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The use of risk prediction models grows as electronic medical records become widely available. Here, we develop and validate a model to identify individuals at increased risk for colorectal cancer (CRC) by analyzing blood counts, age, and sex, then determine the model's value when used to supplement conventional screening.

Electronic medical records (EMRs) are revolutionizing health-related research. One key issue for study quality is the accurate identification of patients with the condition of interest. Information in EMRs can be entered as structured codes or unstructured free text. The majority of research studies have used only coded parts of EMRs for case-detection, which may bias findings, miss cases, and reduce study quality. This review examines whether incorporating information from text [...]

A fundamental challenge in the field of clinical decision support is to determine what characteristics of systems make them effective in supporting particular types of clinical decisions. However, we lack such a theory of decision support itself and a model to describe clinical decisions and the systems to support them. This article outlines such a framework. We present a two-stream model of information flow within clinical decision-support systems (CDSSs): reasoning [...]

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.

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 [...]

We sought to assess the potential of a widely available source of electronic medication data to prevent medication history errors and resultant inpatient order errors.

A software tool is developed to facilitate data entry and to monitor research projects in under-resourced countries in real-time.

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.

Quantify the variability of patients' problem lists - in terms of the number, type, and ordering of problems - across multiple physicians and assess physicians' criteria for organizing and ranking diagnoses.

Patients in intensive care units (ICUs) may lack decisional capacity and may depend on proxy decision makers (PDMs) to make medical decisions on their behalf. High-quality information-sharing with PDMs, including through such means as health information technology, could improve communication and decision making and could potentially minimize the psychological consequences of an ICU stay for both patients and their family members. However, alongside these anticipated benefits of information-sharing are risks [...]