiong, Xin, Ye, Ye, Yehya, Nadir, Yuan, William, Zachariasse, Joany M., Zahner, Janet J., Zambelli, Alberto, Zhang, Harrison G., Zöller, Daniela, Zuccaro, Valentina, Zucco, Chiara, Li, Xiudi, Rofeberg, Valerie N., Elias, Matthew D., Laird-Gion, Jessica, and Newburger, Jane W.
- Published
- 2023
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6. Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort study
- Author
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Aaron, James R., Agapito, Giuseppe, Albayrak, Adem, Albi, Giuseppe, Alessiani, Mario, Alloni, Anna, Amendola, Danilo F., François Angoulvant, Anthony, Li L.L.J., Aronow, Bruce J., Ashraf, Fatima, Atz, Andrew, Avillach, Paul, Azevedo, Paula S., Balshi, James, Beaulieu-Jones, Brett K., Bell, Douglas S., Bellasi, Antonio, Bellazzi, Riccardo, Benoit, Vincent, Beraghi, Michele, Bernal-Sobrino, José Luis, Bernaux, Mélodie, Bey, Romain, Bhatnagar, Surbhi, Blanco-Martínez, Alvar, Bonzel, Clara-Lea, Booth, John, Bosari, Silvano, Bourgeois, Florence T., Bradford, Robert L., Brat, Gabriel A., Bréant, Stéphane, Brown, Nicholas W., Bruno, Raffaele, Bryant, William A., Bucalo, Mauro, Bucholz, Emily, Burgun, Anita, Cai, Tianxi, Cannataro, Mario, Carmona, Aldo, Caucheteux, Charlotte, Champ, Julien, Chen, Jin, Chen, Krista Y., Chiovato, Luca, Chiudinelli, Lorenzo, Cho, Kelly, Cimino, James J., Colicchio, Tiago K., Cormont, Sylvie, Cossin, Sébastien, Craig, Jean B., Cruz-Bermúdez, Juan Luis, Cruz-Rojo, Jaime, Dagliati, Arianna, Daniar, Mohamad, Daniel, Christel, Das, Priyam, Devkota, Batsal, Dionne, Audrey, Duan, Rui, Dubiel, Julien, DuVall, Scott L., Esteve, Loic, Estiri, Hossein, Fan, Shirley, Follett, Robert W., Ganslandt, Thomas, Barrio, Noelia García, Garmire, Lana X., Gehlenborg, Nils, Getzen, Emily J., Geva, Alon, Gradinger, Tobias, Gramfort, Alexandre, Griffier, Romain, Griffon, Nicolas, Grisel, Olivier, Gutiérrez-Sacristán, Alba, Han, Larry, Hanauer, David A., Haverkamp, Christian, Hazard, Derek Y., He, Bing, Henderson, Darren W., Hilka, Martin, Ho, Yuk-Lam, Holmes, John H., Hong, Chuan, Huling, Kenneth M., Hutch, Meghan R., Issitt, Richard W., Jannot, Anne Sophie, Jouhet, Vianney, Kavuluru, Ramakanth, Keller, Mark S., Kennedy, Chris J., Key, Daniel A., Kirchoff, Katie, Klann, Jeffrey G., Kohane, Isaac S., Krantz, Ian D., Kraska, Detlef, Krishnamurthy, Ashok K., L'Yi, Sehi, Le, Trang T., Leblanc, Judith, Lemaitre, Guillaume, Lenert, Leslie, Leprovost, Damien, Liu, Molei, Will Loh, Ne Hooi, Long, Qi, Lozano-Zahonero, Sara, Luo, Yuan, Lynch, Kristine E., Mahmood, Sadiqa, Maidlow, Sarah E., Makoudjou, Adeline, Malovini, Alberto, Mandl, Kenneth D., Mao, Chengsheng, Maram, Anupama, Martel, Patricia, Martins, Marcelo R., Marwaha, Jayson S., Masino, Aaron J., Mazzitelli, Maria, Mensch, Arthur, Milano, Marianna, Minicucci, Marcos F., Moal, Bertrand, Ahooyi, Taha Mohseni, Moore, Jason H., Moraleda, Cinta, Morris, Jeffrey S., Morris, Michele, Moshal, Karyn L., Mousavi, Sajad, Mowery, Danielle L., Murad, Douglas A., Murphy, Shawn N., Naughton, Thomas P., Breda Neto, Carlos Tadeu, Neuraz, Antoine, Newburger, Jane, Ngiam, Kee Yuan, Njoroge, Wanjiku F.M., Norman, James B., Obeid, Jihad, Okoshi, Marina P., Olson, Karen L., Omenn, Gilbert S., Orlova, Nina, Ostasiewski, Brian D., Palmer, Nathan P., Paris, Nicolas, Patel, Lav P., Pedrera-Jiménez, Miguel, Pfaff, Emily R., Pfaff, Ashley C., Pillion, Danielle, Pizzimenti, Sara, Prokosch, Hans U., Prudente, Robson A., Prunotto, Andrea, Quirós-González, Víctor, Ramoni, Rachel B., Raskin, Maryna, Rieg, Siegbert, Roig-Domínguez, Gustavo, Rojo, Pablo, Rubio-Mayo, Paula, Sacchi, Paolo, Sáez, Carlos, Salamanca, Elisa, Samayamuthu, Malarkodi Jebathilagam, Sanchez-Pinto, L. Nelson, Sandrin, Arnaud, Santhanam, Nandhini, Santos, Janaina C.C., Sanz Vidorreta, Fernando J., Savino, Maria, Schriver, Emily R., Schubert, Petra, Schuettler, Juergen, Scudeller, Luigia, Sebire, Neil J., Serrano-Balazote, Pablo, Serre, Patricia, Serret-Larmande, Arnaud, Shah, Mohsin, Hossein Abad, Zahra Shakeri, Silvio, Domenick, Sliz, Piotr, Son, Jiyeon, Sonday, Charles, South, Andrew M., Spiridou, Anastasia, Strasser, Zachary H., Tan, Amelia L.M., Tan, Bryce W.Q., Tan, Byorn W.L., Tanni, Suzana E., Taylor, Deanne M., Terriza-Torres, Ana I., Tibollo, Valentina, Tippmann, Patric, Toh, Emma M.S., Torti, Carlo, Trecarichi, Enrico M., Tseng, Yi-Ju, Vallejos, Andrew K., Varoquaux, Gael, Vella, Margaret E., Verdy, Guillaume, Vie, Jill-Jênn, Visweswaran, Shyam, Vitacca, Michele, Wagholikar, Kavishwar B., Waitman, Lemuel R., Wang, Xuan, Wassermann, Demian, Weber, Griffin M., Wolkewitz, Martin, Wong, Scott, Xia, Zongqi, Xiong, Xin, Ye, Ye, Yehya, Nadir, Yuan, William, Zambelli, Alberto, Zhang, Harrison G., Zo¨ller, Daniela, Zuccaro, Valentina, Zucco, Chiara, Mesa, Rebecca, and Verdy, Guillame
- Published
- 2023
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7. Geospatial Analysis of the Association Between Medicaid Expansion, Minimum Wage Policies, and Alzheimer's Disease Dementia Prevalence in the United States.
- Author
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Mollalo, Abolfazl, Knox, Sara, Meng, Jessica, Benitez, Andreana, Lenert, Leslie A., and Alekseyenko, Alexander V.
- Abstract
Previous studies indicate that increased healthcare access through Medicaid expansion and alleviation of socioeconomic stressors via higher minimum wages improved health outcomes. This study investigates the spatial relationships between the Medicaid expansion, minimum wage policy, and Alzheimer's Disease (AD) dementia prevalence across the US. We used county-level AD dementia prevalence adjusted for age, sex, race/ethnicity, and education. Social Vulnerability Index (SVI) data, Medicaid expansion status, and state minimum wage law status were incorporated from CDC, Kaiser Family Foundation, and US Department of Labor sources, respectively. We employed the Getis-Ord Gi* statistic to identify hotspots and cold spots of AD dementia prevalence at the county level. We compared these locations with the overall SVI scores using univariate analyses. We also assessed the proportion of hot and cold spots at the state level based on Medicaid expansion and minimum wage status using the logistic regression model. The most vulnerable SVI quartile (Q4) had the highest number of hotspots (n = 311, 64.8%), while the least vulnerable quartile (Q1) had the fewest hotspots (n = 22, 4.6%) ( χ
2 = 307.41, p < 0.01). States that adopted Medicaid expansion had a significantly lower proportion of hotspots compared to non-adopting states (p < 0.05), and the non-adopting states had significantly higher odds of having hotspots than adopting states (OR = 2.58, 95% CI: 2.04–3.26, p < 0.001). Conversely, the non-adopting states had significantly lower odds of having cold spots compared to the adopting states (OR = 0.24, 95% CI: 0.19–0.32, p < 0.01). States with minimum wage levels at or below the federal level showed significantly higher odds of having hotspots than states with a minimum wage above the federal level (OR = 1.94, 95% CI: 1.51–2.49, p < 0.01). Our findings suggest significant disparities in AD dementia prevalence related to socioeconomic and policy factors and lay the groundwork for future causal analyses. [ABSTRACT FROM AUTHOR]- Published
- 2024
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8. A pragmatic implementation research study for In Our DNA SC: a protocol to identify multi-level factors that support the implementation of a population-wide genomic screening initiative in diverse populations
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Allen, Caitlin G., Judge, Daniel P., Levin, Elissa, Sterba, Katherine, Hunt, Kelly, Ramos, Paula S., Melvin, Cathy, Wager, Karen, Catchpole, Kenneth, Clinton, Catherine, Ford, Marvella, McMahon, Lori L., and Lenert, Leslie
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- 2022
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9. Barriers Associated with the Implementation of Homework in Youth Mental Health Treatment and Potential Mobile Health Solutions
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Bunnell, Brian E., Nemeth, Lynne S., Lenert, Leslie A., Kazantzis, Nikolaos, Deblinger, Esther, Higgins, Kristen A., and Ruggiero, Kenneth J.
