Finding the patient in informatics.
Author(s): Sarkar, Indra Neil
DOI: 10.1093/jamiaopen/ooy047
Author(s): Sarkar, Indra Neil
DOI: 10.1093/jamiaopen/ooy047
This article describes the method of integrating a manual pediatric emergency department sepsis screening process into the electronic health record that leverages existing clinical documentation and keeps providers in their current, routine clinical workflows.
Author(s): Lloyd, Julia K, Ahrens, Erin A, Clark, Donnie, Dachenhaus, Terri, Nuss, Kathryn E
DOI: 10.1055/s-0038-1675211
Surveillance for surgical site infections (SSIs) after ambulatory surgery in children requires a detailed manual chart review to assess criteria defined by the National Health and Safety Network (NHSN). Electronic health records (EHRs) impose an inefficient search process where infection preventionists must manually review every postsurgical encounter ( 30 days). Using text mining and business intelligence software, we developed an information foraging application, the SSI Workbench, to visually present which [...]
Author(s): Karavite, Dean J, Miller, Matthew W, Ramos, Mark J, Rettig, Susan L, Ross, Rachael K, Xiao, Rui, Muthu, Naveen, Localio, A Russell, Gerber, Jeffrey S, Coffin, Susan E, Grundmeier, Robert W
DOI: 10.1055/s-0038-1675179
Clinician progress notes are an important record for care and communication, but there is a perception that electronic notes take too long to write and may not accurately reflect the patient encounter, threatening quality of care. Automatic speech recognition (ASR) has the potential to improve clinical documentation process; however, ASR inaccuracy and editing time are barriers to wider use. We hypothesized that automatic text processing technologies could decrease editing time [...]
Author(s): Lybarger, Kevin J, Ostendorf, Mari, Riskin, Eve, Payne, Thomas H, White, Andrew A, Yetisgen, Meliha
DOI: 10.1055/s-0038-1673417
Author(s): Ohno-Machado, Lucila
DOI: 10.1093/jamia/ocy136
We introduce data assimilation as a computational method that uses machine learning to combine data with human knowledge in the form of mechanistic models in order to forecast future states, to impute missing data from the past by smoothing, and to infer measurable and unmeasurable quantities that represent clinically and scientifically important phenotypes. We demonstrate the advantages it affords in the context of type 2 diabetes by showing how data [...]
Author(s): Albers, David J, Levine, Matthew E, Stuart, Andrew, Mamykina, Lena, Gluckman, Bruce, Hripcsak, George
DOI: 10.1093/jamia/ocy106
Patient-generated health data (PGHD) collected digitally with mobile health (mHealth) technology has garnered recent excitement for its potential to improve precision management of chronic conditions such as atrial fibrillation (AF), a common cardiac arrhythmia. However, sustained engagement is a major barrier to collection of PGHD. Little is known about barriers to sustained engagement or strategies to intervene upon engagement through application design.
Author(s): Reading, Meghan, Baik, Dawon, Beauchemin, Melissa, Hickey, Kathleen T, Merrill, Jacqueline A
DOI: 10.1055/s-0038-1672138
The Objective Structured Assessment of Debriefing (OSAD) is an evidence-based, 8-item tool that uses a behaviorally anchored rating scale in paper-based form to evaluate the quality of debriefing in medical education. The objective of this project was twofold: 1) to create an easy-to-use electronic format of the OSAD (eOSAD) in order to streamline data entry; and 2) to pilot its use on videoed debriefings.
Author(s): Zamjahn, John B, Baroni de Carvalho, Raquel, Bronson, Megan H, Garbee, Deborah D, Paige, John T
DOI: 10.1093/jamia/ocy113
Legislation aimed at increasing the use of a health information exchange (HIE) in healthcare has excluded long-term care facilities, resulting in a vulnerable patient population that can benefit from the improvement of communication and reduction of waste.
Author(s): Kruse, Clemens Scott, Marquez, Gabriella, Nelson, Daniel, Palomares, Olivia
DOI: 10.1055/s-0038-1670651
We executed the Social Media Mining for Health (SMM4H) 2017 shared tasks to enable the community-driven development and large-scale evaluation of automatic text processing methods for the classification and normalization of health-related text from social media. An additional objective was to publicly release manually annotated data.
Author(s): Sarker, Abeed, Belousov, Maksim, Friedrichs, Jasper, Hakala, Kai, Kiritchenko, Svetlana, Mehryary, Farrokh, Han, Sifei, Tran, Tung, Rios, Anthony, Kavuluru, Ramakanth, de Bruijn, Berry, Ginter, Filip, Mahata, Debanjan, Mohammad, Saif M, Nenadic, Goran, Gonzalez-Hernandez, Graciela
DOI: 10.1093/jamia/ocy114