Search AMIA.org

The objective was to develop non-invasive predictive models for late-onset neonatal sepsis from off-the-shelf medical data and electronic medical records (EMR).

Electronic health records (EHRs) contain information to detect adverse drug reactions (ADRs), as they contain comprehensive clinical information. A major challenge of using comprehensive information involves confounding. We propose a novel data-driven method to identify ADR signals accurately by adjusting for confounders.

Using electronic health records (EHR) to automate publicly reported quality measures is receiving increasing attention and is one of the promises of EHR implementation. Kaiser Permanente has fully or partly automated six of 13 the joint commission measure sets. We describe our experience with automation and the resulting time savings: a reduction by approximately 50% of abstractor time required for one measure set alone (surgical care improvement project). However, our [...]

To examine the use of online social networking for cardiovascular care using Facebook. All posts and comments in a Facebook group between June 2011 and May 2012 were reviewed, and a survey was conducted. A total of 298 members participated. Of the 277 wall posts, 26.7% were question posts requesting rapid replies, and 50.5% were interesting cases shared with other members. The median response time for the question posts was [...]

Clinical databases require accurate entity resolution (ER). One approach is to use algorithms that assign questionable cases to manual review. Few studies have compared the performance of common algorithms for such a task. Furthermore, previous work has been limited by a lack of objective methods for setting algorithm parameters. We compared the performance of common ER algorithms: using algorithmic optimization, rather than manual parameter tuning, and on two-threshold classification (match/manual [...]

Electronic health records (EHR) hold great promise for managing patient information in ways that improve healthcare delivery. Physicians differ, however, in their use of this health information technology (IT), and these differences are not well understood. The authors study the differences in individual physicians' EHR use patterns and identify perceptions of uncertainty as an important new variable in understanding EHR use.

Epilepsy encompasses an extensive array of clinical and research subdomains, many of which emphasize multi-modal physiological measurements such as electroencephalography and neuroimaging. The integration of structured, unstructured, and signal data into a coherent structure for patient care as well as clinical research requires an effective informatics infrastructure that is underpinned by a formal domain ontology.

To better understand the relationship between online health-seeking behaviors and in-world healthcare utilization (HU) by studies of online search and access activities before and after queries that pursue medical professionals and facilities.