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doi:10.22028/D291-33780
Titel: | Big Data in Studying Acute Pain and Regional Anesthesia |
VerfasserIn: | Müller-Wirtz, Lukas M. Volk, Thomas |
Sprache: | Englisch |
Titel: | Journal of Clinical Medicine |
Bandnummer: | 10 |
Heft: | 7 |
Verlag/Plattform: | MDPI |
Erscheinungsjahr: | 2021 |
Freie Schlagwörter: | anesthesia anesthesiology big data registries database research acute pain pain management postoperative pain regional anesthesia regional analgesia |
DDC-Sachgruppe: | 610 Medizin, Gesundheit |
Dokumenttyp: | Journalartikel / Zeitschriftenartikel |
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 der Erstveröffentlichung: | 10.3390/jcm10071425 |
Link zu diesem Datensatz: | urn:nbn:de:bsz:291--ds-337807 hdl:20.500.11880/31150 http://dx.doi.org/10.22028/D291-33780 |
ISSN: | 2077-0383 |
Datum des Eintrags: | 15-Apr-2021 |
Fakultät: | M - Medizinische Fakultät |
Fachrichtung: | M - Anästhesiologie |
Professur: | M - Prof. Dr. Thomas Volk |
Sammlung: | SciDok - Der Wissenschaftsserver der Universität des Saarlandes |
Dateien zu diesem Datensatz:
Datei | Beschreibung | Größe | Format | |
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jcm-10-01425.pdf | 619,88 kB | Adobe PDF | Öffnen/Anzeigen |
Diese Ressource wurde unter folgender Copyright-Bestimmung veröffentlicht: Lizenz von Creative Commons