Search AMIA.org

Alzheimer disease (AD) is the most common cause of dementia, a syndrome characterized by cognitive impairment severe enough to interfere with activities of daily life. We aimed to conduct a systematic literature review (SLR) of studies that applied machine learning (ML) methods to clinical data derived from electronic health records in order to model risk for progression of AD dementia.

The development of clinical predictive models hinges upon the availability of comprehensive clinical data. Tapping into such resources requires considerable effort from clinicians, data scientists, and engineers. Specifically, these efforts are focused on data extraction and preprocessing steps required prior to modeling, including complex database queries. A handful of software libraries exist that can reduce this complexity by building upon data standards. However, a gap remains concerning electronic health records [...]

[This corrects the article DOI: 10.1093/jamiaopen/ooab008.].

Although the value of collecting occupational data is well-established, these data are not systematically collected in clinical practice. We assessed the availability of electronic health record (EHR)-based occupation data within a large integrated health care system to determine the feasibility of its use in research.

The objectives of this pilot study were (1) to assess the feasibility of a larger evaluation of Smart About Meds (SAM), a patient-centered medication management mobile application, and (2) to evaluate SAM's potential to improve outcomes of interest, including adherence to medication changes made at hospital discharge and the occurrence of adverse events.

Documentation at the bedside is still often initiated on paper before being entered in electronic charts, even after implementing electronic health records (EHRs). This 2-step process is time-consuming, a potential source of error, and hinders the use of real-time information. We developed the "Bedside mobility" smartphone application to facilitate bedside documentation in the EHR.

Note entry and review in electronic health records (EHRs) are time-consuming. While some clinics have adopted team-based models of note entry, how these models have impacted note review is unknown in outpatient specialty clinics such as ophthalmology. We hypothesized that ophthalmologists and ancillary staff review very few notes. Using audit log data from 9775 follow-up office visits in an academic ophthalmology clinic, we found ophthalmologists reviewed a median of 1 [...]

To establish an enterprise initiative for improving health and health care through interoperable electronic health record (EHR) innovations.

With the extensive deployment of electronic medical record (EMR) systems, EMR usability remains a significant source of frustration to clinicians. There is a significant research need for software that emulates EMR systems and enables investigators to conduct laboratory-based human-computer interaction studies. We developed an open-source software package that implements the display functions of an EMR system. The user interface emphasizes the temporal display of vital signs, medication administrations, and laboratory [...]

Delivering clinical decision support (CDS) at the point of care has long been considered a major advantage of computerized physician order entry (CPOE). Despite the widespread implementation of CPOE, medication ordering errors and associated adverse events still occur at an unacceptable level. Previous attempts at indication- and kidney function-based dosing have mostly employed intrusive CDS, including interruptive alerts with poor usability. This descriptive work describes the design, development, and deployment [...]