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- 2021
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10. Prescription of Nicotine Replacement Therapy for Hospitalized Tobacco Users.
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Nielsen, Ellen M., Ware, Emily C., Heincelman, Marc, Schreiner, Andrew D., Lenert, Leslie A., and Toll, Benjamin A.
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- 2024
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11. Establishing an infrastructure to optimize the integration of genomics into research: Results from a precision health needs assessment.
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Allen, Caitlin G, Bouchie, Gwendolyn, Judge, Daniel P, Coen, Emma, English, Sarah, Norman, Samantha, Kirchoff, Katie, Ramos, Paula S, Hirschhorn, Julie, Lenert, Leslie, and McMahon, Lori L
- Abstract
Researchers across the translational research continuum have emphasized the importance of integrating genomics into their research program. To date capacity and resources for genomics research have been limited; however, a recent population-wide genomic screening initiative launched at the Medical University of South Carolina in partnership with Helix has rapidly advanced the need to develop appropriate infrastructure for genomics research at our institution. We conducted a survey with researchers from across our institution (n = 36) to assess current knowledge about genomics health, barriers, and facilitators to uptake, and next steps to support translational research using genomics. We also completed 30-minute qualitative interviews with providers and researchers from diverse specialties (n = 8). Quantitative data were analyzed using descriptive analyses. A rapid assessment process was used to develop a preliminary understanding of each interviewee's perspective. These interviews were transcribed and coded to extract themes. The codes included types of research, alignment with precision health, opportunities to incorporate precision health, examples of researchers in the field, barriers, and facilitators to uptake, educational activity suggestions, questions to be answered, and other observations. Themes from the surveys and interviews inform implementation strategies that are applicable not only to our institution, but also to other organizations interested in making genomic data available to researchers to support genomics-informed translational research. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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12. Stressful life events in electronic health records: a scoping review.
- Author
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Scherbakov, Dmitry, Mollalo, Abolfazl, and Lenert, Leslie
- Abstract
Objectives Stressful life events, such as going through divorce, can have an important impact on human health. However, there are challenges in capturing these events in electronic health records (EHR). We conducted a scoping review aimed to answer 2 major questions: how stressful life events are documented in EHR and how they are utilized in research and clinical care. Materials and Methods Three online databases (EBSCOhost platform, PubMed, and Scopus) were searched to identify papers that included information on stressful life events in EHR; paper titles and abstracts were reviewed for relevance by 2 independent reviewers. Results Five hundred fifty-seven unique papers were retrieved, and of these 70 were eligible for data extraction. Most articles (n = 36, 51.4%) were focused on the statistical association between one or several stressful life events and health outcomes, followed by clinical utility (n = 15, 21.4%), extraction of events from free-text notes (n = 12, 17.1%), discussing privacy and other issues of storing life events (n = 5, 7.1%), and new EHR features related to life events (n = 4, 5.7%). The most frequently mentioned stressful life events in the publications were child abuse/neglect, arrest/legal issues, and divorce/relationship breakup. Almost half of the papers (n = 7, 46.7%) that analyzed clinical utility of stressful events were focused on decision support systems for child abuse, while others (n = 7, 46.7%) were discussing interventions related to social determinants of health in general. Discussion and Conclusions Few citations are available on the prevalence and use of stressful life events in EHR reflecting challenges in screening and storing of stressful life events. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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13. Utilities for Prostate Cancer Health States in Men Aged 60 and Older
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Stewart, Susan T., Lenert, Leslie, Bhatnagar, Vibha, and Kaplan, Robert M.
- Published
- 2005
14. Discrete State Analysis for Interpretation of Data from Clinical Trials
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Sugar, Catherine A., James, Gareth M., Lenert, Leslie A., and Rosenheck, Robert A.
- Published
- 2004
15. Use of Willingness to Pay to Study Values for Pharmacotherapies for Migraine Headache
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Lenert, Leslie A.
- Published
- 2003
16. Can Utility-Weighted Health-Related Quality-of-Life Estimates Capture Health Effects of Quality Improvement for Depression?
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Sherbourne, Cathy Donald, Unützer, Jürgen, Schoenbaum, Michael, Duan, Naihua, Lenert, Leslie A., Sturm, Roland, and Wells, Kenneth B.
- Published
- 2001
17. Barriers and facilitators to the implementation of family cancer history collection tools in oncology clinical practices.
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Allen, Caitlin G, Neil, Grace, Halbert, Chanita Hughes, Sterba, Katherine R, Nietert, Paul J, Welch, Brandon, and Lenert, Leslie
- Abstract
Introduction This study aimed to identify barriers and facilitators to the implementation of family cancer history (FCH) collection tools in clinical practices and community settings by assessing clinicians' perceptions of implementing a chatbot interface to collect FCH information and provide personalized results to patients and providers. Objectives By identifying design and implementation features that facilitate tool adoption and integration into clinical workflows, this study can inform future FCH tool development and adoption in healthcare settings. Materials and methods Quantitative data were collected using survey to evaluate the implementation outcomes of acceptability, adoption, appropriateness, feasibility, and sustainability of the chatbot tool for collecting FCH. Semistructured interviews were conducted to gather qualitative data on respondents' experiences using the tool and recommendations for enhancements. Results We completed data collection with 19 providers (n = 9, 47%), clinical staff (n = 5, 26%), administrators (n = 4, 21%), and other staff (n = 1, 5%) affiliated with the NCI Community Oncology Research Program. FCH was systematically collected using a wide range of tools at sites, with information being inserted into the patient's medical record. Participants found the chatbot tool to be highly acceptable, with the tool aligning with existing workflows, and were open to adopting the tool into their practice. Discussion and conclusions We further the evidence base about the appropriateness of scripted chatbots to support FCH collection. Although the tool had strong support, the varying clinical workflows across clinic sites necessitate that future FCH tool development accommodates customizable implementation strategies. Implementation support is necessary to overcome technical and logistical barriers to enhance the uptake of FCH tools in clinical practices and community settings. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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18. Differences in Health Values among Patients, Family Members, and Providers for Outcomes in Schizophrenia
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Lenert, Leslie A., Ziegler, Jennifer, Lee, Tina, Sommi, Roger, and Mahmoud, Ramy
- Published
- 2000
19. Validity and Interpretation of Preference-Based Measures of Health-Related Quality of Life
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Lenert, Leslie and Kaplan, Robert M.
- Published
- 2000
20. Estimation of Utilities for the Effects of Depression from the SF-12
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Lenert, Leslie A., Sherbourne, Cathy D., Sugar, Catherine, and Wells, Kenneth B.
- Published
- 2000
21. The Reliability and Internal Consistency of an Internet-Capable Computer Program for Measuring Utilities
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Lenert, Leslie A.
- Published
- 2000
22. Quality Enhancement Research Initiative for Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome: Framework and Plan
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Bozzette, Samuel A., Phillips, Barbara, Asch, Steven, Gifford, Allen L., Lenert, Leslie, Menke, Terri, Ortiz, Eduardo, Owens, Douglas, and Deyton, Lawrence
- Published
- 2000
23. Feasibility of Quality-of-Life Research on the Internet: A Follow-Up Study
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Treadwell, Jonathan R., Soetikno, Roy M., and Lenert, Leslie A.
- Published
- 1999
24. Associations between Health Status and Utilities Implications for Policy
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Lenert, Leslie A., Treadwell, Jonathan R., and Schwartz, Carolyn E.
- Published
- 1999
25. Variation among Quality-of-Life Surveys: Theory and Practice
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Hornberger, John and Lenert, Leslie A.
