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The augmented value...
The augmented value of using clinical notes in semi-automated surveillance of deep surgical site infections after colorectal surgery
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- Verberk, Janneke D. M. (author)
- University Medical Centre Utrecht, Utrecht, the Netherlands; National Institute for Public Health and the Environment, Bilthoven, the Netherlands
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- van der Werff, Suzanne D. (author)
- Karolinska Institutet
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- Weegar, Rebecka, 1982- (author)
- Stockholms universitet,Institutionen för data- och systemvetenskap
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- Henriksson, Aron, 1985- (author)
- Stockholms universitet,Institutionen för data- och systemvetenskap
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- Richir, Milan C. (author)
- University Medical Centre Utrecht, Utrecht, the Netherlands
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- Buchli, Christian (author)
- Karolinska Institutet, Stockholm, Sweden; Karolinska University Hospital, Stockholm, Sweden
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- van Mourik, Maaike S. M. (author)
- University Medical Centre Utrecht, Utrecht, the Netherlands
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- Naucler, Pontus (author)
- Karolinska Institutet, Stockholm, Sweden; Karolinska University Hospital, Stockholm, Sweden
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(creator_code:org_t)
- 2023
- 2023
- English.
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In: Antimicrobial Resistance and Infection Control. - 2047-2994. ; 12:1
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https://doi.org/10.1...
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https://urn.kb.se/re...
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Abstract
Subject headings
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- BackgroundIn patients who underwent colorectal surgery, an existing semi-automated surveillance algorithm based on structured data achieves high sensitivity in detecting deep surgical site infections (SSI), however, generates a significant number of false positives. The inclusion of unstructured, clinical narratives to the algorithm may decrease the number of patients requiring manual chart review. The aim of this study was to investigate the performance of this semi-automated surveillance algorithm augmented with a natural language processing (NLP) component to improve positive predictive value (PPV) and thus workload reduction (WR).MethodsRetrospective, observational cohort study in patients who underwent colorectal surgery from January 1, 2015, through September 30, 2020. NLP was used to detect keyword counts in clinical notes. Several NLP-algorithms were developed with different count input types and classifiers, and added as component to the original semi-automated algorithm. Traditional manual surveillance was compared with the NLP-augmented surveillance algorithms and sensitivity, specificity, PPV and WR were calculated.ResultsFrom the NLP-augmented models, the decision tree models with discretized counts or binary counts had the best performance (sensitivity 95.1% (95%CI 83.5-99.4%), WR 60.9%) and improved PPV and WR by only 2.6% and 3.6%, respectively, compared to the original algorithm.ConclusionsThe addition of an NLP component to the existing algorithm had modest effect on WR (decrease of 1.4-12.5%), at the cost of sensitivity. For future implementation it will be a trade-off between optimal case-finding techniques versus practical considerations such as acceptability and availability of resources.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Hälsovetenskap (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Health Sciences (hsv//eng)
- NATURVETENSKAP -- Biologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinska och farmaceutiska grundvetenskaper (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Basic Medicine (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Infektionsmedicin (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Infectious Medicine (hsv//eng)
Keyword
- Automated surveillance
- Algorithm
- Colorectal surgery
- Healthcare-associated infections
- Natural language processing
- Surgical site infections
- data- och systemvetenskap
- Computer and Systems Sciences
Publication and Content Type
- ref (subject category)
- art (subject category)
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