Comment on Dr. Chung's Editorial: Pediatric Health Information Technology-What We Need for Optimal Care of Children.
Author(s): Wong, Lori, Liu, Daniel, Thompson, Cori, Margo, Todd, Yu, Feliciano
DOI: 10.1055/s-0041-1740922
Author(s): Wong, Lori, Liu, Daniel, Thompson, Cori, Margo, Todd, Yu, Feliciano
DOI: 10.1055/s-0041-1740922
An electronic clinical decision support (CDS) alert can provide real-time provider support to offer pre-exposure prophylaxis (PrEP) to youth at risk for human immunodeficiency virus (HIV). The purpose of this study was to evaluate provider utilization of a PrEP CDS alert in a large academic-community pediatric network and assess the association of the alert with PrEP prescribing rates.
Author(s): Chan, Carrie T, Vo, Megen, Carlson, Jennifer, Lee, Tzielan, Chang, Marcello, Hart-Cooper, Geoffrey
DOI: 10.1055/s-0041-1740484
This study aimed to develop a virtual electronic health record (EHR) training and optimization program and evaluate the impact of the virtual model on provider and staff burnout and electronic health record (EHR) experience.
Author(s): English, Eden F, Holmstrom, Heather, Kwan, Bethany W, Suresh, Krithika, Rotholz, Stephen, Lin, Chen-Tan, Sieja, Amber
DOI: 10.1055/s-0041-1740482
Developing a diverse informatics workforce broadens the research agenda and ensures the growth of innovative solutions that enable equity-centered care. The American Medical Informatics Association (AMIA) established the AMIA First Look Program in 2017 to address workforce disparities among women, including those from marginalized communities. The program exposes women to informatics, furnishes mentors, and provides career resources. In 4 years, the program has introduced 87 undergraduate women, 41% members of [...]
Author(s): Bright, Tiffani J, Williams, Karmen S, Rajamani, Sripriya, Tiase, Victoria L, Senathirajah, Yalini, Hebert, Courtney, McCoy, Allison B
DOI: 10.1093/jamia/ocab246
Hospital-acquired infections (HAIs) are associated with significant morbidity, mortality, and prolonged hospital length of stay. Risk prediction models based on pre- and intraoperative data have been proposed to assess the risk of HAIs at the end of the surgery, but the performance of these models lag behind HAI detection models based on postoperative data. Postoperative data are more predictive than pre- or interoperative data since it is closer to the [...]
Author(s): Yang, Haoyu, Tourani, Roshan, Zhu, Ying, Kumar, Vipin, Melton, Genevieve B, Steinbach, Michael, Simon, Gyorgy
DOI: 10.1093/jamia/ocab229
Clinical registries-structured databases of demographic, diagnosis, and treatment information-play vital roles in retrospective studies, operational planning, and assessment of patient eligibility for research, including clinical trials. Registry curation, a manual and time-intensive process, is always costly and often impossible for rare or underfunded diseases. Our goal was to evaluate the feasibility of natural language inference (NLI) as a scalable solution for registry curation.
Author(s): Percha, Bethany, Pisapati, Kereeti, Gao, Cynthia, Schmidt, Hank
DOI: 10.1093/jamia/ocab243
This article reports a systematic review of studies containing development and validation of models predicting postacute care destination after adult inpatient hospitalization, summarizes clinical populations and variables, evaluates model performance, assesses risk of bias and applicability, and makes recommendations to reduce bias in future models.
Author(s): Kennedy, Erin E, Bowles, Kathryn H, Aryal, Subhash
DOI: 10.1093/jamia/ocab197
Suboptimal design of health information technology (IT) systems can lead to the introduction of errors in the diagnostic process. We aimed to identify mechanisms that can affect the safety and effectiveness of these systems in hospital settings thus contributing to the building of an explicit and replicable understanding of the variables that can affect the functioning of IT systems.
Author(s): Georgiou, Andrew, Li, Julie, Thomas, Judith, Dahm, Maria R
DOI: 10.1093/jamia/ocab235
Author(s): Alexander, Gregory L, Powell, Kimberly R, Deroche, Chelsea B
DOI: 10.1093/jamia/ocab241
Electronic health records (EHR) are commonly used for the identification of novel risk factors for disease, often referred to as an association study. A major challenge to EHR-based association studies is phenotyping error in EHR-derived outcomes. A manual chart review of phenotypes is necessary for unbiased evaluation of risk factor associations. However, this process is time-consuming and expensive. The objective of this paper is to develop an outcome-dependent sampling approach [...]
Author(s): Yin, Ziyan, Tong, Jiayi, Chen, Yong, Hubbard, Rebecca A, Tang, Cheng Yong
DOI: 10.1093/jamia/ocab222