- Published
- 1996
26. Teleconsent: A novel approach to obtain informed consent for research
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Welch, Brandon M., Marshall, Elizabeth, Qanungo, Suparna, Aziz, Ayesha, Laken, Marilyn, Lenert, Leslie, and Obeid, Jihad
- Published
- 2016
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27. IRB reliance: An informatics approach
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Obeid, Jihad S., Alexander, Randall W., Gentilin, Stephanie M., White, Brigette, Turley, Christine B., Brady, Kathleen T., and Lenert, Leslie A.
- Published
- 2016
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28. Correction to: Automatically identifying social isolation from clinical narratives for patients with prostate Cancer
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Zhu, Vivienne J., Lenert, Leslie A., Bunnell, Brian E., Obeid, Jihad S., Jefferson, Melanie, and Halbert, Chanita Hughes
- Published
- 2019
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29. Automatically identifying social isolation from clinical narratives for patients with prostate Cancer
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Zhu, Vivienne J, Lenert, Leslie A, Bunnell, Brian E, Obeid, Jihad S, Jefferson, Melanie, and Halbert, Chanita Hughes
- Published
- 2019
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30. Higher-Dose Fluvoxamine and Time to Sustained Recovery in Outpatients With COVID-19: The ACTIV-6 Randomized Clinical Trial.
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Stewart, Thomas G., Rebolledo, Paulina A., Mourad, Ahmad, Lindsell, Christopher J., Boulware, David R., McCarthy, Matthew W., Thicklin, Florence, Garcia del Sol, Idania T., Bramante, Carolyn T., Lenert, Leslie A., Lim, Stephen, Williamson, John C., Cardona, Orlando Quintero, Scott, Jake, Schwasinger-Schmidt, Tiffany, Ginde, Adit A., Castro, Mario, Jayaweera, Dushyantha, Sulkowski, Mark, and Gentile, Nina
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COVID-19 ,COVID-19 vaccines ,CLINICAL trials ,MEDICAL care use ,DISEASE progression - Abstract
Key Points: Question: Does 100 mg of fluvoxamine twice daily for 13 days, compared with placebo, shorten symptom duration among outpatient adults (aged ≥30 years) with symptomatic mild to moderate COVID-19? Findings: In this platform randomized clinical trial with 1175 US participants enrolled during the time that Omicron COVID-19 subvariants were circulating, there was no reportable difference in the time to sustained recovery between fluvoxamine and placebo groups (adjusted hazard ratio, 0.99 [95% credible interval, 0.89-1.09]; P for efficacy =.40). Median time to sustained recovery was 10 days (95% CI, 10-11) in both the intervention and placebo group. Meaning: Fluvoxamine, 100 mg twice daily, does not shorten the duration of symptoms in outpatient adults with mild to moderate COVID-19. Importance: The effect of higher-dose fluvoxamine in reducing symptom duration among outpatients with mild to moderate COVID-19 remains uncertain. Objective: To assess the effectiveness of fluvoxamine, 100 mg twice daily, compared with placebo, for treating mild to moderate COVID-19. Design, Setting, and Participants: The ACTIV-6 platform randomized clinical trial aims to evaluate repurposed medications for mild to moderate COVID-19. Between August 25, 2022, and January 20, 2023, a total of 1175 participants were enrolled at 103 US sites for evaluating fluvoxamine; participants were 30 years or older with confirmed SARS-CoV-2 infection and at least 2 acute COVID-19 symptoms for 7 days or less. Interventions: Participants were randomized to receive fluvoxamine, 50 mg twice daily on day 1 followed by 100 mg twice daily for 12 additional days (n = 601), or placebo (n = 607). Main Outcomes and Measures: The primary outcome was time to sustained recovery (defined as at least 3 consecutive days without symptoms). Secondary outcomes included time to death; time to hospitalization or death; a composite of hospitalization, urgent care visit, emergency department visit, or death; COVID-19 clinical progression scale score; and difference in mean time unwell. Follow-up occurred through day 28. Results: Among 1208 participants who were randomized and received the study drug, the median (IQR) age was 50 (40-60) years, 65.8% were women, 45.5% identified as Hispanic/Latino, and 76.8% reported receiving at least 2 doses of a SARS-CoV-2 vaccine. Among 589 participants who received fluvoxamine and 586 who received placebo included in the primary analysis, differences in time to sustained recovery were not observed (adjusted hazard ratio [HR], 0.99 [95% credible interval, 0.89-1.09]; P for efficacy =.40]). Additionally, unadjusted median time to sustained recovery was 10 (95% CI, 10-11) days in both the intervention and placebo groups. No deaths were reported. Thirty-five participants reported health care use events (a priori defined as death, hospitalization, or emergency department/urgent care visit): 14 in the fluvoxamine group compared with 21 in the placebo group (HR, 0.69 [95% credible interval, 0.27-1.21]; P for efficacy =.86) There were 7 serious adverse events in 6 participants (2 with fluvoxamine and 4 with placebo) but no deaths. Conclusions and Relevance: Among outpatients with mild to moderate COVID-19, treatment with fluvoxamine does not reduce duration of COVID-19 symptoms. Trial Registration: ClinicalTrials.gov Identifier: NCT04885530 This randomized study examines the effect of higher-dose fluvoxamine on time to sustained recovery from mild to moderate COVID-19 or progression to severe disease in nonhospitalized adults. [ABSTRACT FROM AUTHOR]
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- 2023
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31. Enhanced phenotypes for identifying opioid overdose in emergency department visit electronic health record data.
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Ward, Ralph, Obeid, Jihad S., Jennings, Lindsey, Szwast, Elizabeth, Hayes, William Garrett, Pipaliya, Royal, Bailey, Cameron, Faul, Skylar, Polyak, Brianna, Baker, George Hamilton, McCauley, Jenna L., and Lenert, Leslie A.
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- 2023
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32. International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality
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Weber, Griffin, Hong, Chuan, Xia, Zongqi, Palmer, Nathan, Avillach, Paul, L’yi, Sehi, Keller, Mark, Murphy, Shawn, Gutiérrez-Sacristán, Alba, Bonzel, Clara-Lea, Serret-Larmande, Arnaud, Neuraz, Antoine, Omenn, Gilbert, Visweswaran, Shyam, Klann, Jeffrey, South, Andrew, Loh, Ne Hooi Will, Cannataro, Mario, Beaulieu-Jones, Brett, Bellazzi, Riccardo, Agapito, Giuseppe, Alessiani, Mario, Aronow, Bruce, Bell, Douglas, Benoit, Vincent, Bourgeois, Florence, Chiovato, Luca, Cho, Kelly, Dagliati, Arianna, Duvall, Scott, Barrio, Noelia García, Hanauer, David, Ho, Yuk-Lam, Holmes, John, Issitt, Richard, Liu, Molei, Luo, Yuan, Lynch, Kristine, Maidlow, Sarah, Malovini, Alberto, Mandl, Kenneth, Mao, Chengsheng, Matheny, Michael, Moore, Jason, Morris, Jeffrey, Morris, Michele, Mowery, Danielle, Ngiam, Kee Yuan, Patel, Lav, Pedrera Jiménez, Miguel, Ramoni, Rachel, Schriver, Emily, Schubert, Petra, Balazote, Pablo Serrano, Spiridou, Anastasia, Tan, Amelia, Tan, Byorn, Tibollo, Valentina, Torti, Carlo, Trecarichi, Enrico, Wang, Xuan, Aaron, James, Albayrak, Adem, Albi, Giuseppe, Balshi, James, Alloni, Anna, Amendola, Danilo, Angoulvant, François, Anthony, Li, Ashraf, Fatima, Atz, Andrew, Azevedo, Paula, Bellasi, Antonio, Beraghi, Michele, Bernal-Sobrino, José Luis, Bernaux, Mélodie, Bey, Romain, Bhatnagar, Surbhi, Blanco-Martínez, Alvar, Boeker, Martin, Booth, John, Bosari, Silvano, Bradford, Robert, Brat, Gabriel, Bréant, Stéphane, Brown, Nicholas, Bruno, Raffaele, Bryant, William, Bucalo, Mauro, Bucholz, Emily, Burgun, Anita, Cai, Tianxi, Carmona, Aldo, Caucheteux, Charlotte, Champ, Julien, Chen, Krista, Chen, Jin, Chiudinelli, Lorenzo, Cimino, James, Colicchio, Tiago, Cormont, Sylvie, Cossin, Sébastien, Craig, Jean, Cruz-Bermúdez, Juan Luis, Cruz-Rojo, Jaime, Daniar, Mohamad, Daniel, Christel, Das, Priyam, Devkota, Batsal, Garmire, Lana, Dionne, Audrey, Duan, Rui, Dubiel, Julien, Esteve, Loic, Estiri, Hossein, Fan, Shirley, Follett, Robert, Ganslandt, Thomas, García-Barrio, Noelia, Gehlenborg, Nils, Getzen, Emily, Geva, Alon, Gradinger, Tobias, Gramfort, Alexandre, Griffier, Romain, Griffon, Nicolas, Grisel, Olivier, Han, Larry, Haverkamp, Christian, Key, Daniel, Hazard, Derek, He, Bing, Henderson, Darren, Hilka, Martin, Huling, Kenneth, Hutch, Meghan, Jannot, Anne Sophie, Jouhet, Vianney, Kavuluru, Ramakanth, Kennedy, Chris, Kernan, Kate, Kirchoff, Katie, Kohane, Isaac, Krantz, Ian, Kraska, Detlef, Krishnamurthy, Ashok, Le, Trang, Leblanc, Judith, Lemaitre, Guillaume, Lenert, Leslie, Leprovost, Damien, Long, Qi, Lozano-Zahonero, Sara, Mahmood, Sadiqa, Makoudjou, Adeline, Maram, Anupama, Martel, Patricia, Martins, Marcelo, Marwaha, Jayson, Masino, Aaron, Mazzitelli, Maria, Mensch, Arthur, Milano, Marianna, Minicucci, Marcos, Moal, Bertrand, Ahooyi, Taha