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Clinical decision support (CDS) is essential for delivery of high-quality, cost-effective, and safe healthcare. The authors sought to evaluate the CDS capabilities across electronic health record (EHR) systems.

We have identified 5 use cases that comprise a useful definition of an "open or interoperable electronic health record (EHR)." Each of these use cases represents important functionality that should be available to 1) clinicians, so they can provide safe and effective health care; 2) researchers, so they can advance our understanding of disease and health care processes; 3) administrators, so they can reduce their reliance on a single-source EHR [...]

To develop and test an instrument for assessing a healthcare organization's ability to mitigate malpractice risk through clinical decision support (CDS).

Analysis of narrative (text) data from electronic health records (EHRs) can improve population-scale phenotyping for clinical and genetic research. Currently, selection of text features for phenotyping algorithms is slow and laborious, requiring extensive and iterative involvement by domain experts. This paper introduces a method to develop phenotyping algorithms in an unbiased manner by automatically extracting and selecting informative features, which can be comparable to expert-curated ones in classification accuracy.

The Health Insurance Portability and Accountability Act Privacy Rule enables healthcare organizations to share de-identified data via two routes. They can either 1) show re-identification risk is small (e.g., via a formal model, such as k-anonymity) with respect to an anticipated recipient or 2) apply a rule-based policy (i.e., Safe Harbor) that enumerates attributes to be altered (e.g., dates to years). The latter is often invoked because it is interpretable [...]

Electronic health data may improve the timeliness and accuracy of resource-intense contact investigations (CIs) in healthcare settings.

Identifying patients who are medication nonpersistent (fail to refill in a timely manner) is important for healthcare operations and research. However, consistent methods to detect nonpersistence using electronic pharmacy records are presently lacking. We developed and validated a nonpersistence algorithm for chronically used medications.

Literature-based discovery (LBD) aims to identify "hidden knowledge" in the medical literature by: (1) analyzing documents to identify pairs of explicitly related concepts (terms), then (2) hypothesizing novel relations between pairs of unrelated concepts that are implicitly related via a shared concept to which both are explicitly related. Many LBD approaches use simple techniques to identify semantically weak relations between concepts, for example, document co-occurrence. These generate huge numbers of [...]

Extracting medical knowledge from electronic medical records requires automated approaches to combat scalability limitations and selection biases. However, existing machine learning approaches are often regarded by clinicians as black boxes. Moreover, training data for these automated approaches at often sparsely annotated at best. The authors target unsupervised learning for modeling clinical narrative text, aiming at improving both accuracy and interpretability.

To design and implement a tool that creates a secure, privacy preserving linkage of electronic health record (EHR) data across multiple sites in a large metropolitan area in the United States (Chicago, IL), for use in clinical research.