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Big Data in Studying Acute Pain and Regional Anesthesia
Version 1
: Received: 14 March 2021 / Approved: 15 March 2021 / Online: 15 March 2021 (17:45:39 CET)
A peer-reviewed article of this Preprint also exists.
Müller-Wirtz, L.M.; Volk, T. Big Data in Studying Acute Pain and Regional Anesthesia. J. Clin. Med. 2021, 10, 1425. Müller-Wirtz, L.M.; Volk, T. Big Data in Studying Acute Pain and Regional Anesthesia. J. Clin. Med. 2021, 10, 1425.
Abstract
The digital transformation of healthcare is advancing, leading to an increasing availability of clinical data for research. Perioperative big data initiatives were established to monitor treatment quality and benchmark outcomes. However, big data analyzes have long exceeded the status of pure quality surveillance instruments. Large retrospective studies nowadays often represent the first approach to new questions in clinical research and pave the way for more expensive and resource intensive prospective trials. As a consequence, utilization of big data in acute pain and regional anesthesia research considerably increased over the last decade. Multicentric clinical registries and administrative databases (e.g., healthcare claims databases) have collected millions of cases until today, on which basis several important research questions were approached. In acute pain research, big data was used to assess postoperative pain outcomes, opioid utilization, and the efficiency of multimodal pain management strategies. In regional anesthesia, adverse events and potential benefits of regional anesthesia on postoperative morbidity and mortality were evaluated. This article provides a narrative review on the growing importance of big data for research in acute postoperative pain and regional anesthesia.
Keywords
anesthesia; anesthesiology; big data; registries; database research; acute pain; pain management; postoperative pain; regional anesthesia; regional analgesia.
Subject
Medicine and Pharmacology, Immunology and Allergy
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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