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1. What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask

Author

Kohane, Isaac S, Aronow, Bruce J, Avillach, Paul, Beaulieu-Jones, Brett K, Bellazzi, Riccardo, Bradford, Robert L, Brat, Gabriel A, Cannataro, Mario, Cimino, James J, García-Barrio, Noelia, Gehlenborg, Nils, Ghassemi, Marzyeh, Gutiérrez-Sacristán, Alba, Hanauer, David A, Holmes, John H, Hong, Chuan, Klann, Jeffrey G, Loh, Ne Hooi Will, Luo, Yuan, Mandl, Kenneth D, Daniar, Mohamad, Moore, Jason H, Murphy, Shawn N, Neuraz, Antoine, Ngiam, Kee Yuan, Omenn, Gilbert S, Palmer, Nathan, Patel, Lav P, Pedrera-Jiménez, Miguel, Sliz, Piotr, South, Andrew M, Tan, Amelia Li Min, Taylor, Deanne M, Taylor, Bradley W, Torti, Carlo, Vallejos, Andrew K, Wagholikar, Kavishwar B, Weber, Griffin M, and Cai, Tianxi

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

Coincident with the tsunami of COVID-19–related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws in EHR-derived studies, many medical editorial teams and otherwise sophisticated medical readers lack the framework to fully critically appraise these studies. In addition, conventional statistical analyses cannot overcome the need for an understanding of the opportunities and limitations of EHR-derived studies. We distill here from the broader informatics literature six key considerations that are crucial for appraising studies utilizing EHR data: data completeness, data collection and handling (eg, transformation), data type (ie, codified, textual), robustness of methods against EHR variability (within and across institutions, countries, and time), transparency of data and analytic code, and the multidisciplinary approach. These considerations will inform researchers, clinicians, and other stakeholders as to the recommended best practices in reviewing manuscripts, grants, and other outputs from EHR-data derived studies, and thereby promote and foster rigor, quality, and reliability of this rapidly growing field.

Published
2021
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7. Clinical phenotypes and outcomes in children with multisystem inflammatory syndrome across SARS-CoV-2 variant eras: a multinational study from the 4CE consortium

Author

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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8. Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort study

Author

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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11. SurvMaximin: Robust federated approach to transporting survival risk prediction models

Author

Wang, Xuan, Zhang, Harrison G., Xiong, Xin, Hong, Chuan, Weber, Griffin M., Brat, Gabriel A., Bonzel, Clara-Lea, Luo, Yuan, Duan, Rui, Palmer, Nathan P., Hutch, Meghan R., Gutiérrez-Sacristán, Alba, Bellazzi, Riccardo, Chiovato, Luca, Cho, Kelly, Dagliati, Arianna, Estiri, Hossein, García-Barrio, Noelia, Griffier, Romain, Hanauer, David A., Ho, Yuk-Lam, Holmes, John H., Keller, Mark S., Klann MEng, Jeffrey G., L'Yi, Sehi, Lozano-Zahonero, Sara, Maidlow, Sarah E., Makoudjou, Adeline, Malovini, Alberto, Moal, Bertrand, Moore, Jason H., Morris, Michele, Mowery, Danielle L., Murphy, Shawn N, Neuraz, Antoine, Yuan Ngiam, Kee, Omenn, Gilbert S., Patel, Lav P., Pedrera-Jiménez, Miguel, Prunotto, Andrea, Jebathilagam Samayamuthu, Malarkodi, Sanz Vidorreta, Fernando J, Schriver, Emily R., Schubert, Petra, Serrano-Balazote, Pablo, South, Andrew M., Tan, Amelia L.M., Tan, Byorn W.L., Tibollo, Valentina, Tippmann, Patric, Visweswaran, Shyam, Xia, Zongqi, Yuan, William, Zöller, Daniela, Kohane, Isaac S., Avillach, Paul, Guo, Zijian, and Cai, Tianxi

Published
2022
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13. Multiview Incomplete Knowledge Graph Integration with application to cross-institutional EHR data harmonization

Author

Zhou, Doudou, Gan, Ziming, Shi, Xu, Patwari, Alina, Rush, Everett, Bonzel, Clara-Lea, Panickan, Vidul A., Hong, Chuan, Ho, Yuk-Lam, Cai, Tianrun, Costa, Lauren, Li, Xiaoou, Castro, Victor M., Murphy, Shawn N., Brat, Gabriel, Weber, Griffin, Avillach, Paul, Gaziano, J. Michael, Cho, Kelly, Liao, Katherine P., Lu, Junwei, and Cai, Tianxi

Published
2022
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17. International electronic health record-derived post-acute sequelae profiles of COVID-19 patients

