Using Machine Learning to Reduce Burden on Infection Control Staff-Reference-Cited by-同舟云学术

Using Machine Learning to Reduce Burden on Infection Control Staff

Published:2022-07-20 Issue:8 Volume:3 Page:
ISSN:2642-0007
Container-title:NEJM Catalyst
language:en
Short-container-title:NEJM Catalyst

Author:

Lukasewicz Ferreira Stephani Amanda¹,Franco Meneses Arateus Crysham²,Vaz Tiago Andres³,da Fontoura Carvalho Otávio Luiz⁴,Dalmora Camila Hubner⁵,Vanni Daiane Pressoto⁶,Berti Isabele Ribeiro⁷,Rocca Fabiane⁸,Ramos Márcio Pereira⁹,dos Santos Rodrigo Pires¹⁰

Affiliation:

1. Nurse Coordinator, Qualis, Porto Alegre, Brazil

2. IT Specialist, Qualis, Porto Alegre, Brazil

3. Chief Technology Officer and Head of Artificial Intelligence, Qualis, Porto Alegre, Brazil

4. Pharmacy Coordinator, Qualis, Porto Alegre, Brazil

5. Partner, Medical Director, and Infectious Diseases Physician, Qualis, Porto Alegre, Brazil

6. Nurse, Infection Control Committee, Tacchini Hospital, Bento Gonçalves, Brazil

7. Infectious Diseases Physician, Tacchini Hospital, Bento Gonçalves, Brazil

8. IT Business Analyst, Tacchini Hospital, Bento Gonçalves, Brazil

9. Hospital Administrator and Quality and Safety Manager, Tacchini Hospital, Bento Gonçalves, Brazil

10. CEO and Founder, and Infectious Diseases Physician, Qualis, Porto Alegre, Brazil

Publisher

Massachusetts Medical Society

Subject

General Medicine

Reference21 articles.

1. Antimicrobial Stewardship Through Telemedicine in a Community Hospital in Southern Brazil

2. Antimicrobial stewardship through telemedicine and its impact on multi-drug resistance

3. Centers for Disease Control and Prevention. National Healthcare Safety Network (NHSN) Patient Safety Component Manual. January 2022. Accessed May 2 2022. https://www.cdc.gov/nhsn/pdfs/pscmanual/pcsmanual_current.pdf.

4. An evaluation of surveillance methods for detecting infections in hospital inpatients

5. Time spent by infection control professionals undertaking healthcare associated infection surveillance: A multi-centred cross sectional study