{"title":"Clinical Informatics in Critical Care Medicine.","authors":"Girish N Nadkarni, Ankit Sakhuja","doi":"10.59249/WTTU3055","DOIUrl":null,"url":null,"abstract":"<p><p>Continuous monitoring and treatment of patients in intensive care units generates vast amounts of data. Critical Care Medicine clinicians incorporate this continuously evolving data to make split-second, life or death decisions for management of these patients. Despite the abundance of data, it can be challenging to consider every accessible data point when making the quick decisions necessary at the point of care. Consequently, Clinical Informatics offers a natural partnership to improve the care for critically ill patients. The last two decades have seen a significant evolution in the role of Clinical Informatics in Critical Care Medicine. In this review, we will discuss how Clinical Informatics improves the care of critically ill patients by enhancing not only data collection and visualization but also bedside medical decision making. We will further discuss the evolving role of machine learning algorithms in Clinical Informatics as it pertains to Critical Care Medicine.</p>","PeriodicalId":48617,"journal":{"name":"Yale Journal of Biology and Medicine","volume":"96 3","pages":"397-405"},"PeriodicalIF":2.5000,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/61/1f/yjbm_96_3_397.PMC10524812.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Yale Journal of Biology and Medicine","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.59249/WTTU3055","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/9/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Continuous monitoring and treatment of patients in intensive care units generates vast amounts of data. Critical Care Medicine clinicians incorporate this continuously evolving data to make split-second, life or death decisions for management of these patients. Despite the abundance of data, it can be challenging to consider every accessible data point when making the quick decisions necessary at the point of care. Consequently, Clinical Informatics offers a natural partnership to improve the care for critically ill patients. The last two decades have seen a significant evolution in the role of Clinical Informatics in Critical Care Medicine. In this review, we will discuss how Clinical Informatics improves the care of critically ill patients by enhancing not only data collection and visualization but also bedside medical decision making. We will further discuss the evolving role of machine learning algorithms in Clinical Informatics as it pertains to Critical Care Medicine.
期刊介绍：
The Yale Journal of Biology and Medicine (YJBM) is a graduate and medical student-run, peer-reviewed, open-access journal dedicated to the publication of original research articles, scientific reviews, articles on medical history, personal perspectives on medicine, policy analyses, case reports, and symposia related to biomedical matters. YJBM is published quarterly and aims to publish articles of interest to both physicians and scientists. YJBM is and has been an internationally distributed journal with a long history of landmark articles. Our contributors feature a notable list of philosophers, statesmen, scientists, and physicians, including Ernst Cassirer, Harvey Cushing, Rene Dubos, Edward Kennedy, Donald Seldin, and Jack Strominger. Our Editorial Board consists of students and faculty members from Yale School of Medicine and Yale University Graduate School of Arts & Sciences. All manuscripts submitted to YJBM are first evaluated on the basis of scientific quality, originality, appropriateness, contribution to the field, and style. Suitable manuscripts are then subject to rigorous, fair, and rapid peer review.
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