4CE is an international consortium for electronic health record (EHR) data-driven studies of the COVID-19 pandemic. The goal of this effort—led by the i2b2 international academics users group—is to inform doctors, epidemiologists and the public about COVID-19 patients with data acquired through the health care process.

In November 16, 2021 will be held by Harvard Medical School the 4CE Fall Symposium – World scale epidemiology in a rapid evolving health crisis: 4CE model using electronic healthcare data

SAVE THE DATE and stay tuned


Harvard, June 22-23, 2021 

The i2b2 tranSMART Foundation, in collaboration with the Department of BioMedical Informatics (DBMI) at Harvard University Medical School, held its second VIRTUAL Symposium entitled “Supporting sequelae of COVID19”. You can view the event program and presentations at the official Foundation link. You can also relive some highlights on Twitter #i2b2tsjune2021


Status: Closed

Coordinator: IRCCS Fondazione Salvatore Maugeri, Pavia (IT)

Involved centres: Atene, Valencia

Clinical field: Diabete

Start date: 1 gennaio 2013

Fund: EU

Platform: i2b2

MOSAIC is a project funded by the European Commission aimed at providing an innovative approach in the diagnosis and follow-up of people with chronic diabetes, in order to improve the patient characterisation and the ability to assess the risk of developing complications related to Type 2 Diabetes Mellitus.
The University of Pavia, in collaboration with BIOMERIS, has used the i2b2 framework to collect and integrate heterogeneous data from the database of diabetic patients of Fondazione Salvatore Maugeri, administrative data from the local health authority and environmental data from regional databases.
When a user of the system selects the group of patients of interest, the query engine retrieves from i2b2 the necessary information, then the Data-Mining module performs the temporal analysis and returns the control to the interface for the visualisation of the results. MOSAIC is already available for use in clinical practice in order to obtain statistics on diabetes care clinics and calculate patient-friendly risk indices. MOSAIC’s data set includes geo-referenced clinical data that allows each subject to be geographically located.


  • Dagliati A, Sacchi L, Bucalo M, Segagni D, Zarkogianni K, Martinez Millana A, Cancela J, Sambo F, Fico G, Meneu Barreira M, T, Cerra C, Nikita K, Cobelli C, Chiovato L, Arredondo M, T, Bellazzi R, (2014) A data gathering framework to collect Type 2 diabetes patients data. In EEE-EMBS International Conference on Biomedical and Health Informatics (BHI), pp. 244-247. (link, pdf)
  • Bellazzi R, Dagliati A, Sacchi L, Segagni D. Big Data Technologies: New Opportunities for Diabetes Management. J Diabetes Sci Technol. 2015 Apr 24. pii: 1932296815583505. (link, pdf)
  • Segagni D, Sacchi L, Dagliati A, Tibollo V, Leporati P, De Cata P, Chiovato L, Bellazzi R. Improving Clinical Decisions on T2DM Patients Integrating Clinical, Administrative and Environmental Data. Stud Health Technol Inform. 2015;216:682-6. (link, pdf)
  • Martinez-Millana A, Fernandez-Llatas C, Sacchi L, Segagni D, Guillen S, Bellazzi R, Traver V.From data to the decision: A software architecture to integrate predictive modelling in clinical settings.Conf Proc IEEE Eng Med Biol Soc. 2015 Aug;2015:8161-4. (link, pdf)


Project status: Current

Coordinator: ASST Papa Giovanni XXIII, Bergamo, IT

Field: Clinical

Start date: september 1st, 2016

Platform: i2b2


Since 2016, ASST Papa Giovanni XXIII has activated a scientific collaboration agreement signed with FROM (Fondazione per la Ricerca Ospedale Maggiore di Bergamo), University of Pavia and BIOMERIS aimed at the implementation and maintenance of an i2b2 system supporting the institute’s clinical research activities.

The i2b2 system implemented allows researchers to obtain summary information in response to their specific epidemiological and scientific questions; with this tool is enhanced the opportunity to cross information relating to patients of the institute and from multiple sources that can be both hospital information flows, and flows from external databases possibly made available by third parties involved in sharing research objectives.

The implemented i2b2 “horizontal” project contains pseudonymized clinical data coming from different databases:

  • hospitalisation flow of the discharged patients
  • pharmacological therapies
  • outpatients procedures
  • outcomes of the chemistry laboratory data
  • anatomic pathology reports.

The integrated data cover a period of about 15 years and consist of about 1 million patients, 15 million events (visits/rehabs) and 200 million observations.

The system is configured as a valid tool for the aggregate analysis of data in order to evaluate the overall trend of specific clinical pictures of pathology and to support the development of research projects; the tool also allows to obtain quantitative indications on the number of selected cases, evaluating the adequacy to answer the questions posed or the potential size for participation in multi-center studies. In addition, the system appears to respond to the need to obtain relevant epidemiological information from the various company software against a great work of participation to feed the contents.


Project status: Terminated

Coordinator: IRCCS ICS Maugeri, Pavia (IT)

Field: Cardiology

Start date: 01 January 2011

Platform: i2b2



The CARDIO-i2b2 project aims to customize the i2b2 bioinformatics platform to integrate clinical and research data in order to support translational research in cardiology at FSM (Fondazione Salvatore Maugeri). CARDIO-i2b2 collects data from the Molecular Cardiology Laboratory databases and combines them with clinical data from the TRIAD system, an information system to collect data related to arrhythmogenic diseases. Genetic information related to affected patients is also collected.

The data contained in the TRIAD relational database were exported to the i2b2 data warehouse. A dedicated extension of i2b2 was developed to include static R software within the architecture and exploit the statistical capabilities of R via the i2b2 web interface. A dedicated plugin was developed to allow researchers to dynamically perform Kaplan-Meier survival analysis on selected patients.


Segagni D, Tibollo V, Dagliati A, Napolitano C, G Priori S, Bellazzi R. (2012) CARDIO-i2b2: integrating arrhythmogenic disease data in i2b2. Stud Health Technol Inform.180:1126-8. (link, pdf)

Segagni D, Tibollo V, Dagliati A, Malovini A, Zambelli A, Napolitano C, Priori SG, Bellazzi R. Clinical and research data integration: the i2b2-FSM experience. AMIA Jt Summits Transl Sci Proc. 2013 Mar 18;2013:239-40. eCollection 2013. (link)