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Accurate electronic phenotyping is essential to support collaborative observational research. Supervised machine learning methods can be used to train phenotype classifiers in a high-throughput manner using imperfectly labeled data. We developed 10 phenotype classifiers using this approach and evaluated performance across multiple sites within the Observational Health Data Sciences and Informatics (OHDSI) network.

The use of real-world evidence for health care research and evaluation is growing. Mobile health apps have often-overlooked potential to contribute valuable real-world data that are not captured by other sources and could provide data that are more cost-effective and generalizable than can randomized controlled trials. However, there are several challenges that must be overcome to realize the potential value of patient-used mobile health app real-world data, including data quality [...]

Efforts aimed at increasing the pace of evidence synthesis have been primarily focused on the use of published articles, but these are a relatively delayed, incomplete, and at times biased source of study results data. Compared to those in bibliographic databases, structured results data available in trial registries may be more timely, complete, and accessible, but these data remain underutilized. Key advantages of using structured results data include the potential [...]

To develop a comprehensive and current description of what health informatics (HI) professionals do and what they need to know.

Incomplete and static reaction picklists in the allergy module led to free-text and missing entries that inhibit the clinical decision support intended to prevent adverse drug reactions. We developed a novel, data-driven, "dynamic" reaction picklist to improve allergy documentation in the electronic health record (EHR).

We sought to determine rates of computerized provider order entry (CPOE) patient identity verification and when and where in the ordering process verification occurred.

The goal of this study was to explore whether features of recorded and transcribed audio communication data extracted by machine learning algorithms can be used to train a classifier for anxiety.

Financial impacts associated with a switch to a different electronic health record (EHR) have been documented. Less attention has been focused on the patient response to an EHR switch. The Mayo Clinic was involved in an EHR switch that occurred at 6 different locations and with 4 different "go-live" dates. We sought to understand the relationship between patient satisfaction and the transition to a new EHR.

Timely availability of intravenous infusion pumps is critical for high-quality care delivery. Pumps are shared among hospital units, often without central management of their distribution. This study seeks to characterize unit-to-unit pump sharing and its impact on shortages, and to evaluate a system-control tool that balances inventory across all care areas, enabling increased availability of pumps.

The study sought to determine frequency and appropriateness of overrides of high-priority drug-drug interaction (DDI) alerts and whether adverse drug events (ADEs) were associated with overrides in a newly implemented electronic health record.