Corrigendum to: The use of Twitter to facilitate sharing of clinical expertise in urology.
Author(s):
DOI: 10.1093/jamia/ocx108
Author(s):
DOI: 10.1093/jamia/ocx108
Investigate the accuracy of 2 different medication reconciliation tools integrated into electronic health record systems (EHRs) using a cognitively demanding scenario and complex medication history.
Author(s): Horsky, Jan, Drucker, Elizabeth A, Ramelson, Harley Z
DOI: 10.1093/jamia/ocx127
Incorrect imaging protocol selection can lead to important clinical findings being missed, contributing to both wasted health care resources and patient harm. We present a machine learning method for analyzing the unstructured text of clinical indications and patient demographics from magnetic resonance imaging (MRI) orders to automatically protocol MRI procedures at the sequence level. We compared 3 machine learning models - support vector machine, gradient boosting machine, and random forest [...]
Author(s): Brown, Andrew D, Marotta, Thomas R
DOI: 10.1093/jamia/ocx125
To define the types and numbers of inpatient clinical decision support alerts, measure the frequency with which they are overridden, and describe providers' reasons for overriding them and the appropriateness of those reasons.
Author(s): Nanji, Karen C, Seger, Diane L, Slight, Sarah P, Amato, Mary G, Beeler, Patrick E, Her, Qoua L, Dalleur, Olivia, Eguale, Tewodros, Wong, Adrian, Silvers, Elizabeth R, Swerdloff, Michael, Hussain, Salman T, Maniam, Nivethietha, Fiskio, Julie M, Dykes, Patricia C, Bates, David W
DOI: 10.1093/jamia/ocx115
Many countries require hospitals to implement medication reconciliation for accreditation, but the process is resource-intensive, thus adherence is poor. We report on the impact of prepopulating and aligning community and hospital drug lists with data from population-based and hospital-based drug information systems to reduce workload and enhance adoption and use of an e-medication reconciliation application, RightRx.
Author(s): Tamblyn, Robyn, Winslade, Nancy, Lee, Todd C, Motulsky, Aude, Meguerditchian, Ari, Bustillo, Melissa, Elsayed, Sarah, Buckeridge, David L, Couture, Isabelle, Qian, Christina J, Moraga, Teresa, Huang, Allen
DOI: 10.1093/jamia/ocx107
Author(s): Vreeman, Daniel J, Abhyankar, Swapna, McDonald, Clement J
DOI: 10.1093/jamia/ocx087
To systematically classify the clinical impact of computerized clinical decision support systems (CDSSs) in inpatient care.
Author(s): Varghese, Julian, Kleine, Maren, Gessner, Sophia Isabella, Sandmann, Sarah, Dugas, Martin
DOI: 10.1093/jamia/ocx100
Many institutions have implemented clinical decision support systems (CDSSs). While CDSS research papers have focused on benefits of these systems, there is a smaller body of literature showing that CDSSs may also produce unintended adverse consequences (UACs). Detailed here are 2 cases of UACs resulting from a CDSS. Both of these cases were related to external systems that fed data into the CDSS. In the first case, lack of knowledge [...]
Author(s): Stone, Erin G
DOI: 10.1093/jamia/ocx096
The purpose of this study was to determine whether an electronic health record-based sepsis alert system could improve quality of care and clinical outcomes for patients with sepsis.
Author(s): Austrian, Jonathan S, Jamin, Catherine T, Doty, Glenn R, Blecker, Saul
DOI: 10.1093/jamia/ocx072
Chronic noncancer pain is a highly prevalent condition among service members returning from deployment overseas. The US Army has a higher rate of opioid misuse than the civilian population. Although most states and many health care systems have implemented prescription drug monitoring programs (PDMPs) or other clinician decision support (CDS) to aid providers in delivering guideline-recommended opioid therapy, similar tools are lacking in military health settings.
Author(s): Finley, Erin P, Schneegans, Suyen, Tami, Claudina, Pugh, Mary Jo, McGeary, Don, Penney, Lauren, Sharpe Potter, Jennifer
DOI: 10.1093/jamia/ocx075