Response to: An Evidence-Based Tool for Safe Configuration of Electronic Health Records: The eSafety Checklist.
Author(s): Koppel, Ross
DOI: 10.1055/s-0038-1675811
Author(s): Koppel, Ross
DOI: 10.1055/s-0038-1675811
Electronic health records (EHRs) are transforming the way health care is delivered. They are central to improving the quality of patient care and have been attributed to making health care more accessible, reliable, and safe. However, in recent years, evidence suggests that specific features and functions of EHRs can introduce new, unanticipated patient safety concerns that can be mitigated by safe configuration practices.
Author(s): Dhillon-Chattha, Pritma, McCorkle, Ruth, Borycki, Elizabeth
DOI: 10.1055/s-0038-1675210
Author(s): Sarkar, Indra Neil
DOI: 10.1093/jamiaopen/ooy047
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
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
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
Globally, healthcare systems are using the Electronic Health Record (EHR) and elements of clinical decision support (CDS) to facilitate palliative care (PC). Examination of published results is needed to determine if the EHR is successfully supporting the multidisciplinary nature and complexity of PC by identifying applications, methodology, outcomes, and barriers of active incorporation of the EHR in PC clinical workflow.
Author(s): Bush, Ruth A, Pérez, Alexa, Baum, Tanja, Etland, Caroline, Connelly, Cynthia D
DOI: 10.1093/jamiaopen/ooy028
This study sought to quantitatively characterize medical students' expectations and experiences of an electronic health record (EHR) system in a hospital setting, and to examine perceived and actual impacts on learning.
Author(s): Cheng, Daryl R, Scodellaro, Thomas, Uahwatanasakul, Wonie, South, Mike
DOI: 10.1055/s-0038-1675371