The Future of Data‐Driven Wound Care. (29th March 2018)

• Record Type: Journal Article
• Authors: Woods, Jon S.; Saxena, Mayur; Nagamine, Tasha; Howell, Raelina S.; Criscitelli, Theresa; Gorenstein, Scott; M. Gillette, Brian
• Is Part Of: AORN journal. Volume 107:Number 4(2018:Apr.)
• Page Start: 455
• Page End: 463
• Publication Date: 2018-03-29
• Subjects: big data -- machine learning -- dataset -- neural networks -- wound care; Operating room nursing -- Periodicals; Soins infirmiers en salle d'opération -- Périodiques; Nursing -- Periodicals; Operating Room Nursing -- Periodicals; Perioperative Nursing -- Periodicals; Societies, Nursing -- Periodicals; 617.917
• DOI: 10.1002/aorn.12102
• Languages: English
• ISSNs: 0001-2092

Abstract

ABSTRACT: Care for patients with chronic wounds can be complex, and the chances of poor outcomes are high if wound care is not optimized through evidence‐based protocols. Tracking and managing every variable and comorbidity in patients with wounds is difficult despite the increasing use of wound‐specific electronic medical records. Harnessing the power of big data analytics to help nurses and physicians provide optimized care based on the care provided to millions of patients can result in better outcomes. Numerous applications of machine learning toward workflow improvements, inpatient monitoring, outpatient communication, and hospital operations can improve overall efficiency and efficacy of care delivery in and out of the hospital, while reducing adverse events and complications. This article provides an overview of the application of big data analytics and machine learning in health care, highlights important recent advances, and discusses how these technologies may revolutionize advanced wound care.