Please use this identifier to cite or link to this item: doi:10.22028/D291-33780
Title: Big Data in Studying Acute Pain and Regional Anesthesia
Author(s): Müller-Wirtz, Lukas M.
Volk, Thomas
Language: English
Title: Journal of Clinical Medicine
Volume: 10
Issue: 7
Publisher/Platform: MDPI
Year of Publication: 2021
Free key words: anesthesia
anesthesiology
big data
registries
database research
acute pain
pain management
postoperative pain
regional anesthesia
regional analgesia
DDC notations: 610 Medicine and health
Publikation type: Journal Article
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 analyses 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, the utilization of big data in acute pain and regional anesthesia research has 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.
DOI of the first publication: 10.3390/jcm10071425
Link to this record: urn:nbn:de:bsz:291--ds-337807
hdl:20.500.11880/31150
http://dx.doi.org/10.22028/D291-33780
ISSN: 2077-0383
Date of registration: 15-Apr-2021
Faculty: M - Medizinische Fakultät
Department: M - Anästhesiologie
Professorship: M - Prof. Dr. Thomas Volk
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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