Author

Zhang, Harrison G., Dagliati, Arianna, Shakeri Hossein Abad, Zahra, Xiong, Xin, Bonzel, Clara-Lea, Xia, Zongqi, Tan, Bryce W. Q., Avillach, Paul, Brat, Gabriel A., Hong, Chuan, Morris, Michele, Visweswaran, Shyam, Patel, Lav P., Gutiérrez-Sacristán, Alba, Hanauer, David A., Holmes, John H., Samayamuthu, Malarkodi Jebathilagam, Bourgeois, Florence T., L’Yi, Sehi, Maidlow, Sarah E., Moal, Bertrand, Murphy, Shawn N., Strasser, Zachary H., Neuraz, Antoine, Ngiam, Kee Yuan, Loh, Ne Hooi Will, Omenn, Gilbert S., Prunotto, Andrea, Dalvin, Lauren A., Klann, Jeffrey G., Schubert, Petra, Vidorreta, Fernando J. Sanz, Benoit, Vincent, Verdy, Guillaume, Kavuluru, Ramakanth, Estiri, Hossein, Luo, Yuan, Malovini, Alberto, Tibollo, Valentina, Bellazzi, Riccardo, Cho, Kelly, Ho, Yuk-Lam, Tan, Amelia L. M., Tan, Byorn W. L., Gehlenborg, Nils, Lozano-Zahonero, Sara, Jouhet, Vianney, Chiovato, Luca, Aronow, Bruce J., Toh, Emma M. S., Wong, Wei Gen Scott, Pizzimenti, Sara, Wagholikar, Kavishwar B., Bucalo, Mauro, Cai, Tianxi, South, Andrew M., Kohane, Isaac S., and Weber, Griffin M.

Published
2022
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18. International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality

Author

Weber, Griffin M., Hong, Chuan, Xia, Zongqi, Palmer, Nathan P., Avillach, Paul, L’Yi, Sehi, Keller, Mark S., Murphy, Shawn N., Gutiérrez-Sacristán, Alba, Bonzel, Clara-Lea, Serret-Larmande, Arnaud, Neuraz, Antoine, Omenn, Gilbert S., Visweswaran, Shyam, Klann, Jeffrey G., South, Andrew M., Loh, Ne Hooi Will, Cannataro, Mario, Beaulieu-Jones, Brett K., Bellazzi, Riccardo, Agapito, Giuseppe, Alessiani, Mario, Aronow, Bruce J., Bell, Douglas S., Benoit, Vincent, Bourgeois, Florence T., Chiovato, Luca, Cho, Kelly, Dagliati, Arianna, DuVall, Scott L., Barrio, Noelia García, Hanauer, David A., Ho, Yuk-Lam, Holmes, John H., Issitt, Richard W., Liu, Molei, Luo, Yuan, Lynch, Kristine E., Maidlow, Sarah E., Malovini, Alberto, Mandl, Kenneth D., Mao, Chengsheng, Matheny, Michael E., Moore, Jason H., Morris, Jeffrey S., Morris, Michele, Mowery, Danielle L., Ngiam, Kee Yuan, Patel, Lav P., Pedrera-Jimenez, Miguel, Ramoni, Rachel B., Schriver, Emily R., Schubert, Petra, Balazote, Pablo Serrano, Spiridou, Anastasia, Tan, Amelia L. M., Tan, Byorn W. L., Tibollo, Valentina, Torti, Carlo, Trecarichi, Enrico M., Wang, Xuan, Kohane, Isaac S., Cai, Tianxi, and Brat, Gabriel A.

Published
2022
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26. An operational guide to translational clinical machine learning in academic medical centers.

Author

Poddar, Mukund, Marwaha, Jayson S., Yuan, William, Romero-Brufau, Santiago, and Brat, Gabriel A.

Subjects
DATA science, ACADEMIC medical centers, COMPUTER software, MEDICAL care, ACADEMIA, ECOSYSTEMS, INFORMATION technology, MACHINE learning, STAKEHOLDER analysis
Abstract

Few published data science tools are ever translated from academia to real-world clinical settings for which they were intended. One dimension of this problem is the software engineering task of turning published academic projects into tools that are usable at the bedside. Given the complexity of the data ecosystem in large health systems, this task often represents a significant barrier to the real-world deployment of data science tools for prospective piloting and evaluation. Many information technology companies have created Machine Learning Operations (MLOps) teams to help with such tasks at scale, but the low penetration of home-grown data science tools in regular clinical practice precludes the formation of such teams in healthcare organizations. Based on experiences deploying data science tools at two large academic medical centers (Beth Israel Deaconess Medical Center, Boston, MA; Mayo Clinic, Rochester, MN), we propose a strategy to facilitate this transition from academic product to operational tool, defining the responsibilities of the principal investigator, data scientist, machine learning engineer, health system IT administrator, and clinician end-user throughout the process. We first enumerate the technical resources and stakeholders needed to prepare for model deployment. We then propose an approach to planning how the final product will work from data extraction and analysis to visualization of model outputs. Finally, we describe how the team should execute on this plan. We hope to guide health systems aiming to deploy minimum viable data science tools and realize their value in clinical practice. [ABSTRACT FROM AUTHOR]

