ABSTRACT
OBJECTIVES: This study aims to enhance the analysis of healthcare processes by introducing Object-Centric Process Mining (OCPM). By offering a holistic perspective that accounts for the interactions among various objects, OCPM transcends the constraints of conventional patient-centric process mining approaches, ensuring a more detailed and inclusive understanding of healthcare dynamics. METHODS: We develop a novel method to transform the Observational Medical Outcomes Partnership Common Data Models (OMOP CDM) into Object-Centric Event Logs (OCELs). First, an OMOP CDM4PM is created from the standard OMOP CDM, focusing on data relevant to generating OCEL and addressing healthcare data's heterogeneity and standardization challenges. Second, this subset is transformed into OCEL based on specified healthcare criteria, including identifying various object types, clinical activities, and their relationships. The methodology is tested on the MIMIC-IV database to evaluate its effectiveness and utility. RESULTS: Our proposed method effectively produces OCELs when applied to the MIMIC-IV dataset, allowing for the implementation of OCPM in the healthcare industry. We rigorously evaluate the comprehensiveness and level of abstraction to validate our approach's effectiveness. Additionally, we create diverse object-centric process models intricately designed to navigate the complexities inherent in healthcare processes. CONCLUSION: Our approach introduces a novel perspective by integrating multiple viewpoints simultaneously. To the best of our knowledge, this is the inaugural application of OCPM within the healthcare sector, marking a significant advancement in the field.
Subject(s)
Data Mining , Data Mining/methods , Humans , Delivery of Health Care , Process Assessment, Health Care/methods , Databases, Factual , Medical Informatics/methods , Electronic Health RecordsABSTRACT
One Health is an integrated approach that aims to balance and optimize the interconnectedness of the health of humans, animals, and ecosystems. Using this transdisciplinary approach, experts from across Canada led the formation of the Wildlife Emerging Pathogens Initiative (Wild EPI) to undertake research and surveillance programs evaluating the potential risks of emerging pathogens at the human-animal interface. Wild EPI is dedicated to implementing the One Health approach to enhance our understanding of the epidemiology and burden of zoonotic infections among humans and other animal hosts.
ABSTRACT
OBJECTIVES: SARS-CoV-2 shedding has changed as new variants have emerged. It is important to understand the trajectory of PCR positivity due to Omicron in vaccinated populations. METHODS: Double- or triple-vaccinated adult household contacts of individuals with COVID-19 self-collected oral-nasal swabs for 14 days. A hierarchical linear model estimated viral load trajectories and an exploratory logistic regression model assessed for factors associated with viral detection before symptom onset. RESULTS: Forty-one participants developed COVID-19 with 37 (90%) symptomatic. Viral load peaked 3 days after symptom onset at a median concentration of 8.83 log10 copies/milliliter (range 5.95-10.32) and the mean difference between participants with two or three COVID-19 vaccine doses was 0.02 log10 copies/milliliter (95% CI -0.13 to 0.16). PCR positivity began with a range of 4 days prior to 3 days after symptom onset and was positive on the day of symptom onset in 76% (28/37). SARS-CoV-2 detection on the day of symptom onset was less likely among those with 2 vaccine doses (OR 0.13, 95%CI 0.02-0.79). 68% (25/37) of infected participants had detectable SARS-CoV-2 with Ct<30 at 7 days after symptom onset. CONCLUSIONS: Peak viral load and duration of PCR positivity were similar in participants with COVID-19 after two versus three COVID-19 vaccine doses. Onset of viral detection relative to symptom onset was variable.