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We evaluated the extent to which studies that tested short message service (SMS)- and application (app)-based interventions for diabetes self-management education and support (DSMES) report on factors that inform both internal and external validity as measured by the RE-AIM (Reach, Efficacy/Effectiveness, Adoption, Implementation, and Maintenance) framework.

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 [...]

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.

In recent years, the OpenNotes movement and other changes in healthcare have driven institutions to make medical records increasingly transparent. As patients have begun to question and request changes to their Problem Lists, clinicians have come to face the ever more frequent challenge of discerning which changes to make and which to refuse. Now clinicians and patients together choose the list of problems that represent the patient's current state of [...]

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 [...]

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.

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 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.

This article reports results from a systematic literature review of the current state of mobile health (mHealth) technologies that have the potential to support self-management for people with diabetes and hypertension. The review aims to (a) characterize mHealth technologies used or described in the mHealth literature and (b) summarize their effects on self-management for people with diabetes and hypertension from the clinical and technical standpoints.