Search AMIA.org

In early 2010, Harvard Medical School and Boston Children's Hospital began an interoperability project with the distinctive goal of developing a platform to enable medical applications to be written once and run unmodified across different healthcare IT systems. The project was called Substitutable Medical Applications and Reusable Technologies (SMART).

Natural language processing methods for medical auto-coding, or automatic generation of medical billing codes from electronic health records, generally assign each code independently of the others. They may thus assign codes for closely related procedures or diagnoses to the same document, even when they do not tend to occur together in practice, simply because the right choice can be difficult to infer from the clinical narrative.

Reinventing data extraction from electronic health records (EHRs) to meet new analytical needs is slow and expensive. However, each new data research network that wishes to support its own analytics tends to develop its own data model. Joining these different networks without new data extraction, transform, and load (ETL) processes can reduce the time and expense needed to participate. The Informatics for Integrating Biology and the Bedside (i2b2) project supports [...]

In order to facilitate clinical research across multiple institutions, data harmonization is a critical requirement. Common data elements (CDEs) collect data uniformly, allowing data interoperability between research studies. However, structural limitations have hindered the application of CDEs. An advanced modeling structure is needed to rectify such limitations. The openEHR 2-level modeling approach has been widely implemented in the medical informatics domain. The aim of our study is to explore the [...]

The metadata reflecting the location of the infected host (LOIH) of virus sequences in GenBank often lacks specificity. This work seeks to enhance this metadata by extracting more specific geographic information from related full-text articles and mapping them to their latitude/longitudes using knowledge derived from external geographical databases.

To systematically review studies assessing the effects of health information technology (health IT) on patient safety outcomes.

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.

The Clinical Genome Resource (ClinGen) Electronic Health Record (EHR) Workgroup aims to integrate ClinGen resources with EHRs. A promising option to enable this integration is through the Health Level Seven (HL7) Infobutton Standard. EHR systems that are certified according to the US Meaningful Use program provide HL7-compliant infobutton capabilities, which can be leveraged to support clinical decision-making in genomics.

A structured report for imaging exams aims at increasing the precision in information retrieval and communication between physicians. However, it is more concise than free text and may limit specialists' descriptions of important findings not covered by pre-defined structures. A computational ontological structure derived from free texts designed by specialists may be a solution for this problem. Therefore, the goal of our study was to develop a methodology for structuring [...]

The integration of clinical decision support (CDS) in documentation practices remains limited due to obstacles in provider workflows and design restrictions in electronic health records (EHRs). The use of electronic problem-oriented templates (POTs) as a CDS has been previously discussed but not widely studied.