Literature DB >> 33626583

Are We Ready for Video Recognition and Computer Vision in the Intensive Care Unit? A Survey.

Alzbeta Glancova1, Quan T Do1, Devang K Sanghavi2, Pablo Moreno Franco3, Neethu Gopal4, Lindsey M Lehman5, Yue Dong6, Brian W Pickering1, Vitaly Herasevich7.   

Abstract

OBJECTIVE: Video recording and video recognition (VR) with computer vision have become widely used in many aspects of modern life. Hospitals have employed VR technology for security purposes, however, despite the growing number of studies showing the feasibility of VR software for physiologic monitoring or detection of patient movement, its use in the intensive care unit (ICU) in real-time is sparse and the perception of this novel technology is unknown. The objective of this study is to understand the attitudes of providers, patients, and patient's families toward using VR in the ICU.
DESIGN: A 10-question survey instrument was used and distributed into two groups of participants: clinicians (MDs, advance practice providers, registered nurses), patients and families (adult patients and patients' relatives). Questions were specifically worded and section for free text-comments created to elicit respondents' thoughts and attitudes on potential issues and barriers toward implementation of VR in the ICU.
SETTING: The survey was conducted at Mayo Clinic in Minnesota and Florida.
RESULTS: A total of 233 clinicians' and 50 patients' surveys were collected. Both cohorts favored VR under specific circumstances (e.g., invasive intervention and diagnostic manipulation). Acceptable reasons for VR usage according to clinicians were anticipated positive impact on patient safety (70%), and diagnostic suggestions and decision support (51%). A minority of providers was concerned that artificial intelligence (AI) would replace their job (14%) or erode professional skills (28%). The potential use of VR in lawsuits (81% clinicians) and privacy breaches (59% patients) were major areas of concern. Further identified barriers were lack of trust for AI, deterioration of the patient-clinician rapport. Patients agreed with VR unless it does not reduce nursing care or record sensitive scenarios.
CONCLUSION: The survey provides valuable information on the acceptance of VR cameras in the critical care setting including an overview of real concerns and attitudes toward the use of VR technology in the ICU. Thieme. All rights reserved.

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Mesh:

Year:  2021        PMID: 33626583      PMCID: PMC7904385          DOI: 10.1055/s-0040-1722614

Source DB:  PubMed          Journal:  Appl Clin Inform        ISSN: 1869-0327            Impact factor:   2.342


  17 in total

1.  Automatically detecting pain in video through facial action units.

Authors:  Patrick Lucey; Jeffrey F Cohn; Iain Matthews; Simon Lucey; Sridha Sridharan; Jessica Howlett; Kenneth M Prkachin
Journal:  IEEE Trans Syst Man Cybern B Cybern       Date:  2010-11-22

2.  Automated Assessment of Children's Postoperative Pain Using Computer Vision.

Authors:  Karan Sikka; Alex A Ahmed; Damaris Diaz; Matthew S Goodwin; Kenneth D Craig; Marian S Bartlett; Jeannie S Huang
Journal:  Pediatrics       Date:  2015-06-01       Impact factor: 7.124

Review 3.  Alarm Safety and Alarm Fatigue.

Authors:  Kendall R Johnson; James I Hagadorn; David W Sink
Journal:  Clin Perinatol       Date:  2017-07-14       Impact factor: 3.430

4.  Satisfaction and Improvements in Peritoneal Dialysis Outcomes Associated with Telehealth.

Authors:  Manya Magnus; Neal Sikka; Teena Cherian; Susie Q Lew
Journal:  Appl Clin Inform       Date:  2017-03-01       Impact factor: 2.342

Review 5.  Impact of Electronic Medical Record Use on the Patient-Doctor Relationship and Communication: A Systematic Review.

Authors:  Maria Alcocer Alkureishi; Wei Wei Lee; Maureen Lyons; Valerie G Press; Sara Imam; Akua Nkansah-Amankra; Deb Werner; Vineet M Arora
Journal:  J Gen Intern Med       Date:  2016-01-19       Impact factor: 5.128

6.  A Survey on Computer Vision for Assistive Medical Diagnosis From Faces.

Authors:  Jerome Thevenot; Miguel Bordallo Lopez; Abdenour Hadid
Journal:  IEEE J Biomed Health Inform       Date:  2017-10-05       Impact factor: 5.772

Review 7.  The significance of circadian rhythms and dysrhythmias in critical illness.

Authors:  Helen T McKenna; Irwin Km Reiss; Daniel S Martin
Journal:  J Intensive Care Soc       Date:  2017-02-13

8.  Intelligent ICU for Autonomous Patient Monitoring Using Pervasive Sensing and Deep Learning.

Authors:  Anis Davoudi; Kumar Rohit Malhotra; Benjamin Shickel; Scott Siegel; Seth Williams; Matthew Ruppert; Emel Bihorac; Tezcan Ozrazgat-Baslanti; Patrick J Tighe; Azra Bihorac; Parisa Rashidi
Journal:  Sci Rep       Date:  2019-05-29       Impact factor: 4.379

9.  Research on Non-Contact Monitoring System for Human Physiological Signal and Body Movement.

Authors:  Qiancheng Liang; Lisheng Xu; Nan Bao; Lin Qi; Jingjing Shi; Yicheng Yang; Yudong Yao
Journal:  Biosensors (Basel)       Date:  2019-04-19

10.  Machine learning in intelligent video and automated monitoring.

Authors:  Yu-Bo Yuan; Gao Yang David; Shan Zhao
Journal:  ScientificWorldJournal       Date:  2015-04-09
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