President's column: subspecialty certification in clinical informatics.
Author(s): Shortliffe, Edward H
DOI: 10.1136/amiajnl-2011-000582
Author(s): Shortliffe, Edward H
DOI: 10.1136/amiajnl-2011-000582
To provide an overview and tutorial of natural language processing (NLP) and modern NLP-system design.
Author(s): Nadkarni, Prakash M, Ohno-Machado, Lucila, Chapman, Wendy W
DOI: 10.1136/amiajnl-2011-000464
Author(s): Ohno-Machado, Lucila
DOI: 10.1136/amiajnl-2011-000501
Many Dutch hospitals have established internal systems for reporting incidents. However, such internal systems do not allow learning from incidents that occur in other hospitals. Therefore a multicenter, information technology (IT) supported reporting system named central medication incidents registration (CMR) was developed. This article describes the architecture, implementation and current status of the CMR in The Netherlands and compare it with similar systems in other countries.
Author(s): Cheung, Ka-Chun, van den Bemt, Patricia M L A, Bouvy, Marcel L, Wensing, Michel, De Smet, Peter A G M
DOI: 10.1136/amiajnl-2011-000191
To compare the use of structured reporting software and the standard electronic medical records (EMR) in the management of patients with bladder cancer. The use of a human factors laboratory to study management of disease using simulated clinical scenarios was also assessed.
Author(s): Bostrom, Peter J, Toren, Paul J, Xi, Hao, Chow, Raymond, Truong, Tran, Liu, Justin, Lane, Kelly, Legere, Laura, Chagpar, Anjum, Zlotta, Alexandre R, Finelli, Antonio, Fleshner, Neil E, Grober, Ethan D, Jewett, Michael A S
DOI: 10.1136/amiajnl-2011-000221
To evaluate the incidence of duplicate medication orders before and after computerized provider order entry (CPOE) with clinical decision support (CDS) implementation and identify contributing factors.
Author(s): Wetterneck, Tosha B, Walker, James M, Blosky, Mary Ann, Cartmill, Randi S, Hoonakker, Peter, Johnson, Mark A, Norfolk, Evan, Carayon, Pascale
DOI: 10.1136/amiajnl-2011-000255
Concept extraction is a process to identify phrases referring to concepts of interests in unstructured text. It is a critical component in automated text processing. We investigate the performance of machine learning taggers for clinical concept extraction, particularly the portability of taggers across documents from multiple data sources.
Author(s): Torii, Manabu, Wagholikar, Kavishwar, Liu, Hongfang
DOI: 10.1136/amiajnl-2011-000155
To evaluate existing automatic speech-recognition (ASR) systems to measure their performance in interpreting spoken clinical questions and to adapt one ASR system to improve its performance on this task.
Author(s): Liu, Feifan, Tur, Gokhan, Hakkani-Tür, Dilek, Yu, Hong
DOI: 10.1136/amiajnl-2010-000071
The 2010 i2b2/VA Workshop on Natural Language Processing Challenges for Clinical Records presented three tasks: a concept extraction task focused on the extraction of medical concepts from patient reports; an assertion classification task focused on assigning assertion types for medical problem concepts; and a relation classification task focused on assigning relation types that hold between medical problems, tests, and treatments. i2b2 and the VA provided an annotated reference standard corpus [...]
Author(s): Uzuner, Özlem, South, Brett R, Shen, Shuying, DuVall, Scott L
DOI: 10.1136/amiajnl-2011-000203
Health-information exchange, that is, enabling the interoperability of automated health data, can facilitate important improvements in healthcare quality and efficiency. A vision of interoperability and its benefits was articulated more than a decade ago. Since then, important advances toward the goal have been made. The advent of the Health Information Technology for Economic and Clinical Health Act and the meaningful use program is already having a significant impact on the [...]
Author(s): Kuperman, Gilad J
DOI: 10.1136/amiajnl-2010-000021