Development and implementation of databases to track patient and safety outcomes

Abstract

Purpose of reviewRecent advancements in big data analytical tools and large patient databases have expanded tremendously the opportunities to track patient and safety outcomes.We discuss the strengths and limitations of large databases and implementation in practice with a focus on the current opportunities to use technological advancements to improve patient safety.Recent findingsThe most used sources of data for large patient safety observational studies are administrative databases, clinical registries, and electronic health records. These data sources have enabled research on patient safety topics ranging from rare adverse outcomes to large cohort studies of the modalities for pain control and safety of medications. Implementing the insights from big perioperative data research is augmented by automating data collection and tracking the safety outcomes on a provider, institutional, national, and global level. In the near future, big data from wearable devices, physiological waveforms, and genomics may lead to the development of personalized outcome measures.SummaryPatient safety research using large databases can provide actionable insights to improve outcomes in the perioperative setting. As datasets and methods to gain insights from those continue to grow, adopting novel technologies to implement personalized quality assurance initiatives can significantly improve patient care.

Opportunities of AI-powered applications in anesthesiology to enhance patient safety

“…The most commonly used data sources are electronic health records, administrative databases, and specialized data sets. 8 The data sources available for model development in anesthesiology have limitations as large databases may not contain sufficiently granular data. In contrast, electronic health records data have significant depth but may require extensive processing.…”

“…This field has far-reaching applications that have become part of our daily lives, such as traffic navigation, spam filtering, shopping, and online searching. In anesthesiology, the amount and complexity of data have been increasing exponentially, 8 and AI technologies, ideally suited for processing these data, can provide valuable insights.…”

“…The models should be developed using large and granular data sets. The most commonly used data sources are electronic health records, administrative databases, and specialized data sets 8 . The data sources available for model development in anesthesiology have limitations as large databases may not contain sufficiently granular data.…”