Current applications of big data in obstetric anesthesiology.

Abstract

PURPOSE OF REVIEW:

The narrative review aims to highlight several recently published 'big data' studies pertinent to the field of obstetric anesthesiology.

RECENT FINDINGS:

Big data has been used to study rare outcomes, to identify trends within the healthcare system, to identify variations in practice patterns, and to highlight potential inequalities in obstetric anesthesia care. Big data studies have helped define the risk of rare complications of obstetric anesthesia, such as the risk of neuraxial hematoma in thrombocytopenic parturients. Also, large national databases have been used to better understand trends in anesthesia-related adverse events during cesarean delivery as well as outline potential racial/ethnic disparities in obstetric anesthesia care. Finally, real-time analysis of patient data across a number of disparate health information systems through the use of sophisticated clinical decision support and surveillance systems is one promising application of big data technology on the labor and delivery unit.

SUMMARY:

'Big data' research has important implications for obstetric anesthesia care and warrants continued study. Real-time electronic surveillance is a potentially useful application of big data technology on the labor and delivery unit.

• Authors: Thomas T Klumpner, Melissa E Bauer, Sachin Kheterpal
• Journal: Current opinion in anaesthesiology (Curr Opin Anaesthesiol) Vol. 30 Issue 3 Pg. 300-305 (Jun 2017) ISSN: 1473-6500 [Electronic] United States
• PMID: 28277382 (Publication Type: Journal Article, Review)

Topics

• Anesthesia, Obstetrical (adverse effects)
• Cesarean Section (adverse effects)
• Computer Systems (trends)
• Data Interpretation, Statistical
• Databases, Factual (statistics & numerical data)
• Datasets as Topic
• Decision Support Systems, Clinical (trends)
• Delivery, Obstetric (adverse effects)
• Female
• Humans
• Incidence
• Labor, Obstetric
• Postoperative Complications (epidemiology, etiology)
• Pregnancy
• Quality Improvement