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
Adverse drug events cause substantial morbidity and mortality and are often discovered after a drug comes to market. We hypothesized that Internet users may provide early clues about adverse drug events via their online information-seeking. We conducted a large-scale study of Web search log data gathered during 2010. We pay particular attention to the specific drug pairing of paroxetine and pravastatin, whose interaction was reported to cause hyperglycemia after the [...]
Author(s): White, Ryen W, Tatonetti, Nicholas P, Shah, Nigam H, Altman, Russ B, Horvitz, Eric
DOI: 10.1136/amiajnl-2012-001482
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
Medication errors in hospitals are common, expensive, and sometimes harmful to patients. This study's objective was to derive a nationally representative estimate of medication error reduction in hospitals attributable to electronic prescribing through computerized provider order entry (CPOE) systems.
Author(s): Radley, David C, Wasserman, Melanie R, Olsho, Lauren Ew, Shoemaker, Sarah J, Spranca, Mark D, Bradshaw, Bethany
DOI: 10.1136/amiajnl-2012-001241
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
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
The internet is increasingly being used to conduct randomized controlled trials (RCTs). Knowledge of the types of interventions evaluated and the methodological quality of these trials could inform decisions about whether to conduct future trials using conventional methods, fully online or a mixture of the two.
Author(s): Mathieu, Erin, McGeechan, Kevin, Barratt, Alexandra, Herbert, Robert
DOI: 10.1136/amiajnl-2012-001175
The increasing availability of clinical data from electronic medical records (EMRs) has created opportunities for secondary uses of health information. When used in machine learning classification, many data features must first be transformed by discretization.
Author(s): Maslove, David M, Podchiyska, Tanya, Lowe, Henry J
DOI: 10.1136/amiajnl-2012-000929
Medical visualization tools have traditionally been constrained to tethered imaging workstations or proprietary client viewers, typically part of hospital radiology systems. To improve accessibility to real-time, remote, interactive, stereoscopic visualization and to enable collaboration among multiple viewing locations, we developed an open source approach requiring only a standard web browser with no added client-side software.
Author(s): Kaspar, Mathias, Parsad, Nigel M, Silverstein, Jonathan C
DOI: 10.1136/amiajnl-2012-001057