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To pilot benchmark measures of health information and communication technology (ICT) availability and use to facilitate cross-country learning.

We explored whether use of deep learning to model temporal relations among events in electronic health records (EHRs) would improve model performance in predicting initial diagnosis of heart failure (HF) compared to conventional methods that ignore temporality.

Electronic trigger detection tools hold promise to reduce Adverse drug event (ADEs) through efficiencies of scale and real-time reporting. We hypothesized that such a tool could automatically detect medication dosing errors as well as manage and evaluate dosing rule modifications.

To develop a descriptive model of structural characteristics of mHealth in the context of newborn nutrition, and to assess the effects of illustrative interventions through a mixed-methods study consisting of an impact evaluation and a qualitative assessment.

The study objective was to evaluate the accuracy, validity, and clinical usefulness of medication error alerts generated by an alerting system using outlier detection screening.

Using Failure Mode and Effects Analysis (FMEA) as an example quality improvement approach, our objective was to evaluate whether secondary use of orders, forms, and notes recorded by the electronic health record (EHR) during daily practice can enhance the accuracy of process maps used to guide improvement. We examined discrepancies between expected and observed activities and individuals involved in a high-risk process and devised diagnostic measures for understanding discrepancies that [...]

To understand the different types and causes of prescribing errors associated with computerized provider order entry (CPOE) systems, and recommend improvements in these systems.

Agency for Healthcare Research and Quality (AHRQ) software applies standardized algorithms to hospital administrative data to identify patient safety indicators (PSIs). The objective of this study was to assess the validity of PSI flags and report reasons for invalid flagging.

While potentially reducing decision errors, decision support systems can introduce new types of errors. Automation bias (AB) happens when users become overreliant on decision support, which reduces vigilance in information seeking and processing. Most research originates from the human factors literature, where the prevailing view is that AB occurs only in multitasking environments.

Government agencies are rapidly developing web portals to proactively publish "open" data that are searchable, available in nonproprietary formats, and with unlimited use and distribution rights. In this dynamic environment, we aimed to understand the experiences of 2 early leaders in open health data, the US Department of Health and Human Services and the New York State Department of Health.