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The re-use of patient data from electronic healthcare record systems can provide tremendous benefits for clinical research, but measures to protect patient privacy while utilizing these records have many challenges. Some of these challenges arise from a misperception that the problem should be solved technically when actually the problem needs a holistic solution.

The quality of colonoscopy procedures for colorectal cancer screening is often inadequate and varies widely among physicians. Routine measurement of quality is limited by the costs of manual review of free-text patient charts. Our goal was to develop a natural language processing (NLP) application to measure colonoscopy quality.

Healthcare delivery organizations are increasingly using online personal health records (PHRs) to provide patients with direct access to their clinical information; however, there may be a lack of consistency in the data made available. We aimed to understand the general use and functionality of PHRs and the organizational policies and decision-making structures for making data available to patients.

Conventional wisdom holds that older, busier clinicians who see complex patients are less likely to adopt and use novel electronic health record (EHR) functionality.

Little information exists concerning SNOMED CT (systematized nomenclature of medicine-clinical terms) users. This report describes current impressions and preferences of direct SNOMED CT users regarding coverage, quality, and concept details, and the change request mechanism.

Many clinical research data integration platforms rely on the Entity-Attribute-Value model because of its flexibility, even though it presents problems in query formulation and execution time. The authors sought more balance in these traits.

To demonstrate that a large, heterogeneous clinical database can reveal fine temporal patterns in clinical associations; to illustrate several types of associations; and to ascertain the value of exploiting time.

To determine whether a rule-based algorithm applied to an outpatient electronic medical record (EMR) can identify patients who are pregnant and prescribed medications proved to cause birth defects.

To categorize the appropriateness of provider and pharmacist responses to warfarin critical drug-drug interaction (cDDI) alerts, assess responses and actions to the cDDI, and determine the occurrence of warfarin adverse drug events (ADE) after alerts.

There are several challenges in encoding guideline knowledge in a form that is portable to different clinical sites, including the heterogeneity of clinical decision support (CDS) tools, of patient data representations, and of workflows.