Mohseni, Moraleda, Cinta, Moshal, Karyn, Mousavi, Sajad, Murad, Douglas, Naughton, Thomas, Neto, Carlos Tadeu Breda, Newburger, Jane, Njoroge, Wanjiku, Norman, James, Obeid, Jihad, Okoshi, Marina, Olson, Karen, Orlova, Nina, Ostasiewski, Brian, Paris, Nicolas, Pedrera-Jiménez, Miguel, Pfaff, Ashley, Pfaff, Emily, Pillion, Danielle, Pizzimenti, Sara, Prokosch, Hans, Prudente, Robson, Prunotto, Andrea, Quirós-González, Víctor, Raskin, Maryna, Rieg, Siegbert, Roig-Domínguez, Gustavo, Rojo, Pablo, Rubio-Mayo, Paula, Sacchi, Paolo, Sáez, Carlos, Salamanca, Elisa, Samayamuthu, Malarkodi Jebathilagam, Sanchez-Pinto, L. Nelson, Sandrin, Arnaud, Santhanam, Nandhini, Santos, Janaina, Sanz Vidorreta, Fernando, Savino, Maria, Schuettler, Juergen, Scudeller, Luigia, Sebire, Neil, Serrano-Balazote, Pablo, Serre, Patricia, Shah, Mohsin, Abad, Zahra Shakeri Hossein, Silvio, Domenick, Sliz, Piotr, Son, Jiyeon, Sonday, Charles, Sperotto, Francesca, Strasser, Zachary, Tan, Bryce, Tanni, Suzana, Taylor, Deanne, Terriza-Torres, Ana, Tippmann, Patric, Toh, Emma, Tseng, Yi-Ju, Vallejos, Andrew, Varoquaux, Gael, Vella, Margaret, Verdy, Guillaume, Vie, Jill-Jênn, Vitacca, Michele, Wagholikar, Kavishwar, Waitman, Lemuel, Wassermann, Demian, Wolkewitz, Martin, Wong, Scott, Xiong, Xin, Ye, Ye, Yehya, Nadir, Yuan, William, Zambelli, Alberto, Zhang, Harrison, Zöller, Daniela, Zuccaro, Valentina, Zucco, Chiara, Harvard Medical School [Boston] (HMS), University of Pittsburgh (PITT), Pennsylvania Commonwealth System of Higher Education (PCSHE), Massachusetts General Hospital [Boston], Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Service d'informatique médicale et biostatistiques [CHU Necker], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Université Paris Cité (UPCité), Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), University of Michigan [Ann Arbor], University of Michigan System, Wake Forest School of Medicine [Winston-Salem], Wake Forest Baptist Medical Center, National University Health System [Singapore] (NUHS), Università degli Studi 'Magna Graecia' di Catanzaro = University of Catanzaro (UMG), Università degli Studi di Pavia = University of Pavia (UNIPV), Istituti Clinici Scientifici Maugeri [Pavia] (IRCCS Pavia - ICS Maugeri), ASST Pavia, University of Cincinnati (UC), University of California [Los Angeles] (UCLA), University of California (UC), VA Boston Healthcare System, Hospital Universitario 12 de Octubre [Madrid], University of Pennsylvania, Great Ormond Street Hospital for Children [London] (GOSH), Harvard School of Public Health, Northwestern University [Chicago, Ill. USA], VA Salt Lake City Health Care System, Boston Children's Hospital, University of Kansas [Kansas City], and National University Hospital [Singapore] (NUH)
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Health Information Management ,Medicine (miscellaneous) ,Health Informatics ,[SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie ,Computer Science Applications - Abstract
Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach.
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- 2022
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33. Chapter 27 - CDS for public health
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Lenert, Leslie A.
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- 2023
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34. Anticipating adaptation: tracking the impact of planned and unplanned adaptations during the implementation of a complex population-based genomic screening program.
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Allen, Caitlin G, Judge, Daniel P, Nietert, Paul J, Hunt, Kelly J, Jackson, Amy, Gallegos, Sam, Sterba, Katherine R, Ramos, Paula S, Melvin, Cathy L, Wager, Karen, Catchpole, Ken, Ford, Marvella, McMahon, Lori, and Lenert, Leslie
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In 2021, the Medical University of South Carolina (MUSC) launched In Our DNA SC. This large-scale initiative will screen 100,000 individuals in South Carolina for three preventable hereditary conditions that impact approximately two million people in the USA but often go undetected. In anticipation of inevitable changes to the delivery of this complex initiative, we developed an approach to track and assess the impact of evaluate adaptations made during the pilot phase of program implementation. We used a modified version of the Framework for Reporting Adaptations and Modification-Enhanced (FRAME) and Adaptations to code adaptations made during the 3-month pilot phase of In Our DNA SC. Adaptations were documented in real-time using a REDCap database. We used segmented linear regression models to independently test three hypotheses about the impact of adaptations on program reach (rate of enrollment in the program, rate of messages viewed) and implementation (rate of samples collected) 7 days pre- and post-adaptation. Effectiveness was assessed using qualitative observations. Ten adaptations occurred during the pilot phase of program implementation. Most adaptations (60%) were designed to increase the number and type of patient contacted (reach). Adaptations were primarily made based on knowledge and experience (40%) or from quality improvement data (30%). Of the three adaptations designed to increase reach, shortening the recruitment message potential patients received significantly increased the average rate of invitations viewed by 7.3% (p = 0.0106). There was no effect of adaptations on implementation (number of DNA samples collected). Qualitative findings support improvement in effectiveness of the intervention after shortening the consent form and short-term positive impact on uptake of the intervention as measured by team member's participation. Our approach to tracking adaptations of In Our DNA SC allowed our team to quantify the utility of modifications, make decisions about pursuing the adaptation, and understand consequences of the change. Streamlining tools for tracking and responding to adaptations can help monitor the incremental impact of interventions to support continued learning and problem solving for complex interventions being delivered in health systems based on real-time data. [ABSTRACT FROM AUTHOR]
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- 2023
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35. Strengths, weaknesses, opportunities, and threats for the nation's public health information systems infrastructure: synthesis of discussions from the 2022 ACMI Symposium.
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Acharya, Jessica C, Staes, Catherine, Allen, Katie S, Hartsell, Joel, Cullen, Theresa A, Lenert, Leslie, Rucker, Donald W, Lehmann, Harold P, and Dixon, Brian E
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Objective The annual American College of Medical Informatics (ACMI) symposium focused discussion on the national public health information systems (PHIS) infrastructure to support public health goals. The objective of this article is to present the strengths, weaknesses, threats, and opportunities (SWOT) identified by public health and informatics leaders in attendance. Materials and Methods The Symposium provided a venue for experts in biomedical informatics and public health to brainstorm, identify, and discuss top PHIS challenges. Two conceptual frameworks, SWOT and the Informatics Stack, guided discussion and were used to organize factors and themes identified through a qualitative approach. Results A total of 57 unique factors related to the current PHIS were identified, including 9 strengths, 22 weaknesses, 14 opportunities, and 14 threats, which were consolidated into 22 themes according to the Stack. Most themes (68%) clustered at the top of the Stack. Three overarching opportunities were especially prominent: (1) addressing the needs for sustainable funding, (2) leveraging existing infrastructure and processes for information exchange and system development that meets public health goals, and (3) preparing the public health workforce to benefit from available resources. Discussion The PHIS is unarguably overdue for a strategically designed, technology-enabled, information infrastructure for delivering day-to-day essential public health services and to respond effectively to public health emergencies. Conclusion Most of the themes identified concerned context, people, and processes rather than technical elements. We recommend that public health leadership consider the possible actions and leverage informatics expertise as we collectively prepare for the future. [ABSTRACT FROM AUTHOR]
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- 2023
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36. Enhancing the nation's public health information infrastructure: a report from the ACMI symposium.
