Literature DB >> 29595902

The Future of Data-Driven Wound Care.

Jon S Woods, Mayur Saxena, Tasha Nagamine, Raelina S Howell, Theresa Criscitelli, Scott Gorenstein, Brian M Gillette.   

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. © AORN, Inc, 2018.

Entities:  

Keywords:  big data; dataset; machine learning; neural networks; wound care

Mesh:

Year:  2018        PMID: 29595902     DOI: 10.1002/aorn.12102

Source DB:  PubMed          Journal:  AORN J        ISSN: 0001-2092            Impact factor:   0.676


  1 in total

1.  Development of a Method for Clinical Evaluation of Artificial Intelligence-Based Digital Wound Assessment Tools.

Authors:  Raelina S Howell; Helen H Liu; Aziz A Khan; Jon S Woods; Lawrence J Lin; Mayur Saxena; Harshit Saxena; Michael Castellano; Patrizio Petrone; Eric Slone; Ernest S Chiu; Brian M Gillette; Scott A Gorenstein
Journal:  JAMA Netw Open       Date:  2021-05-03
  1 in total

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