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Experts suggest that formulary alerts at the time of medication order entry are the most effective form of clinical decision support to automate formulary management.

The objective of this project was to use statistical techniques to determine the completeness and accuracy of data migrated during electronic health record conversion.

To examine whether patients invited to review their clinicians' notes continue to access them and to assess the impact of reminders on whether patients continued to view notes.

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.

To demonstrate use of the electronic health record (EHR) for health insurance surveillance and identify factors associated with lack of coverage.

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.