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Dixon, Brian E, Staes, Catherine, Acharya, Jessica, Allen, Katie S, Hartsell, Joel, Cullen, Theresa, Lenert, Leslie, Rucker, Donald W, and Lehmann, Harold
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The COVID-19 pandemic exposed multiple weaknesses in the nation's public health system. Therefore, the American College of Medical Informatics selected "Rebuilding the Nation's Public Health Informatics Infrastructure" as the theme for its annual symposium. Experts in biomedical informatics and public health discussed strategies to strengthen the US public health information infrastructure through policy, education, research, and development. This article summarizes policy recommendations for the biomedical informatics community postpandemic. First, the nation must perceive the health data infrastructure to be a matter of national security. The nation must further invest significantly more in its health data infrastructure. Investments should include the education and training of the public health workforce as informaticians in this domain are currently limited. Finally, investments should strengthen and expand health data utilities that increasingly play a critical role in exchanging information across public health and healthcare organizations. [ABSTRACT FROM AUTHOR]
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- 2023
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37. Could an artificial intelligence approach to prior authorization be more human?
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Lenert, Leslie A, Lane, Steven, and Wehbe, Ramsey
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Prior authorization (PA) may be a necessary evil within the healthcare system, contributing to physician burnout and delaying necessary care, but also allowing payers to prevent wasting resources on redundant, expensive, and/or ineffective care. PA has become an "informatics issue" with the rise of automated methods for PA review, championed in the Health Level 7 International's (HL7's) DaVinci Project. DaVinci proposes using rule-based methods to automate PA, a time-tested strategy with known limitations. This article proposes an alternative that may be more human-centric, using artificial intelligence (AI) methods for the computation of authorization decisions. We believe that by combining modern approaches for accessing and exchanging existing electronic health data with AI methods tailored to reflect the judgments of expert panels that include patient representatives, and refined with "few shot" learning approaches to prevent bias, we could create a just and efficient process that serves the interests of society as a whole. Efficient simulation of human appropriateness assessments from existing data using AI methods could eliminate burdens and bottlenecks while preserving PA's benefits as a tool to limit inappropriate care. [ABSTRACT FROM AUTHOR]
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- 2023
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38. The effectiveness of a noninterruptive alert to increase prescription of take-home naloxone in emergency departments.
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Jennings, Lindsey K, Ward, Ralph, Pekar, Ekaterina, Szwast, Elizabeth, Sox, Luke, Hying, Joseph, Mccauley, Jenna, Obeid, Jihad S, and Lenert, Leslie A
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Objective Opioid-related overdose (OD) deaths continue to increase. Take-home naloxone (THN), after treatment for an OD in an emergency department (ED), is a recommended but under-utilized practice. To promote THN prescription, we developed a noninterruptive decision support intervention that combined a detailed OD documentation template with a reminder to use the template that is automatically inserted into a provider's note by decision rules. We studied the impact of the combined intervention on THN prescribing in a longitudinal observational study. Methods ED encounters involving an OD were reviewed before and after implementation of the reminder embedded in the physicians' note to use an advanced OD documentation template for changes in: (1) use of the template and (2) prescription of THN. Chi square tests and interrupted time series analyses were used to assess the impact. Usability and satisfaction were measured using the System Usability Scale (SUS) and the Net Promoter Score. Results In 736 OD cases defined by International Classification of Disease version 10 diagnosis codes (247 prereminder and 489 postreminder), the documentation template was used in 0.0% and 21.3%, respectively (P < .0001). The sensitivity and specificity of the reminder for OD cases were 95.9% and 99.8%, respectively. Use of the documentation template led to twice the rate of prescribing of THN (25.7% vs 50.0%, P < .001). Of 19 providers responding to the survey, 74% of SUS responses were in the good-to-excellent range and 53% of providers were Net Promoters. Conclusions A noninterruptive decision support intervention was associated with higher THN prescribing in a pre-post study across a multiinstitution health system. [ABSTRACT FROM AUTHOR]
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- 2023
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39. Toward evidence-based Internet interventions: A Spanish/English Web site for international smoking cessation trials
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Muñoz, Ricardo F., Lenert, Leslie L., Delucchi, Kevin, Stoddard, Jacqueline, Perez, John E., Penilla, Carlos, and Pérez-Stable, Eliseo J.
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- 2006
40. Evolving phenotypes of non-hospitalized patients that indicate long COVID
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ESTIRI, Hossein, Strasser, Zachary, Brat, Gabriel, Semenov, Yevgeniy, Patel, Chirag, Murphy, Shawn, Aaron, James, Agapito, Giuseppe, Albayrak, Adem, Alessiani, Mario, Amendola, Danilo, Anthony, Li, Aronow, Bruce, Ashraf, Fatima, Atz, Andrew, Avillach, Paul, Balshi, James, Beaulieu-Jones, Brett, Bell, Douglas, Bellasi, Antonio, Bellazzi, Riccardo, Benoit, Vincent, Beraghi, Michele, Sobrino, José Luis Bernal, Bernaux, Mélodie, Bey, Romain, Martínez, Alvar Blanco, Boeker, Martin, Bonzel, Clara-Lea, Booth, John, Bosari, Silvano, Bourgeois, Florence, Bradford, Robert, Bréant, Stéphane, Brown, Nicholas, Bryant, William, Bucalo, Mauro, Burgun, Anita, Cai, Tianxi, Cannataro, Mario, Carmona, Aldo, Caucheteux, Charlotte, Champ, Julien, Chen, Jin, Chen, Krista, Chiovato, Luca, Chiudinelli, Lorenzo, Cho, Kelly, Cimino, James, Colicchio, Tiago, Cormont, Sylvie, COSSIN, Sébastien, Craig, Jean, Bermúdez, Juan Luis Cruz, Rojo, Jaime Cruz, Dagliati, Arianna, Daniar, Mohamad, Daniel, Christel, Davoudi, Anahita, Devkota, Batsal, Dubiel, Julien, Esteve, Loic, Fan, Shirley, Follett, Robert, Gaiolla, Paula, Ganslandt, Thomas, Barrio, Noelia García, Garmire, Lana, Gehlenborg, Nils, GEVA, Alon, Gradinger, Tobias, Gramfort, Alexandre, Griffier, Romain, Griffon, Nicolas, Grisel, Olivier, Gutiérrez-Sacristán, Alba, Hanauer, David, Haverkamp, Christian, He, Bing, Henderson, Darren, Hilka, Martin, Holmes, John, Hong, Chuan, Horki, Petar, Huling, Kenneth, HUTCH, Meghan, Issitt, Richard, Jannot, Anne Sophie, Jouhet, Vianney, Keller, Mark, Kirchoff, Katie, Klann, Jeffrey, Kohane, Isaac, Krantz, Ian, Kraska, Detlef, Krishnamurthy, Ashok, L’Yi, Sehi, Le, Trang, Leblanc, Judith, Leite, Andressa, Lemaitre, Guillaume, Lenert, Leslie, Leprovost, Damien, Liu, Molei, LOH, Ne Hooi Will, Lozano-Zahonero, Sara, Luo, Yuan, Lynch, Kristine, Mahmood, Sadiqa, Maidlow, Sarah, Malovini, Alberto, Mandl, Kenneth, Mao, Chengsheng, Maram, Anupama, Martel, Patricia, Masino, Aaron, Mazzitelli, Maria, Mensch, Arthur, Milano, Marianna, Minicucci, Marcos, Moal, Bertrand, Moore, Jason, Moraleda, Cinta, Morris, Jeffrey, MORRIS, Michele, Moshal, Karyn, Mousavi, Sajad, Mowery, Danielle, Murad, Douglas, Naughton, Thomas, Neuraz, Antoine, Ngiam, Kee Yuan, Norman, James, Obeid, Jihad, Okoshi, Marina, Olson, Karen, Omenn, Gilbert, Orlova, Nina, Ostasiewski, Brian, Palmer, Nathan, Paris, Nicolas, Patel, Lav, Jimenez, Miguel