Electronic health record enabled track and trace inan urban hospital network: implications for infectionprevention and control
Pi, L.; Expert, P.; Clarke, J.; Jauneikaite, E.; Costelloe, C.
Show abstract
Healthcare-associated infections represent one of the most significant challenges for modern medicine as they can significantly impact patientslives. Carbapenemase-producing Enterobacteriaceae (CPE) pose the greatest clinical threat, given the high levels of resistance to carbapenems, which are considered as agents of last resort against life-threatening infections. Understanding patterns of CPE infection spreading in hospitals is paramount to design effective infection control protocols to mitigate the presence of CPE in hospitals. We used patient electronic health records from three urban hospitals to: i) track microbiologically confirmed carbapenemase producing Escherichia coli (CP-Ec) carriers and ii) trace the patients they shared place and time with until their identification. We show that yearly contact networks in each hospital consistently exhibit a core-periphery structure, highlighting the presence of a core set of wards where most carrier-contact interactions occured before being distributed to peripheral wards. We also identified functional communities of wards from the general patient movement network. The contact networks projected onto the general patient movement community structure showed a comprehensive coverage of the hospital. Our findings highlight that infections such as CP-Ec infections can reach virtually all parts of hospitals through first-level contacts.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Using explainable machine learning to identify patients at risk of reattendance at discharge from emergency departments 94%
- Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study 93%
- Developing Machine Learning Models for Predicting Intensive Care Unit Resource Use During the COVID-19 Pandemic 93%
Similar papers in this journal
- A Deep Recurrent Reinforced Learning model to compare the efficacy of targeted local vs. national measures on the spread of COVID-19 in the UK 92%
- COVID-19 outbreak rates and infection attack rates associated with the workplace: a descriptive epidemiological study 91%
- Social disparities in the first wave of COVID-19 infections in Germany: A county-scale explainable machine learning approach 90%
Similar papers in this journal
Similar papers in this journal
- A targeted e-learning approach to reduce student mixing during a pandemic 94%
- Preventing COVID-19 spread in closed facilities by regular testing of employees – an efficient intervention in long-term care facilities and prisons? 93%
- Towards a COVID-19 symptom triad: The importance of symptom constellations in the SARS-CoV-2 pandemic 93%
Similar papers in this journal
- Optimizing the Implementation of Clinical Predictive Models to Minimize National Costs: A Sepsis Case Study 92%
- COHD-COVID: Columbia Open Health Data for COVID-19 Research 91%
- Current infection control behaviour patterns in the UK, and how they can be improved by 'Germ Defence', an online behavioural intervention to reduce the spread of COVID-19 in the home. 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.