Correction to: A blockchain-based healthcare data marketplace: prototype and demonstration.
[This corrects the article DOI: 10.1093/jamiaopen/ooae029.].
Author(s):
DOI: 10.1093/jamiaopen/ooae046
[This corrects the article DOI: 10.1093/jamiaopen/ooae029.].
Author(s):
DOI: 10.1093/jamiaopen/ooae046
Healthcare providers employ heuristic and analytical decision-making to navigate the high-stakes environment of the emergency department (ED). Despite the increasing integration of information systems (ISs), research on their efficacy is conflicting. Drawing on related fields, we investigate how timing and mode of delivery influence IS effectiveness. Our objective is to reconcile previous contradictory findings, shedding light on optimal IS design in the ED.
Author(s): Born, Cornelius, Schwarz, Romy, Böttcher, Timo Phillip, Hein, Andreas, Krcmar, Helmut
DOI: 10.1093/jamia/ocae096
Healthcare organizations, including Clinical and Translational Science Awards (CTSA) hubs funded by the National Institutes of Health, seek to enable secondary use of electronic health record (EHR) data through an enterprise data warehouse for research (EDW4R), but optimal approaches are unknown. In this qualitative study, our goal was to understand EDW4R impact, sustainability, demand management, and accessibility.
Author(s): Campion, Thomas R, Craven, Catherine K, Dorr, David A, Bernstam, Elmer V, Knosp, Boyd M
DOI: 10.1093/jamia/ocae111
Surface the urgent dilemma that healthcare delivery organizations (HDOs) face navigating the US Food and Drug Administration (FDA) final guidance on the use of clinical decision support (CDS) software.
Author(s): Sendak, Mark P, Liu, Vincent X, Beecy, Ashley, Vidal, David E, Shaw, Keo, Lifson, Mark A, Tobey, Danny, Valladares, Alexandra, Loufek, Brenna, Mogri, Murtaza, Balu, Suresh
DOI: 10.1093/jamia/ocae119
This study evaluates regularization variants in logistic regression (L1, L2, ElasticNet, Adaptive L1, Adaptive ElasticNet, Broken adaptive ridge [BAR], and Iterative hard thresholding [IHT]) for discrimination and calibration performance, focusing on both internal and external validation.
Author(s): Fridgeirsson, Egill A, Williams, Ross, Rijnbeek, Peter, Suchard, Marc A, Reps, Jenna M
DOI: 10.1093/jamia/ocae109
Synthesizing and evaluating inconsistent medical evidence is essential in evidence-based medicine. This study aimed to employ ChatGPT as a sophisticated scientific reasoning engine to identify conflicting clinical evidence and summarize unresolved questions to inform further research.
Author(s): Xie, Shiyao, Zhao, Wenjing, Deng, Guanghui, He, Guohua, He, Na, Lu, Zhenhua, Hu, Weihua, Zhao, Mingming, Du, Jian
DOI: 10.1093/jamia/ocae100
Implement the 5-type health information technology (HIT) patient safety concern classification system for HIT patient safety issues reported to the Veterans Health Administration's Informatics Patient Safety Office.
Author(s): Kato, Danielle, Lucas, Joe, Sittig, Dean F
DOI: 10.1093/jamia/ocae107
Linking information on Japanese pharmaceutical products to global knowledge bases (KBs) would enhance international collaborative research and yield valuable insights. However, public access to mappings of Japanese pharmaceutical products that use international controlled vocabularies remains limited. This study mapped YJ codes to RxNorm ingredient classes, providing new insights by comparing Japanese and international drug-drug interaction (DDI) information using a case study methodology.
Author(s): Kawakami, Yukinobu, Matsuda, Takuya, Hidaka, Noriaki, Tanaka, Mamoru, Kimura, Eizen
DOI: 10.1093/jamia/ocae094
To develop recommendations regarding the use of weights to reduce selection bias for commonly performed analyses using electronic health record (EHR)-linked biobank data.
Author(s): Salvatore, Maxwell, Kundu, Ritoban, Shi, Xu, Friese, Christopher R, Lee, Seunggeun, Fritsche, Lars G, Mondul, Alison M, Hanauer, David, Pearce, Celeste Leigh, Mukherjee, Bhramar
DOI: 10.1093/jamia/ocae098
Error analysis plays a crucial role in clinical concept extraction, a fundamental subtask within clinical natural language processing (NLP). The process typically involves a manual review of error types, such as contextual and linguistic factors contributing to their occurrence, and the identification of underlying causes to refine the NLP model and improve its performance. Conducting error analysis can be complex, requiring a combination of NLP expertise and domain-specific knowledge. Due [...]
Author(s): Fu, Sunyang, Wang, Liwei, He, Huan, Wen, Andrew, Zong, Nansu, Kumari, Anamika, Liu, Feifan, Zhou, Sicheng, Zhang, Rui, Li, Chenyu, Wang, Yanshan, St Sauver, Jennifer, Liu, Hongfang, Sohn, Sunghwan
DOI: 10.1093/jamia/ocae101