Journal article
ACESO: An Optimized System for Collecting Granular Data on Patient Care Sequences and Infection Prevention Practices
Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Vol.68(1), pp.460-464
09/2024
DOI: 10.1177/10711813241275913
Abstract
Recent outbreaks of viral respiratory or resistant bacterial pathogens underscore the critical importance of infection prevention and control (IPC) measures in safeguarding healthcare professionals (HCPs), patients, and the environment against transmission of infectious agents. To identify the root causes of suboptimal IPC practices, such as low hand-hygiene (HH) compliance and improper personal protective equipment (PPE) use, we created the Applied Care Event Sequence Observation (ACESO) system. We used human factors engineering concepts to evaluate various observational tools developed for prior studies and create ACESO’s highly structured data-collection form and related electronic data-entry and compilation templates. The ACESO system streamlined the collection, entry, and initial analysis of granular data about patient-care sequences. Our initial findings revealed concerning trends, particularly regarding suboptimal HH compliance observed during HCP’s patient-care processes. Additionally, our observations revealed instances of HCPs touching various surfaces with potentially contaminated gloves, heightening the risk of cross-contamination within healthcare units.
Details
- Title: Subtitle
- ACESO: An Optimized System for Collecting Granular Data on Patient Care Sequences and Infection Prevention Practices
- Creators
- Jaqueline Pereira Da Silva - University of IowaLoreen A. Herwaldt - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Vol.68(1), pp.460-464
- DOI
- 10.1177/10711813241275913
- ISSN
- 1071-1813
- eISSN
- 2169-5067
- Publisher
- Sage
- Language
- English
- Electronic publication date
- 08/29/2024
- Date published
- 09/2024
- Academic Unit
- Infectious Diseases; Epidemiology; Internal Medicine
- Record Identifier
- 9984701658902771
Metrics
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