Showing papers in "Journal of the American Medical Informatics Association in 2015"

TL;DR: A machine learning-based approach to extract mentions of adverse drug reactions (ADRs) from highly informal text in social media, suitable for social media mining, as it relies on large volumes of unlabeled data, thus diminishing the need for large, annotated training data sets.

TL;DR: Overall, SNS interventions appeared to be effective in promoting changes in health-related behaviors, and further research regarding the application of these promising tools is warranted.

TL;DR: The specific research questions that emerged from the workshop, alongside the potential for diverse communities to assemble to address them through a ‘new science of learning systems’, create an important agenda for informatics and related disciplines.

TL;DR: The findings show that currently mHealth developers often fail to provide app privacy policies, and the privacy policies that are available do not make information privacy practices transparent to users, require college-level literacy, and are often not focused on the app itself.

TL;DR: This study investigates the use of visualization techniques reported between 1996 and 2013 and evaluates innovative approaches to information visualization of electronic health record (EHR) data for knowledge discovery.

TL;DR: EHR data suggested that the use of metformin was associated with decreased mortality after a cancer diagnosis compared with diabetic and non-diabetic cancer patients not on meetformin, indicating its potential as a chemotherapeutic regimen.

TL;DR: Advancing a patient work approach within CHI is integral to developing and deploying consumer-facing technologies that are integrated with patients' everyday lives.

TL;DR: A research team visited 11 different EHR vendors in order to analyze their UCD processes and discover the specific challenges that vendors faced as they sought to integrate UCD with their EHR development.

TL;DR: Standardizing data structure, content, and analytics can enable an institution to apply a network-based approach to observational research across multiple, disparate observational health databases.

TL;DR: This review examines work on automated summarization of electronic health record (EHR) data and in particular, individual patient record summarization with a particular focus on methods for detecting and removing redundancy, describing temporality, determining salience, accounting for missing data, and taking advantage of encoded clinical knowledge.

TL;DR: Recommendations are provided for healthcare organizations and IT vendors to improve the clinician interface of DDI alerts, with the aim of reducing alert fatigue and improving patient safety.

TL;DR: In this article, the authors report that EHR adoption has had unintended clinical consequences, including reduced time for patient-clinician interaction, new and burdensome data entry tasks being transferred to front-line clinicians, and lengthened clinician workdays.

TL;DR: The task of disorder normalization is more challenging than that of identification, and the ShARe corpus is available to the community as a reference standard for future studies.

TL;DR: In this article, a method to develop phenotyping algorithms in an unbiased manner by automatically extracting and selecting informative features, which can be comparable to expert-curated ones in classification accuracy, was introduced.

TL;DR: There are stark health literacy, educational, and racial disparities in the registration, and subsequent use of an online patient portal among older adults, and intervention strategies are needed to monitor and reduce disparities in their use.

TL;DR: Despite the many hopes that access to more information would lead to more informed decisions, access to comprehensive and large-scale clinical data resources has instead made some analytical processes even more difficult.

TL;DR: Initial assessment of the patient experience within the first 9 months of availability provides evidence that patients both value and benefit from online access to clinical notes.

TL;DR: Smartphone apps can be used to deliver a screening tool for depression across a large number of countries and have the potential to play a significant role in disease screening, self-management, monitoring, and health education, particularly among younger adults.

TL;DR: A computable PheRM is needed for true phenotype portability and reliability across different EHR products and healthcare systems, and desiderata are a guide to inform the establishment and evolution of EHR phenotype algorithm authoring platforms and languages.

TL;DR: HARVEST was designed to address the unmet need for clinicians at the point of care, facilitating review of essential patient information, and provides an opportunity to learn how clinicians use the summarizer, enabling informed interface and content iteration and optimization to improve patient care.

TL;DR: This prototyping work suggests that an entirely data (and web) standards-based approach could prove both effective and efficient for advancing personalized medicine.

TL;DR: Responsiveness to the public's concerns regarding their health information is a pre-requisite for patient-centeredness and Responsiveness to these needs, rather than mere reliance on Health Insurance Portability and Accountability Act (HIPAA), may improve support of data networks.

TL;DR: The implications of this emerging monoculture: its advantages and disadvantages for physicians and hospitals and its role in innovation, professional autonomy, implementation difficulties, workflow, flexibility, cost, data standards, interoperability, and interactions with other information technology systems are examined.

TL;DR: Software that standardizes the assignment of a unique seeded hash identifier merged through an agreed upon third-party honest broker can enable large-scale secure linkage of EHR data for epidemiologic and public health research.

TL;DR: This systematic literature review finds highly reliable data handling methods, human resources and effective project management, as well as technical architecture and infrastructure are all key factors for successful EMR implementation.

TL;DR: Lifetime disease risk is affected by birth month and seasonally dependent early developmental mechanisms may play a role in increasing lifetime risk of disease.

TL;DR: A proof-of-concept web service that implements a distributed algorithm to conduct distributed survival analysis without sharing patient level data and shows that the implementation of the distributed model can achieve the same results as the centralized implementation.

TL;DR: Early experiences indicate that the resulting digital infrastructure is being used to improve quality of care and curtail costs, and reform efforts are however severely limited by problems with usability, limited interoperability and the persistence of the fee-for-service paradigm.

TL;DR: A risk prediction model that takes longitudinal laboratory test results and clinical documentation into consideration can predict CKD progression from stage III to stage IV more accurately than three models that do not take all of these variables into consideration.

TL;DR: The Center for Expanded Data Annotation and Retrieval is studying the creation of comprehensive and expressive metadata for biomedical datasets to facilitate data discovery, data interpretation, and data reuse.