President's column: AMIA's policy priorities for 2014.
Author(s): Middleton, Blackford
DOI: 10.1136/amiajnl-2014-002809
Author(s): Middleton, Blackford
DOI: 10.1136/amiajnl-2014-002809
The constant progress in computational linguistic methods provides amazing opportunities for discovering information in clinical text and enables the clinical scientist to explore novel approaches to care. However, these new approaches need evaluation. We describe an automated system to compare descriptions of epilepsy patients at three different organizations: Cincinnati Children's Hospital, the Children's Hospital Colorado, and the Children's Hospital of Philadelphia. To our knowledge, there have been no similar previous [...]
Author(s): Connolly, Brian, Matykiewicz, Pawel, Bretonnel Cohen, K, Standridge, Shannon M, Glauser, Tracy A, Dlugos, Dennis J, Koh, Susan, Tham, Eric, Pestian, John
DOI: 10.1136/amiajnl-2013-002601
On July 1, 2012 Australia launched a personally controlled electronic health record (PCEHR) designed around the needs of consumers. Using a distributed model and leveraging key component national eHealth infrastructure, the PCEHR is designed to enable sharing of any health information about a patient with them and any other health practitioner involved in their care to whom the patient allows access. This paper discusses the consumer-facing part of the program.
Author(s): Pearce, Christopher, Bainbridge, Michael
DOI: 10.1136/amiajnl-2013-002068
We developed the Medication Extraction and Normalization (MedXN) system to extract comprehensive medication information and normalize it to the most appropriate RxNorm concept unique identifier (RxCUI) as specifically as possible.
Author(s): Sohn, Sunghwan, Clark, Cheryl, Halgrim, Scott R, Murphy, Sean P, Chute, Christopher G, Liu, Hongfang
DOI: 10.1136/amiajnl-2013-002190
This study describes the implementation and impact of an electronic test result acknowledgement (RA) system in the Mater Mothers' Hospital in Brisbane, Australia. The Verdi application electronically records clinicians' acknowledgement of the review of results. Hospital data (August 2011-August 2012) were extracted to measure clinicians' acknowledgement practices. There were 27,354 inpatient test results for 6855 patients. All test results were acknowledged. 60% (95% CI 59% to 61%) of laboratory and [...]
Author(s): Georgiou, Andrew, Lymer, Sharyn, Forster, Megan, Strachan, Michael, Graham, Sara, Hirst, Geof, Callen, Joanne, Westbrook, Johanna I
DOI: 10.1136/amiajnl-2013-002466
Author(s): Ohno-Machado, Lucila
DOI: 10.1136/amiajnl-2014-002666
A recent report from the Institute of Medicine titled Delivering High-Quality Cancer Care: Charting a New Course for a System in Crisis, identifies improvement in information technology (IT) as essential to improving the quality of cancer care in America. The report calls for implementation of a learning healthcare IT system: a system that supports patient-clinician interactions by providing patients and clinicians with the information and tools necessary to make well [...]
Author(s): Feeley, Thomas W, Sledge, George W, Levit, Laura, Ganz, Patricia A
DOI: 10.1136/amiajnl-2013-002346
Named entity recognition (NER) is one of the fundamental tasks in natural language processing. In the medical domain, there have been a number of studies on NER in English clinical notes; however, very limited NER research has been carried out on clinical notes written in Chinese. The goal of this study was to systematically investigate features and machine learning algorithms for NER in Chinese clinical text.
Author(s): Lei, Jianbo, Tang, Buzhou, Lu, Xueqin, Gao, Kaihua, Jiang, Min, Xu, Hua
DOI: 10.1136/amiajnl-2013-002381
Adverse drug reaction (ADR) can have dire consequences. However, our current understanding of the causes of drug-induced toxicity is still limited. Hence it is of paramount importance to determine molecular factors of adverse drug responses so that safer therapies can be designed.
Author(s): Liu, Mei, Cai, Ruichu, Hu, Yong, Matheny, Michael E, Sun, Jingchun, Hu, Jun, Xu, Hua
DOI: 10.1136/amiajnl-2013-002051
The rapidly growing volume of multimodal electrophysiological signal data is playing a critical role in patient care and clinical research across multiple disease domains, such as epilepsy and sleep medicine. To facilitate secondary use of these data, there is an urgent need to develop novel algorithms and informatics approaches using new cloud computing technologies as well as ontologies for collaborative multicenter studies.
Author(s): Sahoo, Satya S, Jayapandian, Catherine, Garg, Gaurav, Kaffashi, Farhad, Chung, Stephanie, Bozorgi, Alireza, Chen, Chien-Hun, Loparo, Kenneth, Lhatoo, Samden D, Zhang, Guo-Qiang
DOI: 10.1136/amiajnl-2013-002156