Data-driven informatics tools targeting patients and providers.
Author(s): Ohno-Machado, Lucila, ,
DOI: 10.1093/jamia/ocw149
Author(s): Ohno-Machado, Lucila, ,
DOI: 10.1093/jamia/ocw149
Author(s): Fridsma, Douglas B
DOI: 10.1093/jamia/ocw146
First, to evaluate the effect of standard vs pictograph-enhanced discharge instructions on patients' immediate and delayed recall of and satisfaction with their discharge instructions. Second, to evaluate the effect of automated pictograph enhancement on patient satisfaction with their discharge instructions.
Author(s): Hill, Brent, Perri-Moore, Seneca, Kuang, Jinqiu, Bray, Bruce E, Ngo, Long, Doig, Alexa, Zeng-Treitler, Qing
DOI: 10.1093/jamia/ocw019
Clinical information models are formal specifications for representing the structure and semantics of the clinical content within electronic health record systems. This research aims to define, test, and validate evaluation metrics for software tools designed to support the processes associated with the definition, management, and implementation of these models.
Author(s): Moreno-Conde, Alberto, Austin, Tony, Moreno-Conde, Jesús, Parra-Calderón, Carlos L, Kalra, Dipak
DOI: 10.1093/jamia/ocw018
Communication inequalities deepen health disparities even when internet access is achieved. The goal of this study is to understand how a range of barriers may inhibit individuals from low socioeconomic position (SEP) from engaging with online health information even when it is freely available.
Author(s): McCloud, Rachel F, Okechukwu, Cassandra A, Sorensen, Glorian, Viswanath, K
DOI: 10.1093/jamia/ocv204
We describe use cases and an institutional reference architecture for maintaining high-capacity, data-intensive network flows (e.g., 10, 40, 100 Gbps+) in a scientific, medical context while still adhering to security and privacy laws and regulations.
Author(s): Peisert, Sean, Barnett, William, Dart, Eli, Cuff, James, Grossman, Robert L, Balas, Edward, Berman, Ari, Shankar, Anurag, Tierney, Brian
DOI: 10.1093/jamia/ocw032
To describe the creation and evaluate the usage of the first medical wiki linked to dedicated mobile applications.
Author(s): Donaldson, Ross I, Ostermayer, Daniel G, Banuelos, Rosa, Singh, Manpreet
DOI: 10.1093/jamia/ocw033
Traditionally, patient groups with a phenotype are selected through rule-based definitions whose creation and validation are time-consuming. Machine learning approaches to electronic phenotyping are limited by the paucity of labeled training datasets. We demonstrate the feasibility of utilizing semi-automatically labeled training sets to create phenotype models via machine learning, using a comprehensive representation of the patient medical record.
Author(s): Agarwal, Vibhu, Podchiyska, Tanya, Banda, Juan M, Goel, Veena, Leung, Tiffany I, Minty, Evan P, Sweeney, Timothy E, Gyang, Elsie, Shah, Nigam H
DOI: 10.1093/jamia/ocw028
To determine problem list completeness related to chronic diseases in electronic medical records (EMRs) and explore clinic and physician factors influencing completeness.
Author(s): Singer, Alexander, Yakubovich, Sari, Kroeker, Andrea L, Dufault, Brenden, Duarte, Roberto, Katz, Alan
DOI: 10.1093/jamia/ocw013
Quantitative Structure-Activity Relationship (QSAR) models can predict adverse drug reactions (ADRs), and thus provide early warnings of potential hazards. Timely identification of potential safety concerns could protect patients and aid early diagnosis of ADRs among the exposed. Our objective was to determine whether global spontaneous reporting patterns might allow chemical substructures associated with Stevens-Johnson Syndrome (SJS) to be identified and utilized for ADR prediction by QSAR models.
Author(s): Low, Yen S, Caster, Ola, Bergvall, Tomas, Fourches, Denis, Zang, Xiaoling, Norén, G Niklas, Rusyn, Ivan, Edwards, Ralph, Tropsha, Alexander
DOI: 10.1093/jamia/ocv127