Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Jessica M Schwartz"'
Autor:
Jessica M Schwartz, Maureen George, Sarah Collins Rossetti, Patricia C Dykes, Simon R Minshall, Eugene Lucas, Kenrick D Cato
Publikováno v:
JMIR Human Factors, Vol 9, Iss 2, p e33960 (2022)
BackgroundClinician trust in machine learning–based clinical decision support systems (CDSSs) for predicting in-hospital deterioration (a type of predictive CDSS) is essential for adoption. Evidence shows that clinician trust in predictive CDSSs is
Externí odkaz:
https://doaj.org/article/0481e8fa72aa435a922e97bc6f7e26e4
Publikováno v:
Applied Clinical Informatics. 13:1223-1236
Background Seamless data integration between point-of-care medical devices and the electronic health record (EHR) can be central to clinical decision support systems (CDSS). Objective The objective of this scoping review is to (1) examine the existin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6424be8c2009187d1f80cfa4cdb97690
https://doi.org/10.1055/s-0042-1759513
https://doi.org/10.1055/s-0042-1759513
Autor:
Sarah Collins Rossetti, Eugene Lucas, Kenrick Cato, S. Trent Rosenbloom, Amanda J. Moy, Jennifer Withall, Judy Murphy, Kevin B. Johnson, Don E. Detmer, Jessica M. Schwartz
Publikováno v:
Applied Clinical Informatics
Background Substantial strategies to reduce clinical documentation were implemented by health care systems throughout the coronavirus disease-2019 (COVID-19) pandemic at national and local levels. This natural experiment provides an opportunity to st
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::028e1ee8f9110be08763a603158bb5bd
https://doi.org/10.1055/s-0041-1739518
https://doi.org/10.1055/s-0041-1739518
Autor:
Ruijun Chen, Shirin Sadri, Kenrick Cato, Amanda J. Moy, Sarah Collins Rossetti, Eugene Lucas, Jessica M. Schwartz
Publikováno v:
Journal of the American Medical Informatics Association
Journal of the American Medical Informatics Association : JAMIA
Journal of the American Medical Informatics Association : JAMIA
Background Objective Electronic health records (EHRs) are linked with documentation burden resulting in clinician burnout. While clear classifications and validated measures of burnout exist, documentation burden remains ill-defined and inconsistentl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9563ec1daa4e8747801b44e6839c9f3d
https://doi.org/10.1093/jamia/ocaa325
https://doi.org/10.1093/jamia/ocaa325
Autor:
Kenrick Cato, Jonathan E. Elias, Amanda J. Moy, Lucy Aaron, Jessica M. Schwartz, Richard Trepp, Sarah Collins Rossetti
Publikováno v:
Appl Clin Inform
Background The impact of electronic health records (EHRs) in the emergency department (ED) remains mixed. Dynamic and unpredictable, the ED is highly vulnerable to workflow interruptions. Objectives The aim of the study is to understand multitasking
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::696e25585a7261c05a91e3589113d4de
https://pubmed.ncbi.nlm.nih.gov/34706395
https://pubmed.ncbi.nlm.nih.gov/34706395
Publikováno v:
J Am Med Inform Assoc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::329f9e7b95103edeaa4ab1a2e24fc27c
https://europepmc.org/articles/PMC8510376/
https://europepmc.org/articles/PMC8510376/
Autor:
Li Zhou, Patricia C. Dykes, Jose P. Garcia, Kenrick Cato, Haomiao Jia, Liqin Wang, Christopher Knaplund, Jessica M. Schwartz, Kumiko O. Schnock, Zfania Tom Korach, Sarah A. Collins, Frank Y. Chang, Min-Jeoung Kang
Publikováno v:
Appl Clin Inform
Background In the hospital setting, it is crucial to identify patients at risk for deterioration before it fully develops, so providers can respond rapidly to reverse the deterioration. Rapid response (RR) activation criteria include a subjective com
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::252fcc3ae49d3b5d4afbb9020cb96679
https://doi.org/10.1055/s-0039-3401814
https://doi.org/10.1055/s-0039-3401814
Publikováno v:
Journal of the American Medical Informatics Association. 28:2543-2544
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d298f3ed05e44915de85066c90f49328
https://doi.org/10.1093/jamia/ocab177
https://doi.org/10.1093/jamia/ocab177
Autor:
Jessica M. Schwartz, Kenrick Cato
Publikováno v:
ICHI
Machine learning is burgeoning in the clinical decision support domain, with the potential to bolster the power of decision support systems, improving data-informed clinical decision making. However, barriers persist to the adoption and regular use o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::42963d40154737bd28461a6dd3f1c9c4
https://doi.org/10.1109/ichi48887.2020.9374365
https://doi.org/10.1109/ichi48887.2020.9374365
Publikováno v:
J Am Med Inform Assoc
Objective The study sought to describe the prevalence and nature of clinical expert involvement in the development, evaluation, and implementation of clinical decision support systems (CDSSs) that utilize machine learning to analyze electronic health
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::53edcd012c67b84ca350514d0f902f15
https://pubmed.ncbi.nlm.nih.gov/34498082
https://pubmed.ncbi.nlm.nih.gov/34498082