Pedrera, Pfaff, Emily, Pillion, Danielle, Prokosch, Hans, Prudente, Robson, González, Víctor Quirós, Ramoni, Rachel, Raskin, Maryna, RIEG, Siegbert, Domínguez, Gustavo Roig, Rojo, Pablo, Sáez, Carlos, Salamanca, Elisa, Samayamuthu, Malarkodi, Sandrin, Arnaud, Santos, Janaina, Savino, Maria, SCHRIVER, Emily, Schubert, Petra, Schuettler, Juergen, Scudeller, Luigia, Sebire, Neil, Balazote, Pablo Serrano, Serre, Patricia, Serret-Larmande, Arnaud, Shakeri, Zahra, Silvio, Domenick, Sliz, Piotr, SON, Jiyeon, Sonday, Charles, South, Andrew, Spiridou, Anastasia, Tan, Amelia, Tan, Bryce, Tan, Byorn, Tanni, Suzana, Taylor, Deanne, Terriza Torres, Ana, Tibollo, Valentina, Tippmann, Patric, Torti, Carlo, Trecarichi, Enrico, Tseng, Yi-Ju, Vallejos, Andrew, Varoquaux, Gael, Vella, Margaret, Verdy, Guillaume, Vie, Jill-Jênn, Visweswaran, Shyam, Vitacca, Michele, Wagholikar, Kavishwar, Waitman, Lemuel, Wang, Xuan, Wassermann, Demian, Weber, Griffin, XIA, Zongqi, Yehya, Nadir, Yuan, William, Zambelli, Alberto, Zhang, Harrison, Zoeller, Daniel, Zucco, Chiara, Massachusetts General Hospital [Boston], Harvard Medical School [Boston] (HMS), Service d'informatique médicale et biostatistiques [CHU Necker], CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP), Université de Paris - UFR Médecine Paris Centre [Santé] (UP Médecine Paris Centre), Université de Paris (UP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPC)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPC), Université Paris Cité - UFR Médecine Paris Centre [Santé] (UPC Médecine Paris Centre), Université Paris Cité (UPC), This work was supported by the National Human Genome Research Institute grant 3U01HG008685-05S2 and the National Library of Medicine grant T15LM007092., École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), UFR Médecine [Santé] - Université Paris Cité (UFR Médecine UPCité), and Université Paris Cité (UPCité)
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medicine.medical_specialty ,Neurological disorder ,Chest pain ,MESH: Phenotype ,Article ,03 medical and health sciences ,0302 clinical medicine ,Post-Acute COVID-19 Syndrome ,Diabetes mellitus ,Internal medicine ,Machine learning ,medicine ,Chronic fatigue syndrome ,Humans ,Electronic health records ,Post-acute sequelae of SARS-CoV-2 ,MESH: COVID-19 ,030304 developmental biology ,Retrospective Studies ,0303 health sciences ,MESH: Humans ,business.industry ,Medical record ,Type 2 Diabetes Mellitus ,COVID-19 ,Retrospective cohort study ,MESH: Retrospective Studies ,General Medicine ,medicine.disease ,3. Good health ,Dysgeusia ,Phenotypes ,Phenotype ,Medicine ,[SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie ,medicine.symptom ,business ,030217 neurology & neurosurgery ,Research Article ,Cohort study - Abstract
Background For some SARS-CoV-2 survivors, recovery from the acute phase of the infection has been grueling with lingering effects. Many of the symptoms characterized as the post-acute sequelae of COVID-19 (PASC) could have multiple causes or are similarly seen in non-COVID patients. Accurate identification of PASC phenotypes will be important to guide future research and help the healthcare system focus its efforts and resources on adequately controlled age- and gender-specific sequelae of a COVID-19 infection. Methods In this retrospective electronic health record (EHR) cohort study, we applied a computational framework for knowledge discovery from clinical data, MLHO, to identify phenotypes that positively associate with a past positive reverse transcription-polymerase chain reaction (RT-PCR) test for COVID-19. We evaluated the post-test phenotypes in two temporal windows at 3–6 and 6–9 months after the test and by age and gender. Data from longitudinal diagnosis records stored in EHRs from Mass General Brigham in the Boston Metropolitan Area was used for the analyses. Statistical analyses were performed on data from March 2020 to June 2021. Study participants included over 96 thousand patients who had tested positive or negative for COVID-19 and were not hospitalized. Results We identified 33 phenotypes among different age/gender cohorts or time windows that were positively associated with past SARS-CoV-2 infection. All identified phenotypes were newly recorded in patients’ medical records 2 months or longer after a COVID-19 RT-PCR test in non-hospitalized patients regardless of the test result. Among these phenotypes, a new diagnosis record for anosmia and dysgeusia (OR 2.60, 95% CI [1.94–3.46]), alopecia (OR 3.09, 95% CI [2.53–3.76]), chest pain (OR 1.27, 95% CI [1.09–1.48]), chronic fatigue syndrome (OR 2.60, 95% CI [1.22–2.10]), shortness of breath (OR 1.41, 95% CI [1.22–1.64]), pneumonia (OR 1.66, 95% CI [1.28–2.16]), and type 2 diabetes mellitus (OR 1.41, 95% CI [1.22–1.64]) is one of the most significant indicators of a past COVID-19 infection. Additionally, more new phenotypes were found with increased confidence among the cohorts who were younger than 65. Conclusions The findings of this study confirm many of the post-COVID-19 symptoms and suggest that a variety of new diagnoses, including new diabetes mellitus and neurological disorder diagnoses, are more common among those with a history of COVID-19 than those without the infection. Additionally, more than 63% of PASC phenotypes were observed in patients under 65 years of age, pointing out the importance of vaccination to minimize the risk of debilitating post-acute sequelae of COVID-19 among younger adults.
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- 2021
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41. VACtrac: enhancing access immunization registry data for population outreach using the Bulk Fast Healthcare Interoperable Resource (FHIR) protocol.
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Lenert, Leslie, Jacobs, Jeff, Agnew, James, Ding, Wei, Kirchoff, Katie, Weatherston, Duncan, and Deans, Kenneth
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COVID-19 vaccination uptake has been suboptimal, even in high-risk populations. New approaches are needed to bring vaccination data to the groups leading outreach efforts. This article describes work to make state-level vaccination data more accessible by extending the Bulk Fast Healthcare Interoperability Resource (FHIR) standard to better support the repeated retrieval of vaccination data for coordinated outreach efforts. We also describe a corresponding low-foot-print software for population outreach that automates repeated checks of state-level immunization data and prioritizes outreach by social determinants of health. Together this software offers an integrated approach to addressing vaccination gaps. Several extensions to the Bulk FHIR protocol were needed to support bulk query of immunization records. These are described in detail. The results of a pilot study, using the outreach tool to target a population of 1500 patients are also described. The results confirmed the limitations of current patient-by-patient approach for querying state immunizations systems for population data and the feasibility of a Bulk FHIR approach. [ABSTRACT FROM AUTHOR]
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- 2023
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42. Careful experiments advance the science of informatics
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Lenert, Leslie A and Taft, Tersa
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- 2015
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43. Effects of electronic health record use on the exam room communication skills of resident physicians: a randomized within-subjects study
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Taft, Teresa, Lenert, Leslie, Sakaguchi, Farrant, Stoddard, Gregory, and Milne, Caroline
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- 2015
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44. Evaluation of an Internet-Based Disease Trajectory Decision Tool for Prostate Cancer Screening
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Bhatnagar, Vibha, Frosch, Dominick L., Tally, Steven R., Hamori, Charles J., Lenert, Leslie, and Kaplan, Robert M.
