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Record linkage to integrate uncoordinated databases is critical in biomedical research using Big Data. Balancing privacy protection against the need for high quality record linkage requires a human-machine hybrid system to safely manage uncertainty in the ever changing streams of chaotic Big Data.

To systematically review mobile applications currently available to patients to support outpatient medication self-management.

Integrating data from multiple sources is a crucial and challenging problem. Even though there exist numerous algorithms for record linkage or deduplication, they suffer from either large time needs or restrictions on the number of datasets that they can integrate. In this paper we report efficient sequential and parallel algorithms for record linkage which handle any number of datasets and outperform previous algorithms.

Electronic health records databases are increasingly used for identifying cohort populations, covariates, or outcomes, but discerning such clinical 'phenotypes' accurately is an ongoing challenge. We developed a flexible method using overlapping (Venn diagram) queries. Here we describe this approach to find patients hospitalized with acute congestive heart failure (CHF), a sampling strategy for one-by-one 'gold standard' chart review, and calculation of positive predictive value (PPV) and sensitivities, with SEs, across [...]

Using numeric laboratory data and administrative data from hospital electronic health record (EHR) systems, to develop an inpatient mortality predictive model.

The aim of this study was to describe an integrated informatics approach to aggregating and displaying clinically relevant data that can identify problems with medication adherence and facilitate patient-provider communication about strategies to improve medication use. We developed a clinical dashboard within an electronic health record (EHR) system that uses data from three sources: the medical record, pharmacy claims, and a personal health record. The data are integrated to inform [...]

Current readmission models use administrative data supplemented with clinical information. However, the majority of these result in poor predictive performance (area under the curve (AUC)<0.70).

Providing patients with access to their medical data is widely expected to help educate and empower them to manage their own health. Health information exchange (HIE) infrastructures could potentially help patients access records across multiple healthcare providers. We studied three HIE organizations as they developed portals to give consumers access to HIE data previously exchanged only among healthcare organizations.

Common chronic diseases such as hypertension are costly and difficult to manage. Our ultimate goal is to use data from electronic health records to predict the risk and timing of deterioration in hypertension control. Towards this goal, this work predicts the transition points at which hypertension is brought into, as well as pushed out of, control.

To present a series of experiments: (1) to evaluate the impact of pre-annotation on the speed of manual annotation of clinical trial announcements; and (2) to test for potential bias, if pre-annotation is utilized.