ArticleInfection control and hospital epidemiology2019
Automated data mining of the electronic health record for investigation of healthcare-associated outbreaks.
Article in Infection control and hospital epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 49 citations in OpenAlex.
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- Empirically derived sequence similarity thresholds to study the genomic epidemiology of plasmids shared among healthcare-associated bacterial pathogens.EBioMedicine · 2023Observational
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- A new method for near real-time, nationwide surveillance of nosocomial COVID-19 in Norway: providing data at all levels of the healthcare system, March 2020 to March 2022.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2023Article
- The future of automated infection detection: Innovation to transform practice (Part III/III).Antimicrobial stewardship & healthcare epidemiology : ASHE · 2023Review
- Whole-Genome Sequencing Surveillance and Machine Learning of the Electronic Health Record for Enhanced Healthcare Outbreak Detection.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2022Article
- Genomic Diversity of Hospital-Acquired Infections Revealed through Prospective Whole-Genome Sequencing-Based Surveillance.mSystems · 2022Article
- Transmission visualizations of healthcare infection clusters: A scoping review.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2022Article
- Automated data extraction of electronic medical records: Validity of data mining to construct research databases for eligibility in gastroenterological clinical trials.Upsala journal of medical sciences · 2022Article
- Article
- Whole-genome sequencing surveillance and machine learning for healthcare outbreak detection and investigation: A systematic review and summary.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2022Review
- Outbreak of Pseudomonas aeruginosa Infections from a Contaminated Gastroscope Detected by Whole Genome Sequencing Surveillance.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2021Article
- Method for Economic Evaluation of Bacterial Whole Genome Sequencing Surveillance Compared to Standard of Care in Detecting Hospital Outbreaks.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
13 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundIdentifying routes of transmission among hospitalized patients during a healthcare-associated outbreak can be tedious, particularly among patients with complex hospital stays and multiple exposures. Data mining of the electronic health record (EHR) has the potential to rapidly identify common exposures among patients suspected of being part of an outbreak.
methodsWe retrospectively analyzed 9 hospital outbreaks that occurred during 2011-2016 and that had previously been characterized both according to transmission route and by molecular characterization of the bacterial isolates. We determined (1) the ability of data mining of the EHR to identify the correct route of transmission, (2) how early the correct route was identified during the timeline of the outbreak, and (3) how many cases in the outbreaks could have been prevented had the system been running in real time.
resultsCorrect routes were identified for all outbreaks at the second patient, except for one outbreak involving >1 transmission route that was detected at the eighth patient. Up to 40 or 34 infections (78% or 66% of possible preventable infections, respectively) could have been prevented if data mining had been implemented in real time, assuming the initiation of an effective intervention within 7 or 14 days of identification of the transmission route, respectively.
conclusionsData mining of the EHR was accurate for identifying routes of transmission among patients who were part of the outbreak. Prospective validation of this approach using routine whole-genome sequencing and data mining of the EHR for both outbreak detection and route attribution is ongoing.
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