Transforming consumer health informatics: connecting CHI applications to the health-IT ecosystem.
Author(s): Marceglia, Sara, Fontelo, Paul, Ackerman, Michael J
DOI: 10.1093/jamia/ocu030
Author(s): Marceglia, Sara, Fontelo, Paul, Ackerman, Michael J
DOI: 10.1093/jamia/ocu030
In alignment with a major shift toward patient-centered care as the model for improving care in our health system, informatics is transforming patient-provider relationships and overall care delivery. AMIA's 2013 Health Policy Invitational was focused on examining existing challenges surrounding full engagement of the patient and crafting a research agenda and policy framework encouraging the use of informatics solutions to achieve this goal. The group tackled this challenge from educational [...]
Author(s): Brennan, Patti Flatley, Valdez, Rupa, Alexander, Greg, Arora, Shifali, Bernstam, Elmer V, Edmunds, Margo, Kirienko, Nikolai, Martin, Ross D, Sim, Ida, Skiba, Diane, Rosenbloom, Trent
DOI: 10.1136/amiajnl-2014-003176
Radiology reports are usually narrative, unstructured text, a format which hinders the ability to input report contents into decision support systems. In addition, reports often describe multiple lesions, and it is challenging to automatically extract information on each lesion and its relationships to characteristics, anatomic locations, and other information that describes it. The goal of our work is to develop natural language processing (NLP) methods to recognize each lesion in [...]
Author(s): Bozkurt, Selen, Lipson, Jafi A, Senol, Utku, Rubin, Daniel L
DOI: 10.1136/amiajnl-2014-003009
To improve the accuracy of mining structured and unstructured components of the electronic medical record (EMR) by adding temporal features to automatically identify patients with rheumatoid arthritis (RA) with methotrexate-induced liver transaminase abnormalities.
Author(s): Lin, Chen, Karlson, Elizabeth W, Dligach, Dmitriy, Ramirez, Monica P, Miller, Timothy A, Mo, Huan, Braggs, Natalie S, Cagan, Andrew, Gainer, Vivian, Denny, Joshua C, Savova, Guergana K
DOI: 10.1136/amiajnl-2014-002642
To evaluate the contribution of the MEDication Indication (MEDI) resource and SemRep for identifying treatment relations in clinical text.
Author(s): Bejan, Cosmin Adrian, Wei, Wei-Qi, Denny, Joshua C
DOI: 10.1136/amiajnl-2014-002954
To integrate data elements from multiple sources for informing comprehensive and standardized collection of family health history (FHH).
Author(s): Chen, Elizabeth S, Carter, Elizabeth W, Winden, Tamara J, Sarkar, Indra Neil, Wang, Yan, Melton, Genevieve B
DOI: 10.1136/amiajnl-2014-003092
We study the use of speech recognition and information extraction to generate drafts of Australian nursing-handover documents.
Author(s): Suominen, Hanna, Johnson, Maree, Zhou, Liyuan, Sanchez, Paula, Sirel, Raul, Basilakis, Jim, Hanlen, Leif, Estival, Dominique, Dawson, Linda, Kelly, Barbara
DOI: 10.1136/amiajnl-2014-002868
This research identifies specific care coordination activities used by Aging in Place (AIP) nurse care coordinators and home healthcare (HHC) nurses when coordinating care for older community-dwelling adults and suggests a method to quantify care coordination.
Author(s): Popejoy, Lori L, Khalilia, Mohammed A, Popescu, Mihail, Galambos, Colleen, Lyons, Vanessa, Rantz, Marilyn, Hicks, Lanis, Stetzer, Frank
DOI: 10.1136/amiajnl-2014-002702
We developed and implemented a 'CPOE-QT Alert' system, that is, clinical decision support integrated in the computerized physician order entry system (CPOE), in 2011. The system identifies any attempts to order medications with risk of torsade de pointes (TdP) for patients with a history of significant QT prolongation (QTc ≥500 ms) and alerts the provider entering the order. We assessed its impact by comparing orders and subsequent medication administration before [...]
Author(s): Sorita, Atsushi, Bos, J Martijn, Morlan, Bruce W, Tarrell, Robert F, Ackerman, Michael J, Caraballo, Pedro J
DOI: 10.1136/amiajnl-2014-002896
Mobile health (mHealth) customers shopping for applications (apps) should be aware of app privacy practices so they can make informed decisions about purchase and use. We sought to assess the availability, scope, and transparency of mHealth app privacy policies on iOS and Android. Over 35,000 mHealth apps are available for iOS and Android. Of the 600 most commonly used apps, only 183 (30.5%) had privacy policies. Average policy length was [...]
Author(s): Sunyaev, Ali, Dehling, Tobias, Taylor, Patrick L, Mandl, Kenneth D
DOI: 10.1136/amiajnl-2013-002605