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- 2009
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45. Electronic Support for Public Health: Validated Case Finding and Reporting for Notifiable Diseases Using Electronic Medical Data
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Lazarus, Ross, Klompas, Michael, Campion, Francis X., McNabb, Scott J.N., Hou, Xuanlin, Daniel, James, Haney, Gillian, DeMaria, Alfred, Lenert, Leslie, and Platt, Richard
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- 2009
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46. International Analysis of Electronic Health Records of Children and Youth Hospitalized With COVID-19 Infection in 6 Countries
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Bourgeois, Florence, Gutiérrez-Sacristán, Alba, Keller, Mark, Liu, Molei, Hong, Chuan, Bonzel, Clara-Lea, Tan, Amelia, Aronow, Bruce, Boeker, Martin, Booth, John, Cruz Rojo, Jaime, Devkota, Batsal, García Barrio, Noelia, Gehlenborg, Nils, Geva, Alon, Hanauer, David, Hutch, Meghan, Issitt, Richard, Klann, Jeffrey, Luo, Yuan, Mandl, Kenneth, Mao, Chengsheng, Moal, Bertrand, Moshal, Karyn, Murphy, Shawn, Neuraz, Antoine, Ngiam, Kee Yuan, Omenn, Gilbert, Patel, Lav, Jiménez, Miguel Pedrera, Sebire, Neil, Balazote, Pablo Serrano, Serret-Larmande, Arnaud, South, Andrew, Spiridou, Anastasia, Taylor, Deanne, Tippmann, Patric, Visweswaran, Shyam, Weber, Griffin, Kohane, Isaac, Cai, Tianxi, Avillach, Paul, Cruz-Rojo, Jaime, García-Barrio, Noelia, Pedrera-Jiménez, Miguel, Serrano-Balazote, Pablo, Aaron, James, Agapito, Giuseppe, Albayrak, Adem, Alessiani, Mario, Amendola, Danilo, Angoulvant, François, Anthony, Li Llj, Atz, Andrew, Balshi, James, Beaulieu-Jones, Brett, Bell, Douglas, Bellasi, Antonio, Bellazzi, Riccardo, Benoit, Vincent, Beraghi, Michele, Bernal Sobrino, José Luis, Bernaux, Mélodie, Bey, Romain, Blanco Martínez, Alvar, Bosari, Silvano, Bradford, Robert, Brat, Gabriel, Bréant, Stéphane, Brown, Nicholas, Bryant, William, Bucalo, Mauro, Burgun, Anita, Cannataro, Mario, Carmona, Aldo, Caucheteux, Charlotte, Champ, Julien, Chen, Krista, Chen, Jin, Chiovato, Luca, Chiudinelli, Lorenzo, Cimino, James, Colicchio, Tiago, Cormont, Sylvie, Cossin, Sébastien, Craig, Jean, Cruz Bermúdez, Juan Luis, Dagliati, Arianna, Daniar, Mohamad, Daniel, Christel, Davoudi, Anahita, Dubiel, Julien, Duvall, Scott, Esteve, Loic, Fan, Shirley, Follett, Robert, Gaiolla, Paula Sa, Ganslandt, Thomas, Garmire, Lana, Gradinger, Tobias, Gramfort, Alexandre, Griffier, Romain, Griffon, Nicolas, Grisel, Olivier, Haverkamp, Christian, He, Bing, Henderson, Darren, Hilka, Martin, Holmes, John, Horki, Petar, Huling, Kenneth, Jannot, Anne Sophie, Jouhet, Vianney, Kavuluru, Ramakanth, Kirchoff, Katie, Krantz, Ian, Kraska, Detlef, Krishnamurthy, Ashok, L'Yi, Sehi, Le, Trang, Leblanc, Judith, Leite, Andressa Rr, Lemaitre, Guillaume, Lenert, Leslie, Leprovost, Damien, Loh, Ne Hooi Will, Lynch, Kristine, Mahmood, Sadiqa, Maidlow, Sarah, Malovini, Alberto, Maram, Anupama, Martel, Patricia, Masino, Aaron, Matheny, Michael, Maulhardt, Thomas, Mazzitelli, Maria, Mcduffie, Michael, Mensch, Arthur, Milano, Marianna, Minicucci, Marcos, Moore, Jason, Moraleda, Cinta, Morris, Jeffrey, Morris, Michele, Mousavi, Sajad, Mowery, Danielle, Murad, Douglas, Naughton, Thomas, Norman, James, Obeid, Jihad, Okoshi, Marina, Olson, Karen, Orlova, Nina, Ostasiewski, Brian, Palmer, Nathan, Paris, Nicolas, Pfaff, Emily, Pillion, Danielle, Prokosch, Hans, Prudente, Robson, Quirós González, Víctor, Ramoni, Rachel, Raskin, Maryna, Rieg, Siegbert, Roig Domínguez, Gustavo, Rojo, Pablo, Sáez, Carlos, Salamanca, Elisa, Samayamuthu, Malarkodi, Sandrin, Arnaud, Santos, Janaina Cc, Savino, Maria, Schriver, Emily, Schuettler, Juergen, Scudeller, Luigia, Serre, Patricia, Silvio, Domenick, Sliz, Piotr, Son, Jiyeon, Sonday, Charles, Tan, Bryce Wq, Tan, Byorn Wl, Tanni, Suzana, Terriza Torres, Ana, Tibollo, Valentina, Torti, Carlo, Trecarichi, Enrico, Tseng, Yi-Ju, Vallejos, Andrew, Varoquaux, Gael, Vie, Jill-Jênn, Vitacca, Michele, Wagholikar, Kavishwar, Waitman, Lemuel, Wassermann, Demian, William, Yuan, Xia, Zongqi, Yehya, Nadir, Zambelli, Alberto, Zhang, Harrison, Zucco, Chiara, Service d'informatique médicale et biostatistiques [CHU Necker], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), UFR Médecine [Santé] - Université Paris Cité (UFR Médecine UPCité), Université Paris Cité (UPCité), Dr Bourgeois was funded by a grant from the Burroughs Wellcome Fund and supported by the Harvard-MIT Center for Regulatory Science. Mr Keller was funded by grant 5T32HG002295-18 from the National Human Genome Research Institute (NHGRI). Dr Aronow was funded by grant U24 HL148865 from the National Heart, Lung, and Blood Institute (NHLBI). Ms García Barrio was supported by grant PI18/00981 from the Carlos III Health Institute. Dr Gehlenborg was funded by grant T15 LM007092 from the NIH National Library of Medicine. Dr Geva was funded by grant K12 HD047349 from the NIH and Eunice Kennedy Shriver National Institute of Child Health and Human Development. Dr Hanauer was funded by grant UL1TR002240 from the National Center for Advancing Translational Sciences (NCATS). Drs Klann and Murphy were funded by grant 5UL1TR001857-05 from the NCATS and grant 5R01HG009174-04 from the NHGRI. Dr Luo was funded by grant R01LM013337 from the NLM. Mr Patel was funded by grant UL1TR002366 from the NCATS. Dr Gutiérrez-Sacristán was funded by grants K23HL148394 and L40HL148910 from the NIH NHLBI and grant UL1TR001420 from the NIH NCATS. Dr Visweswaran was funded by grant R01LM012095 from the NLM and grant UL1TR001857 from the NCATS. Dr Weber was supported by grants UL1TR002541 and UL1TR000005 from the NIH-NCATS, and grant R01LM013345 from the NLM., CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP), Université de Paris - UFR Médecine Paris Centre [Santé] (UP Médecine Paris Centre), Université de Paris (UP), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPC)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPC), Université Paris Cité - UFR Médecine Paris Centre [Santé] (UPC Médecine Paris Centre), and Université Paris Cité (UPC)
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medicine.medical_specialty ,MESH: Pandemics ,education ,Health Informatics ,MESH: Global Health ,MESH: Hospitalization ,Procalcitonin ,03 medical and health sciences ,0302 clinical medicine ,030225 pediatrics ,Internal medicine ,MESH: Child ,Epidemiology ,medicine ,Infection control ,MESH: COVID-19 ,MESH: SARS-CoV-2 ,030212 general & internal medicine ,health care economics and organizations ,MESH: Electronic Health Records ,Original Investigation ,MESH: Adolescent ,Disease surveillance ,MESH: Humans ,business.industry ,Research ,MESH: Infant, Newborn ,MESH: Child, Preschool ,Retrospective cohort study ,MESH: Retrospective Studies ,General Medicine ,medicine.disease ,MESH: Infant ,MESH: Male ,3. Good health ,Online Only ,Respiratory failure ,Viral pneumonia ,Cohort ,[SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie ,business ,MESH: Female - Abstract
This cohort study aims to describe international hospitalization trends and key epidemiological and clinical features of children and youth with COVID-19., Key Points Question What are international trends in hospitalizations for children and youth with SARS-CoV-2, and what are the epidemiological and clinical features of these patients? Findings This cohort study of 671 children and youth found discrete surges in hospitalizations with variable trends and timing across countries. Common complications included cardiac arrhythmias and viral pneumonia, and laboratory findings included elevations in markers of inflammation and abnormalities of coagulation; few children and youth were treated with medications directed specifically at SARS-CoV-2. Meaning These findings suggest large-scale informatics-based approaches used to incorporate electronic health record data across health care systems can provide an efficient source of information to monitor disease activity and define epidemiological and clinical features of pediatric patients hospitalized with SARS-CoV-2 infections., Importance Additional sources of pediatric epidemiological and clinical data are needed to efficiently study COVID-19 in children and youth and inform infection prevention and clinical treatment of pediatric patients. Objective To describe international hospitalization trends and key epidemiological and clinical features of children and youth with COVID-19. Design, Setting, and Participants This retrospective cohort study included pediatric patients hospitalized between February 2 and October 10, 2020. Patient-level electronic health record (EHR) data were collected across 27 hospitals in France, Germany, Spain, Singapore, the UK, and the US. Patients younger than 21 years who tested positive for COVID-19 and were hospitalized at an institution participating in the Consortium for Clinical Characterization of COVID-19 by EHR were included in the study. Main Outcomes and Measures Patient characteristics, clinical features, and medication use. Results There were 347 males (52%; 95% CI, 48.5-55.3) and 324 females (48%; 95% CI, 44.4-51.3) in this study’s cohort. There was a bimodal age distribution, with the greatest proportion of patients in the 0- to 2-year (199 patients [30%]) and 12- to 17-year (170 patients [25%]) age range. Trends in hospitalizations for 671 children and youth found discrete surges with variable timing across 6 countries. Data from this cohort mirrored national-level pediatric hospitalization trends for most countries with available data, with peaks in hospitalizations during the initial spring surge occurring within 23 days in the national-level and 4CE data. A total of 27 364 laboratory values for 16 laboratory tests were analyzed, with mean values indicating elevations in markers of inflammation (C-reactive protein, 83 mg/L; 95% CI, 53-112 mg/L; ferritin, 417 ng/mL; 95% CI, 228-607 ng/mL; and procalcitonin, 1.45 ng/mL; 95% CI, 0.13-2.77 ng/mL). Abnormalities in coagulation were also evident (D-dimer, 0.78 ug/mL; 95% CI, 0.35-1.21 ug/mL; and fibrinogen, 477 mg/dL; 95% CI, 385-569 mg/dL). Cardiac troponin, when checked (n = 59), was elevated (0.032 ng/mL; 95% CI, 0.000-0.080 ng/mL). Common complications included cardiac arrhythmias (15.0%; 95% CI, 8.1%-21.7%), viral pneumonia (13.3%; 95% CI, 6.5%-20.1%), and respiratory failure (10.5%; 95% CI, 5.8%-15.3%). Few children were treated with COVID-19–directed medications. Conclusions and Relevance This study of EHRs of children and youth hospitalized for COVID-19 in 6 countries demonstrated variability in hospitalization trends across countries and identified common complications and laboratory abnormalities in children and youth with COVID-19 infection. Large-scale informatics-based approaches to integrate and analyze data across health care systems complement methods of disease surveillance and advance understanding of epidemiological and clinical features associated with COVID-19 in children and youth.
