The future of automated infection detection: Innovation to transform practice (Part III/III)
Current methods of emergency-room–based syndromic surveillance were insufficient to detect early community spread of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) in the United States, which slowed the infection prevention and control response to the novel pathogen. Emerging technologies...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2023-01-01
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| Series: | Antimicrobial Stewardship & Healthcare Epidemiology |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2732494X22003333/type/journal_article |
| _version_ | 1811157220506730496 |
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| author | Westyn Branch-Elliman Alexander J. Sundermann Jenna Wiens Erica S. Shenoy |
| author_facet | Westyn Branch-Elliman Alexander J. Sundermann Jenna Wiens Erica S. Shenoy |
| author_sort | Westyn Branch-Elliman |
| collection | DOAJ |
| description | Current methods of emergency-room–based syndromic surveillance were insufficient to detect early community spread of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) in the United States, which slowed the infection prevention and control response to the novel pathogen. Emerging technologies and automated infection surveillance have the potential to improve upon current practice standards and to revolutionize the practice of infection detection, prevention and control both inside and outside of healthcare settings. Genomics, natural language processing, and machine learning can be leveraged to improve identification of transmission events and aid and evaluate outbreak response. In the near future, automated infection detection strategies can be used to advance a true “Learning Healthcare System” that will support near–real-time quality improvement efforts and advance the scientific basis for the practice of infection control. |
| first_indexed | 2024-04-10T05:03:29Z |
| format | Article |
| id | doaj.art-40138c3594784c61b1c3a163065238be |
| institution | Directory Open Access Journal |
| issn | 2732-494X |
| language | English |
| last_indexed | 2024-04-10T05:03:29Z |
| publishDate | 2023-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Antimicrobial Stewardship & Healthcare Epidemiology |
| spelling | doaj.art-40138c3594784c61b1c3a163065238be2023-03-09T12:27:54ZengCambridge University PressAntimicrobial Stewardship & Healthcare Epidemiology2732-494X2023-01-01310.1017/ash.2022.333The future of automated infection detection: Innovation to transform practice (Part III/III)Westyn Branch-Elliman0https://orcid.org/0000-0002-9658-5124Alexander J. Sundermann1Jenna Wiens2Erica S. Shenoy3https://orcid.org/0000-0001-8086-1123Section of Infectious Diseases, Department of Medicine, Veterans’ Affairs (VA) Boston Healthcare System, Boston, Massachusetts VA Boston Center for Healthcare Organization and Implementation Research (CHOIR), Boston, Massachusetts Harvard Medical School, Boston, MassachusettsDivision of Infectious Diseases, Department of Medicine, University of Pittsburgh, Pittsburgh, PennsylvaniaDivision of Computer Science and Engineering, University of Michigan, Ann Arbor, MichiganHarvard Medical School, Boston, Massachusetts Infection Control Unit, Massachusetts General Hospital, Boston, Massachusetts Division of Infectious Diseases, Department of Medicine, Massachusetts General Hospital, Boston, MassachusettsCurrent methods of emergency-room–based syndromic surveillance were insufficient to detect early community spread of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) in the United States, which slowed the infection prevention and control response to the novel pathogen. Emerging technologies and automated infection surveillance have the potential to improve upon current practice standards and to revolutionize the practice of infection detection, prevention and control both inside and outside of healthcare settings. Genomics, natural language processing, and machine learning can be leveraged to improve identification of transmission events and aid and evaluate outbreak response. In the near future, automated infection detection strategies can be used to advance a true “Learning Healthcare System” that will support near–real-time quality improvement efforts and advance the scientific basis for the practice of infection control.https://www.cambridge.org/core/product/identifier/S2732494X22003333/type/journal_article |
| spellingShingle | Westyn Branch-Elliman Alexander J. Sundermann Jenna Wiens Erica S. Shenoy The future of automated infection detection: Innovation to transform practice (Part III/III) Antimicrobial Stewardship & Healthcare Epidemiology |
| title | The future of automated infection detection: Innovation to transform practice (Part III/III) |
| title_full | The future of automated infection detection: Innovation to transform practice (Part III/III) |
| title_fullStr | The future of automated infection detection: Innovation to transform practice (Part III/III) |
| title_full_unstemmed | The future of automated infection detection: Innovation to transform practice (Part III/III) |
| title_short | The future of automated infection detection: Innovation to transform practice (Part III/III) |
| title_sort | future of automated infection detection innovation to transform practice part iii iii |
| url | https://www.cambridge.org/core/product/identifier/S2732494X22003333/type/journal_article |
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