Putting the 'i' in iHealth.
Author(s): Middleton, Blackford, Fickenscher, Kevin M
DOI: 10.1136/amiajnl-2013-002537
Author(s): Middleton, Blackford, Fickenscher, Kevin M
DOI: 10.1136/amiajnl-2013-002537
Tuberculosis (TB) surveillance in China is organized through a nationwide network of about 3200 hospitals and health facilities. In 2005, an electronic Tuberculosis Information Management System (TBIMS) started to be phased in to replace paper recording. The TBIMS collects key information on TB cases notified in TB care facilities, and exchanges real-time data with the Infectious Disease Reporting System, which covers the country's 37 notifiable diseases. The system is accessible [...]
Author(s): Huang, Fei, Cheng, ShiMing, Du, Xin, Chen, Wei, Scano, Fabio, Falzon, Dennis, Wang, Lixia
DOI: 10.1136/amiajnl-2013-002001
Pharmacogenetics (PG) examines gene variations for drug disposition, response, or toxicity. At the National Institutes of Health Clinical Center (NIH CC), a multidepartment Pharmacogenetics Testing Implementation Committee (PGTIC) was established to develop clinical decision support (CDS) algorithms for abacavir, carbamazepine, and allopurinol, medications for which human leukocyte antigen (HLA) variants predict severe hypersensitivity reactions. Providing PG CDS in the electronic health record (EHR) during order entry could prevent adverse drug [...]
Author(s): Goldspiel, Barry R, Flegel, Willy A, DiPatrizio, Gary, Sissung, Tristan, Adams, Sharon D, Penzak, Scott R, Biesecker, Leslie G, Fleisher, Thomas A, Patel, Jharana J, Herion, David, Figg, William D, Lertora, Juan J L, McKeeby, Jon W
DOI: 10.1136/amiajnl-2013-001873
We postulate that professional proximity due to common patients and geographical proximity among practice locations are significant factors influencing the adoption of health information exchange (HIE) services by healthcare providers. The objective of this study is to investigate the direct and indirect network effects of these drivers on HIE diffusion.
Author(s): Yaraghi, Niam, Du, Anna Ye, Sharman, Raj, Gopal, Ram D, Ramesh, R, Singh, Ranjit, Singh, Gurdev
DOI: 10.1136/amiajnl-2012-001293
Learning of classification models in medicine often relies on data labeled by a human expert. Since labeling of clinical data may be time-consuming, finding ways of alleviating the labeling costs is critical for our ability to automatically learn such models. In this paper we propose a new machine learning approach that is able to learn improved binary classification models more efficiently by refining the binary class information in the training [...]
Author(s): Nguyen, Quang, Valizadegan, Hamed, Hauskrecht, Milos
DOI: 10.1136/amiajnl-2013-001964
Adverse drug events, the unintended and harmful effects of medications, are important outcome measures in health services research. Yet no universally accepted set of International Classification of Diseases (ICD) revision 10 codes or coding algorithms exists to ensure their consistent identification in administrative data. Our objective was to synthesize a comprehensive set of ICD-10 codes used to identify adverse drug events.
Author(s): Hohl, Corinne M, Karpov, Andrei, Reddekopp, Lisa, Doyle-Waters, Mimi, Stausberg, Jürgen
DOI: 10.1136/amiajnl-2013-002116
Ambiguous definitions of quality measures in natural language impede their automated computability and also the reproducibility, validity, timeliness, traceability, comparability, and interpretability of computed results. Therefore, quality measures should be formalized before their release. We have previously developed and successfully applied a method for clinical indicator formalization (CLIF). The objective of our present study is to test whether CLIF is generalizable--that is, applicable to a large set of heterogeneous measures [...]
Author(s): Dentler, Kathrin, Numans, Mattijs E, ten Teije, Annette, Cornet, Ronald, de Keizer, Nicolette F
DOI: 10.1136/amiajnl-2013-001921
As healthcare systems and providers move toward meaningful use of electronic health records, longitudinal care plans (LCPs) may provide a means to improve communication and coordination as patients transition across settings. The objective of this study was to determine the current state of communication of LCPs across settings and levels of care.
Author(s): Dykes, Patricia C, Samal, Lipika, Donahue, Moreen, Greenberg, Jeffrey O, Hurley, Ann C, Hasan, Omar, O'Malley, Terrance A, Venkatesh, Arjun K, Volk, Lynn A, Bates, David W
DOI: 10.1136/amiajnl-2013-002454
To reliably extract two entity types, symptoms and conditions (SCs), and drugs and treatments (DTs), from patient-authored text (PAT) by learning lexico-syntactic patterns from data annotated with seed dictionaries.
Author(s): Gupta, Sonal, MacLean, Diana L, Heer, Jeffrey, Manning, Christopher D
DOI: 10.1136/amiajnl-2014-002669
Consumers facing barriers to healthcare access may use online health information seeking and online communication with physicians, but the empirical relationship has not been sufficiently analyzed. Our study examines the association of barriers to healthcare access with consumers' health-related information searching on the internet, use of health chat groups, and email communication with physicians, using data from 27,210 adults from the 2009 National Health Interview Survey. Individuals with financial barriers [...]
Author(s): Bhandari, Neeraj, Shi, Yunfeng, Jung, Kyoungrae
DOI: 10.1136/amiajnl-2013-002350