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- 2021
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47. Effect of Ivermectin vs Placebo on Time to Sustained Recovery in Outpatients With Mild to Moderate COVID-19: A Randomized Clinical Trial.
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Naggie, Susanna, Boulware, David R., Lindsell, Christopher J., Stewart, Thomas G., Gentile, Nina, Collins, Sean, McCarthy, Matthew William, Jayaweera, Dushyantha, Castro, Mario, Sulkowski, Mark, McTigue, Kathleen, Thicklin, Florence, Felker, G. Michael, Ginde, Adit A., Bramante, Carolyn T., Slandzicki, Alex J., Gabriel, Ahab, Shah, Nirav S., Lenert, Leslie A., and Dunsmore, Sarah E.
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Importance: The effectiveness of ivermectin to shorten symptom duration or prevent hospitalization among outpatients in the US with mild to moderate symptomatic COVID-19 is unknown.Objective: To evaluate the efficacy of ivermectin, 400 μg/kg, daily for 3 days compared with placebo for the treatment of early mild to moderate COVID-19.Design, Setting, and Participants: ACTIV-6, an ongoing, decentralized, double-blind, randomized, placebo-controlled platform trial, was designed to evaluate repurposed therapies in outpatients with mild to moderate COVID-19. A total of 1591 participants aged 30 years and older with confirmed COVID-19, experiencing 2 or more symptoms of acute infection for 7 days or less, were enrolled from June 23, 2021, through February 4, 2022, with follow-up data through May 31, 2022, at 93 sites in the US.Interventions: Participants were randomized to receive ivermectin, 400 μg/kg (n = 817), daily for 3 days or placebo (n = 774).Main Outcomes and Measures: Time to sustained recovery, defined as at least 3 consecutive days without symptoms. There were 7 secondary outcomes, including a composite of hospitalization or death by day 28.Results: Among 1800 participants who were randomized (mean [SD] age, 48 [12] years; 932 women [58.6%]; 753 [47.3%] reported receiving at least 2 doses of a SARS-CoV-2 vaccine), 1591 completed the trial. The hazard ratio (HR) for improvement in time to recovery was 1.07 (95% credible interval [CrI], 0.96-1.17; posterior P value [HR >1] = .91). The median time to recovery was 12 days (IQR, 11-13) in the ivermectin group and 13 days (IQR, 12-14) in the placebo group. There were 10 hospitalizations or deaths in the ivermectin group and 9 in the placebo group (1.2% vs 1.2%; HR, 1.1 [95% CrI, 0.4-2.6]). The most common serious adverse events were COVID-19 pneumonia (ivermectin [n = 5]; placebo [n = 7]) and venous thromboembolism (ivermectin [n = 1]; placebo [n = 5]).Conclusions and Relevance: Among outpatients with mild to moderate COVID-19, treatment with ivermectin, compared with placebo, did not significantly improve time to recovery. These findings do not support the use of ivermectin in patients with mild to moderate COVID-19.Trial Registration: ClinicalTrials.gov Identifier: NCT04885530. [ABSTRACT FROM AUTHOR]- Published
- 2022
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48. Why common carrier and network neutrality principles apply to the Nationwide Health Information Network (NWHIN)
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Gaynor, Mark, Lenert, Leslie, Wilson, Kristin D, and Bradner, Scott
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- 2014
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49. Lessons Learned from the Pilot Phase of a Population-Wide Genomic Screening Program: Building the Base to Reach a Diverse Cohort of 100,000 Participants.
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Allen, Caitlin G., Lenert, Leslie, Hunt, Kelly, Jackson, Amy, Levin, Elissa, Clinton, Catherine, Clark, John T., Garrison, Kelli, Gallegos, Sam, Wager, Karen, He, Wenjun, Sterba, Katherine, Ramos, Paula S., Melvin, Cathy, Ford, Marvella, Catchpole, Kenneth, McMahon, Lori, and Judge, Daniel P.
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MEDICAL screening , *PATIENT portals , *HEREDITARY nonpolyposis colorectal cancer , *BLACK people , *DEMOGRAPHIC characteristics , *ADULTS - Abstract
Background and Objectives: Genomic information is increasingly relevant for disease prevention and risk management at the individual and population levels. Screening healthy adults for Tier 1 conditions of hereditary breast and ovarian cancer, Lynch syndrome, and familial hypercholesterolemia using a population-based approach can help identify the 1–2% of the US population at increased risk of developing diseases associated with these conditions and tailor prevention strategies. Our objective is to report findings from an implementation science study that evaluates multi-level facilitators and barriers to implementation of the In Our DNA SC population-wide genomic screening initiative. Methods: We established an IMPACTeam (IMPlementAtion sCience for In Our DNA SC Team) to evaluate the pilot phase using principles of implementation science. We used a parallel convergent mixed methods approach to assess the Reach, Implementation, and Effectiveness outcomes from the RE-AIM implementation science framework during the pilot phase of In Our DNA SC. Quantitative assessment included the examination of frequencies and response rates across demographic categories using chi-square tests. Qualitative data were audio-recorded and transcribed, with codes developed by the study team based on the semi-structured interview guide. Results: The pilot phase (8 November 2021, to 7 March 2022) included recruitment from ten clinics throughout South Carolina. Reach indicators included enrollment rate and representativeness. A total of 23,269 potential participants were contacted via Epic's MyChart patient portal with 1976 (8.49%) enrolled. Black individuals were the least likely to view the program invitation (28.9%) and take study-related action. As a result, there were significantly higher enrollment rates among White (10.5%) participants than Asian (8.71%) and Black (3.46%) individuals (p < 0.0001). Common concerns limiting reach and participation included privacy and security of results and the impact participation would have on health or life insurance. Facilitators included family or personal history of a Tier 1 condition, prior involvement in genetic testing, self-interest, and altruism. Assessment of implementation (i.e., adherence to protocols/fidelity to protocols) included sample collection rate (n = 1104, 55.9%) and proportion of samples needing recollection (n = 19, 1.7%). There were no significant differences in sample collection based on demographic characteristics. Implementation facilitators included efficient collection processes and enthusiastic clinical staff. Finally, we assessed the effectiveness of the program, finding low dropout rates (n = 7, 0.35%), the identification of eight individuals with Tier 1 conditions (0.72% positive), and high rates of follow-up genetic counseling (87.5% completion). Conclusion: Overall, Asian and Black individuals were less engaged, with few taking any study-related actions. Strategies to identify barriers and promoters for the engagement of diverse populations are needed to support participation. Once enrolled, individuals had high rates of completing the study and follow-up engagement with genetic counselors. Findings from the pilot phase of In Our DNA SC offer opportunities for improvement as we expand the program and can provide guidance to organizations seeking to begin efforts to integrate population-wide genomic screening. [ABSTRACT FROM AUTHOR]
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- 2022
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50. Enhancing research data infrastructure to address the opioid epidemic: the Opioid Overdose Network (O2-Net).
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Lenert, Leslie A., Zhu, Vivienne, Jennings, Lindsey, McCauley, Jenna L., Obeid, Jihad S., Ward, Ralph, Hassanpour, Saeed, Marsch, Lisa A., Hogarth, Michael, Shipman, Perry, Harris, Daniel R., and Talbert, Jeffery C.
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- 2022
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