Published
2024
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31. International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium

Author

Brat, Gabriel A., Weber, Griffin M., Gehlenborg, Nils, Avillach, Paul, Palmer, Nathan P., Chiovato, Luca, Cimino, James, Waitman, Lemuel R., Omenn, Gilbert S., Malovini, Alberto, Moore, Jason H., Beaulieu-Jones, Brett K., Tibollo, Valentina, Murphy, Shawn N., Yi, Sehi L’, Keller, Mark S., Bellazzi, Riccardo, Hanauer, David A., Serret-Larmande, Arnaud, Gutierrez-Sacristan, Alba, Holmes, John J., Bell, Douglas S., Mandl, Kenneth D., Follett, Robert W., Klann, Jeffrey G., Murad, Douglas A., Scudeller, Luigia, Bucalo, Mauro, Kirchoff, Katie, Craig, Jean, Obeid, Jihad, Jouhet, Vianney, Griffier, Romain, Cossin, Sebastien, Moal, Bertrand, Patel, Lav P., Bellasi, Antonio, Prokosch, Hans U., Kraska, Detlef, Sliz, Piotr, Tan, Amelia L. M., Ngiam, Kee Yuan, Zambelli, Alberto, Mowery, Danielle L., Schiver, Emily, Devkota, Batsal, Bradford, Robert L., Daniar, Mohamad, Daniel, Christel, Benoit, Vincent, Bey, Romain, Paris, Nicolas, Serre, Patricia, Orlova, Nina, Dubiel, Julien, Hilka, Martin, Jannot, Anne Sophie, Breant, Stephane, Leblanc, Judith, Griffon, Nicolas, Burgun, Anita, Bernaux, Melodie, Sandrin, Arnaud, Salamanca, Elisa, Cormont, Sylvie, Ganslandt, Thomas, Gradinger, Tobias, Champ, Julien, Boeker, Martin, Martel, Patricia, Esteve, Loic, Gramfort, Alexandre, Grisel, Olivier, Leprovost, Damien, Moreau, Thomas, Varoquaux, Gael, Vie, Jill-Jênn, Wassermann, Demian, Mensch, Arthur, Caucheteux, Charlotte, Haverkamp, Christian, Lemaitre, Guillaume, Bosari, Silvano, Krantz, Ian D., South, Andrew, Cai, Tianxi, and Kohane, Isaac S.

Published
2020
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36. The truth about trauma readmissions

Author

Olufajo, Olubode A., Cooper, Zara, Yorkgitis, Brian K., Najjar, Peter A., Metcalfe, David, Havens, Joaquim M., Askari, Reza, Brat, Gabriel A., Haider, Adil H., and Salim, Ali

Published
2016
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42. Acute respiratory distress syndrome after SARS-CoV-2 infection on young adult population: International observational federated study based on electronic health records through the 4CE consortium.

Author

Moal, Bertrand, Orieux, Arthur, Ferté, Thomas, Neuraz, Antoine, Brat, Gabriel A., Avillach, Paul, Bonzel, Clara-Lea, Cai, Tianxi, Cho, Kelly, Cossin, Sébastien, Griffier, Romain, Hanauer, David A., Haverkamp, Christian, Ho, Yuk-Lam, Hong, Chuan, Hutch, Meghan R., Klann, Jeffrey G., Le, Trang T., Loh, Ne Hooi Will, and Luo, Yuan

Subjects
YOUNG adults, SARS-CoV-2, ADULT respiratory distress syndrome, ELECTRONIC health records, NEUROENDOCRINE cells, CONSORTIA
Abstract

Purpose: In young adults (18 to 49 years old), investigation of the acute respiratory distress syndrome (ARDS) after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been limited. We evaluated the risk factors and outcomes of ARDS following infection with SARS-CoV-2 in a young adult population. Methods: A retrospective cohort study was conducted between January 1st, 2020 and February 28th, 2021 using patient-level electronic health records (EHR), across 241 United States hospitals and 43 European hospitals participating in the Consortium for Clinical Characterization of COVID-19 by EHR (4CE). To identify the risk factors associated with ARDS, we compared young patients with and without ARDS through a federated analysis. We further compared the outcomes between young and old patients with ARDS. Results: Among the 75,377 hospitalized patients with positive SARS-CoV-2 PCR, 1001 young adults presented with ARDS (7.8% of young hospitalized adults). Their mortality rate at 90 days was 16.2% and they presented with a similar complication rate for infection than older adults with ARDS. Peptic ulcer disease, paralysis, obesity, congestive heart failure, valvular disease, diabetes, chronic pulmonary disease and liver disease were associated with a higher risk of ARDS. We described a high prevalence of obesity (53%), hypertension (38%- although not significantly associated with ARDS), and diabetes (32%). Conclusion: Trough an innovative method, a large international cohort study of young adults developing ARDS after SARS-CoV-2 infection has been gather. It demonstrated the poor outcomes of this population and associated risk factor. [ABSTRACT FROM AUTHOR]

