Infrastructure

Logistics services within hospitals impact 15-20% of operating costs. These services include moving patients, transporting linens, meals, medicines, equipment, and samples between clinics, wards, operating rooms, laboratories, and warehouses.

Digitization, automation, and robotic technologies can optimize these processes through solutions that enable the automatic transportation of patients and materials and the automatic management of the hospital warehouse.

The main difference with respect to factory logistics systems is the need to operate in anthropogenic environments.

A robotic systems will be devised capable of interacting with humans (patients, medical / nursing staff, visiting relatives) in an intuitive and safe way.

In the "hospital 4.0" model, the automation system for logistics represents a further integrated node within the ICT network for the management of services, whose architecture is typically distributed in turn and whose management and analysis methodologies are typical of the Data Analysis presented in the previous point.

By extending this integration to a "smart grid" for utility management, it is possible to increase the energy efficiency of the logistics system. However, this integration makes it possible to obtain a considerable containment of the power peaks required from the grid ("peak shaving"), with consequent containment of the expenditure for electricity consumption.

We want to evaluate the integration of typical methodologies of energy optimization with the management of logistics automation in order to ensure the safe execution of all critical operations by controlling and, if possible, optimizing energy consumption.

EHealth services and technologies generate enormous amounts of data and information which, for their processing, require the use of non-traditional methodologies and technologies: Cloud Computing, Internet of Things and Big Data and Analytics are the new paradigms founding the new generation of information management systems in eHealth.

The data sources to be considered, in addition to having a high volume, are also heterogeneous due to their different type and origin. Typically, such data are collected and stored in heterogeneous forms and are rarely reused in an aggregate manner.

The speed with which information is produced and saved, together with the aforementioned volume and variety, require systems and tools to collect, manage, and analyze data and all the information produced by healthcare systems, directing this research towards methods and techniques typical of Big Data Analytics (BDA).

The BDA in eHealth enables the transformation of a classic analysis of hypothesis-driven information to an innovative one of the data-driven type, capable of identifying non-trivial connections between heterogeneous data and information. This requires the need to investigate:

new cloud-based architectures that allow the timely processing of information, from the Hadoop to the Spark Ecosystem;
new information management systems that integrate relational (SQL), non-relational (NoSQL) and new relational (newSQL) architectures;
use of descriptive, diagnostic, predictive, and prescriptive analysis techniques;
use of Data Mining tools and techniques on Massive Data Sets, which include Deep Learning based systems.

In this context, digital infrastructures are equally important for the circulation of data and the interconnection of devices, following the paradigm of the Internet of Things.

The laboratory operates two different infrastructures, one based on a public Cloud and one based on a local datacenter.

In this section you will find the calendars of the current use of the infrastructures and a for to book them.

Details on the infrastructure and calendars for Cloud resources are available at the link below:

Cloud Resources

Details on the infrastructure and calendars for local resources are available at the link below:

Local Resources

The eHealth Big Data Analytics (eHBDA) laboratory was established as part of the ICT for Health project, funded by the Departments of Excellence program of the Ministry of Education, University and Research. The laboratory has acquired a public-private cloud infrastructure to support research, mainly along two lines of the ICT for Heath project: "Data for Health" and "Logistics for Health".

The eHealth Analytics laboratory is based on Big Data technologies for the design and prototyping of architectural and algorithmic solutions for the management and analysis of eHealth data. The 4.0 hospital is conceived as the place where processes aimed at "producing value" are implemented, integrating technology and processes for production and delivery of health, clinical or surgical services. Furthermore, it represents the place where different instruments and devices (from sensors to medical devices, from more or less complex devices linked to diagnostics equipment in the operating room, up to the ward and to the patient's bedside) are interconnected in an ecosystem in which they send parameters that can feed the wealth of clinical information of each patient in real time, informing people about their state of health, helping to raise the expectation of a healthy life from a physical and mental point of view.

Starting from this point of view, the architectural layout and design of an information management system infrastructure must necessarily take into account new paradigms and new models that allow managing complexity, volume, heterogeneity and data speed. This requires the need to investigate solutions based on the Internet of Things paradigm, for the interconnection and acquisition of data from different ecosystem devices, and the analysis of new processing paradigms, in particular cloud-based. The solution proposed in this laboratory is cloud-based. Given the amount of data, the infrastructure proposed is based on the Hadoop and Spark ecosystems, taking into consideration new information management systems that integrate relational (SQL), non-relational (NoSQL) architectures, and new relational (newSQL). On these architectures, specific environments are used for the development and integration of descriptive, diagnostic, predictive, and prescriptive analysis techniques as well as Data Mining tools and techniques on Massive Data Sets, which also include systems based on Deep Learning.

The laboratory operates two types of infrastructures:

Public cloud infrastructure, based on the services of Amazon Web Services (AWS) and managed by TIM SPA, which involves the acquisition of AWS Cloud resources for 3 years. The resources are divided among nodes for data collection, storage, processing, and for the use of results with Business Intelligence techniques.
Private cloud infrastructure being acquired and installed at the San Giovanni a Teduccio Campus. The resources are divided between nodes for data collection, storage, processing, and for the use of results with Business Intelligence techniques.

The details on the infrastructures are on the relevant pages: