Response to 'Use of an algorithm for identifying hidden drug-drug interactions in adverse event reports' by Gooden et al.
Author(s): Tatonetti, Nicholas P, Denny, Joshua C, Altman, Russ B
DOI: 10.1136/amiajnl-2012-001603
Author(s): Tatonetti, Nicholas P, Denny, Joshua C, Altman, Russ B
DOI: 10.1136/amiajnl-2012-001603
Author(s): Fickenscher, Kevin M
DOI: 10.1136/amiajnl-2013-001768
To extract drug indications from structured drug labels and represent the information using codes from standard medical terminologies.
Author(s): Fung, Kin Wah, Jao, Chiang S, Demner-Fushman, Dina
DOI: 10.1136/amiajnl-2012-001291
Social networks have been used in the study of outbreaks of infectious diseases, including in small group settings such as individual hospitals. Collecting the data needed to create such networks, however, can be time consuming, costly, and error prone. We sought to create a social network of hospital inpatients using electronic medical record (EMR) data already collected for other purposes, for use in simulating outbreaks of nosocomial infections.
Author(s): Cusumano-Towner, Marco, Li, Daniel Y, Tuo, Shanshan, Krishnan, Gomathi, Maslove, David M
DOI: 10.1136/amiajnl-2012-001401
With increasing use electronic health records (EHR) in the USA, we looked at the predictive values of the International Classification of Diseases, 9th revision (ICD-9) coding system for surveillance of chronic hepatitis B virus (HBV) infection.
Author(s): Mahajan, Reena, Moorman, Anne C, Liu, Stephen J, Rupp, Loralee, Klevens, R Monina, ,
DOI: 10.1136/amiajnl-2012-001558
A sizable fraction of patients experiences adverse drug events or lack of drug efficacy. A part of this variability in drug response can be explained by genetic differences between patients. However, pharmacogenomic data as well as computational clinical decision support systems for interpreting such data are still unavailable in most healthcare settings. We address this problem by introducing the medicine safety code (MSC), which captures compressed pharmacogenomic data in a [...]
Author(s): Samwald, Matthias, Adlassnig, Klaus-Peter
DOI: 10.1136/amiajnl-2012-001275
To determine whether indication-based computer order entry alerts intercept wrong-patient medication errors.
Author(s): Galanter, William, Falck, Suzanne, Burns, Matthew, Laragh, Marci, Lambert, Bruce L
DOI: 10.1136/amiajnl-2012-001555
This paper describes our considerations and methods for implementing an open-source centralized research data repository (CRDR) and reports its impact on retrospective outcomes research capacity in the urology department at Columbia University. We performed retrospective pretest and post-test analyses of user acceptance, workflow efficiency, and publication quantity and quality (measured by journal impact factor) before and after the implementation. The CRDR transformed the research workflow and enabled a new research [...]
Author(s): Hruby, Gregory William, McKiernan, James, Bakken, Suzanne, Weng, Chunhua
DOI: 10.1136/amiajnl-2012-001302
To adapt and automate the medication regimen complexity index (MRCI) within the structure of a commercial medication database in the post-acute home care setting.
Author(s): McDonald, Margaret V, Peng, Timothy R, Sridharan, Sridevi, Foust, Janice B, Kogan, Polina, Pezzin, Liliana E, Feldman, Penny H
DOI: 10.1136/amiajnl-2012-001272
Patient portal use has been associated with favorable outcomes, but we know less about how patients use and benefit from specific patient portal features.
Author(s): Wade-Vuturo, Ashley E, Mayberry, Lindsay Satterwhite, Osborn, Chandra Y
DOI: 10.1136/amiajnl-2012-001253