Ziel Ziel der Studie war es, unter Verwendung einer Software-Applikation die Untersuchungsdauern und Wechselzeiten von 2 klinisch stark frequentierten MRT-Scannern einer Universitätsklinik für Radiologie zu analysieren und zu evaluieren, ob sich daraus ein Optimierungspotenzial für die Untersuchungsplanung in der täglichen klinischen Routine der MRT-Diagnostik ableiten lässt. Material und Methoden Anhand einer detaillierten Abfrage mit einer neu entwickelten Software-Applikation („Teamplay Usage“, Siemens Healthineers, Deutschland) wurden innerhalb eines Analysezeitraums von 12 Monaten an 2 MRT-Scannern (1,5 T und 3 T) die durchgeführten Untersuchungen im Hinblick auf Untersuchungsart und jeweilige Untersuchungsdauer analysiert. Zudem erfolgte eine Überprüfung der Einhaltung vorab definierter Planzeiten (30, 45, 60 min) und eine Analyse von Planzeitabweichungen. Des Weiteren wurden Wechselzeiten zwischen Untersuchungen ermittelt und bei einer Auswahl von Wechselkombinationen ein möglicher Einfluss durch den Austausch von MRT-Spulen untersucht. Ergebnisse Bei insgesamt 7184 (1,5T: 3740; 3T: 3444) in die Studie einbezogenen Untersuchungen betrug die mediane Untersuchungsdauer 43:02 Minuten (1,5T: 43:17 min; 3T: 42:45 min). Die 10 häufigsten Untersuchungsarten je MRT-Scanner wurden unter Berücksichtigung einer Vor- und Nachbereitungszeit von 9 Minuten je Untersuchung zu 54,5 % (1,5 T) bzw. 51,9 % (3 T) innerhalb der vordefinierten Planzeit abgeschlossen. Gesamthaft betrachtet wurde für Untersuchungen mit einer Planzeit von 30 Minuten mehr Zeit aufgewendet, hingegen wurde der größte Anteil der mit 45 Minuten geplanten Untersuchungen auch innerhalb dieser Zeit abgeschlossen. Untersuchungen mit einer Planzeit von 60 Minuten nahmen zumeist weniger Zeit in Anspruch. Ein Vergleich zwischen Planzeit und ermittelter Untersuchungsdauer der häufigsten Untersuchungsarten zeigte insgesamt ein nur geringes Optimierungspotenzial. Spulenaustausche zwischen 2 Untersuchungen hatten einen geringen, jedoch statistisch nicht signifikanten Effekt auf die mediane Wechselzeit (p = 0,062). Schlussfolgerung Mittels einer Software-basierten Analyse konnte ein detaillierter Überblick in Bezug auf Untersuchungsart, Untersuchungsdauer und Wechselzeiten hochfrequentierter klinischer MRT-Scanner erlangt werden. In der untersuchten Klinik ließ sich ein geringes Optimierungspotenzial für die Untersuchungsplanung ableiten. Ein für unterschiedliche Untersuchungsarten notwendiger Austausch von MRT-Spulen hatte einen geringen Effekt auf die Wechselzeiten. Kernaussagen Zitierweise
Laboratory medicine is a digital science. Every large hospital produces a wealth of data each day—from simple numerical results from, e.g., sodium measurements to highly complex output of “-omics” analyses, as well as quality control results and metadata. Processing, connecting, storing, and ordering extensive parts of these individual data requires Big Data techniques. Whereas novel technologies such as artificial intelligence and machine learning have exciting application for the augmentation of laboratory medicine, the Big Data concept remains fundamental for any sophisticated data analysis in large databases. To make laboratory medicine data optimally usable for clinical and research purposes, they need to be FAIR: findable, accessible, interoperable, and reusable. This can be achieved, for example, by automated recording, connection of devices, efficient ETL (Extract, Transform, Load) processes, careful data governance, and modern data security solutions. Enriched with clinical data, laboratory medicine data allow a gain in pathophysiological insights, can improve patient care, or can be used to develop reference intervals for diagnostic purposes. Nevertheless, Big Data in laboratory medicine do not come without challenges: the growing number of analyses and data derived from them is a demanding task to be taken care of. Laboratory medicine experts are and will be needed to drive this development, take an active role in the ongoing digitalization, and provide guidance for their clinical colleagues engaging with the laboratory data in research.
Laboratory medicine is a digital science. Every large hospital produces a wealth of data each day - from simple numerical results from e.g. sodium measurements to highly complex output of “-omics” analyses, as well as quality control results and meta-data. Processing, connecting, storing, and ordering extensive parts of these individual data requires Big Data techniques. Whereas novel technologies such as artificial intelligence and machine learning have exciting application for the augmentation of laboratory medicine, the Big Data concept remains fundamental for any sophisticated data analysis in large databases. To make laboratory medicine data optimally usable for clinical and research purposes, they need to be FAIR: findable, accessible, interoperable, and reusable. This can be achieved for example by automated recording, connection of devices, efficient ETL (Extract, Transform, Load) processes, careful data governance, and modern data security solutions. Enriched with clinical data, laboratory medicine data allow a gain in pathophysiological insights, can improve patient care, or they can be used to develop reference intervals for diagnostic purposes. Nevertheless, Big Data in laboratory medicine do not come without challenges: The growing number of analyses and data derived from them is a demanding task to be taken care of. Laboratory medicine experts are and will be needed to drive this development, take an active role in the ongoing digitalization, and provide guidance for their clinical colleagues engaging with the laboratory data in research.
Purpose: Swiss BioRef is a nation-wide multicenter infrastructure project, the aim of which is to become a sustainable framework for the estimation and assessment of patient-group-specific reference intervals in laboratory medicine and beyond. In this unprecedented effort, nation-wide multidimensional data from multiple clinical laboratory databases has been combined under the common interoperable semantic framework of the Swiss Personalized Health Network (SPHN) initiative. The consolidated effort enables creating extremely detailed patient group-specific queries via intuitive web applications, allowing the generation of individualised, covariate adjusted reference intervals on-the-fly. Participants: The project is a collaborative effort of four major hospitals in Switzerland, the University Hospital Bern (Inselspital, Insel), University Hospital Lausanne (CHUV), Swiss Spinal Cord Injury Cohort (SwiSCI) and the University Children's Hospital Zurich (KiSpi), and two academic groups in Bern and in Lausanne. Findings to date: Within the infrastructure we deployed, the laboratory data from four major hospitals (approximately 9 million measurements from 250'000 patients) is made available to two conceptually different web applications (one centralised and statistically detailed, one decentralised using distributed computing). They enable the inference of reference intervals for more than 40 blood test variables from clinical chemistry, hematology, point-of-care-testing and coagulation testing, with various patient factors (such as age, sex and a combination of ICD-10 defined diagnoses) and analytical factors (such as type or unique identifiers) that can be used to generate precise reference intervals for the respective groups. Future plans: Now that all required basic infrastructure elements for Swiss BioRef are deployed, we are evaluating inter-cohort transferability of semantic standards, change tracking in merged databases and biological variation of the blood test variables, in order to generate precise reference intervals. While adjusting the developed web-interfaces to suit the needs of the various end-users, we additionally plan to onboard new national and international partners.
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