Published
2023
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47. Deploying digital health tools within large, complex health systems: key considerations for adoption and implementation.

Author

Marwaha, Jayson S., Landman, Adam B., Brat, Gabriel A., Dunn, Todd, and Gordon, William J.

Subjects
STRATEGIC planning, HEALTH facility administration, DIGITAL health, VALUE-based healthcare, COMMERCIAL product evaluation, SYSTEMS development, SYSTEM integration
Abstract

In recent years, the number of digital health tools with the potential to significantly improve delivery of healthcare services has grown tremendously. However, the use of these tools in large, complex health systems remains comparatively limited. The adoption and implementation of digital health tools at an enterprise level is a challenge; few strategies exist to help tools cross the chasm from clinical validation to integration within the workflows of a large health system. Many previously proposed frameworks for digital health implementation are difficult to operationalize in these dynamic organizations. In this piece, we put forth nine dimensions along which clinically validated digital health tools should be examined by health systems prior to adoption, and propose strategies for selecting digital health tools and planning for implementation in this setting. By evaluating prospective tools along these dimensions, health systems can evaluate which existing digital health solutions are worthy of adoption, ensure they have sufficient resources for deployment and long-term use, and devise a strategic plan for implementation. [ABSTRACT FROM AUTHOR]

Published
2022
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50. Effective Communication of Personalized Risks and Patient Preferences During Surgical Informed Consent Using Data Visualization: Qualitative Semistructured Interview Study With Patients After Surgery.

Author

Gisladottir, Undina, Nakikj, Drashko, Jhunjhunwala, Rashi, Panton, Jasmine, Brat, Gabriel, and Gehlenborg, Nils

Subjects
DATA modeling, BIOSENSORS, PERIOPERATIVE care, COGNITIVE ability, ELECTRONIC health records
Abstract

Background: There is no consensus on which risks to communicate to a prospective surgical patient during informed consent or how. Complicating the process, patient preferences may diverge from clinical assumptions and are often not considered for discussion. Such discrepancies can lead to confusion and resentment, raising the potential for legal action. To overcome these issues, we propose a visual consent tool that incorporates patient preferences and communicates personalized risks to patients using data visualization. We used this platform to identify key effective visual elements to communicate personalized surgical risks. Objective: Our main focus is to understand how to best communicate personalized risks using data visualization. To contextualize patient responses to the main question, we examine how patients perceive risks before surgery (research question 1), how suitably the visual consent tool is able to present personalized surgical risks (research question 2), how well our visualizations convey those personalized surgical risks (research question 3), and how the visual consent tool could improve the informed consent process and how it can be used (research question 4). Methods: We designed a visual consent tool to meet the objectives of our study. To calculate and list personalized surgical risks, we used the American College of Surgeons risk calculator. We created multiple visualization mock-ups using visual elements previously determined to be well-received for risk communication. Semistructured interviews were conducted with patients after surgery, and each of the mock-ups was presented and evaluated independently and in the context of our visual consent tool design. The interviews were transcribed, and thematic analysis was performed to identify major themes. We also applied a quantitative approach to the analysis to assess the prevalence of different perceptions of the visualizations presented in our tool. Results: In total, 20 patients were interviewed, with a median age of 59 (range 29-87) years. Thematic analysis revealed factors that influenced the perception of risk (the surgical procedure, the cognitive capacity of the patient, and the timing of consent; research question 1); factors that influenced the perceived value of risk visualizations (preference for rare event communication, preference for risk visualization, and usefulness of comparison with the average; research question 3); and perceived usefulness and use cases of the visual consent tool (research questions 2 and 4). Most importantly, we found that patients preferred the visual consent tool to current text-based documents and had no unified preferences for risk visualization. Furthermore, our findings suggest that patient concerns were not often represented in existing risk calculators. Conclusions: We identified key elements that influence effective visual risk communication in the perioperative setting and pointed out the limitations of the existing calculators in addressing patient concerns. Patient preference is highly variable and should influence choices regarding risk presentation and visualization. [ABSTRACT FROM AUTHOR]

Published
2022
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