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A learning health care system (LHS) uses routinely collected data to continuously monitor and improve health care outcomes. Little is reported on the challenges and methods used to implement the analytics underpinning an LHS. Our aim was to systematically review the literature for reports of real-time clinical analytics implementation in digital hospitals and to use these findings to synthesize a conceptual framework for LHS implementation.

Molecular tumor boards (MTBs) cope with the complexity of an increased usage of genome sequencing data in cancer treatment. As for most of these patients, guideline-based therapy options are exhausted, finding matching clinical trials is crucial. This search process is often performed manually and therefore time consuming and complex due to the heterogeneous and challenging dataset.

Hospitals are increasingly replacing pagers with clinical texting systems that allow users to use smartphones to send messages while maintaining compliance for privacy and security. As more institutions adopt such systems, the need to understand the impact of such transitions on team communication becomes ever more significant.

Clinical decision support systems (CDSSs) use alerts to enhance medication safety and reduce medication error rates. A major challenge of medication alerts is their low acceptance rate, limiting their potential benefit. A structured overview about modulators influencing alert acceptance is lacking. Therefore, we aimed to review and compile qualitative and quantitative modulators of alert acceptance and organize them in a comprehensive model.

In 2009, Schattauer Verlag in Stuttgart, Germany first published the Applied Clinical Informatics (ACI) Journal. ACI has served since its inception as an official journal of the International Medical Informatics Association. Later, the American Medical Informatics Association and the European Federation for Medical Informatics named ACI as an official journal. This manuscript describes the history of the journal from its inception to present day including publication measures, challenges, and successes.

Many critically ill children are initially evaluated in front-line settings by clinicians with variable pediatric training before they are transferred to a pediatric intensive care unit (PICU). Because clinicians learn from past performance, communicating outcomes of patients back to front-line clinicians who provide pediatric emergency care could be valuable; however, referring clinicians do not consistently receive this important feedback.

The widespread adoption of electronic health records and a simultaneous increase in regulatory demands have led to an acceleration of documentation requirements among clinicians. The corresponding burden from documentation requirements is a central contributor to clinician burnout and can lead to an increased risk of suboptimal patient care.

The purpose of this study is to evaluate the ability of three metrics to monitor for a reduction in performance of a chronic kidney disease (CKD) model deployed at a pediatric hospital.

Postpartum depression (PPD) remains an understudied research area despite its high prevalence. The goal of this study is to develop an ontology to aid in the identification of patients with PPD and to enable future analyses with electronic health record (EHR) data.