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A growing body of observational data enabled its secondary use to facilitate clinical care for complex cases not covered by the existing evidence. We conducted a scoping review to characterize clinical decision support systems (CDSSs) that generate new knowledge to provide guidance for such cases in real time.

Improving the patient experience has become an essential component of any healthcare system's performance metrics portfolio. In this study, we developed a machine learning model to predict a patient's response to the Hospital Consumer Assessment of Healthcare Providers and Systems survey's "Doctor Communications" domain questions while simultaneously identifying most impactful providers in a network.

Disease surveillance systems are expanding using electronic health records (EHRs). However, there are many challenges in this regard. In the present study, the solutions and challenges of implementing EHR-based disease surveillance systems (EHR-DS) have been reviewed.

To determine the content priorities and design preferences for a longitudinal care plan (LCP) among caregivers and healthcare providers who care for children with medical complexity (CMC) in acute care settings.

In applying machine learning (ML) to electronic health record (EHR) data, many decisions must be made before any ML is applied; such preprocessing requires substantial effort and can be labor-intensive. As the role of ML in health care grows, there is an increasing need for systematic and reproducible preprocessing techniques for EHR data. Thus, we developed FIDDLE (Flexible Data-Driven Pipeline), an open-source framework that streamlines the preprocessing of data extracted [...]

Randomized controlled trials (RCTs) are the gold standard method for evaluating whether a treatment works in health care but can be difficult to find and make use of. We describe the development and evaluation of a system to automatically find and categorize all new RCT reports.

The rise of digital data and computing power have contributed to significant advancements in artificial intelligence (AI), leading to the use of classification and prediction models in health care to enhance clinical decision-making for diagnosis, treatment and prognosis. However, such advances are limited by the lack of reporting standards for the data used to develop those models, the model architecture, and the model evaluation and validation processes. Here, we present [...]

To synthesize data quality (DQ) dimensions and assessment methods of real-world data, especially electronic health records, through a systematic scoping review and to assess the practice of DQ assessment in the national Patient-centered Clinical Research Network (PCORnet).

Studies that use patient smartphones to collect ecological momentary assessment and sensor data, an approach frequently referred to as digital phenotyping, have increased in popularity in recent years. There is a lack of formal guidelines for the design of new digital phenotyping studies so that they are powered to detect both population-level longitudinal associations as well as individual-level change points in multivariate time series. In particular, determining the appropriate balance [...]