Abstract
The problem of transforming Real World Data into Real World Evidence is becoming increasingly important in the frameworks of Digital Health and Personalized Medicine, especially with the availability of modern algorithms of Artificial Intelligence high computing power, and large storage facilities.Even where Real World Data are well maintained in a hospital data warehouse and are made available for research purposes, many aspects need to be addressed to build an effective architecture enabling researchers to extract knowledge from data.We describe the first year of activity at Gemelli Generator RWD, the challenges we faced and the solutions we put in place to build a Real World Data laboratory at the service of patients and health researchers. Three classes of services are available today: retrospective analysis of existing patient data for descriptive and clustering purposes; automation of knowledge extraction, ranging from text mining, patient selection for trials, to generation of new research hypotheses; and finally the creation of Decision Support Systems, with the integration of data from the hospital data warehouse, apps, and Internet of Things.
Highlights
REAL WORLD EVIDENCE AND DIGITAL HEALTHThe huge availability of data from different generic and special purpose information technology (IT) systems in today’s healthcare process is profoundly impacting knowledge management for medical specialists, by providing new insight and understanding in all diagnostic and prognostic domains
The problem of transforming Real World Data into Real World Evidence is becoming increasingly important in the frameworks of Digital Health and Personalized Medicine, especially with the availability of modern algorithms of Artificial Intelligence high computing power, and large storage facilities.Even where Real World Data are well maintained in a hospital data warehouse and are made available for research purposes, many aspects need to be addressed to build an effective architecture enabling researchers to extract knowledge from data.We describe the first year of activity at Gemelli Generator RWD, the challenges we faced and the solutions we put in place to build a Real World Data laboratory at the service of patients and health researchers
In designing and developing the Gemelli Generator Infrastructure, a few challenges had to be faced, and some founding principles were chosen since the beginning: the Big Data approach for data management; the Rapid Learning paradigm to extract value from data in a multidisciplinary, evolving, personalized medicine-oriented framework; a flexible standardization of clinical data via the use of formal and nonformal ontologies; a high level of automation in the data transfer; a “privacy by design” approach, as dictated by the GDPR, in view of the protection of data ownership and intellectual property
Summary
The huge availability of data from different generic and special purpose information technology (IT) systems in today’s healthcare process is profoundly impacting knowledge management for medical specialists, by providing new insight and understanding in all diagnostic and prognostic domains. This will progressively help reshaping the care process to design personalized therapies and improve quality of care. Under the name of GENERATOR REAL WORLD DATA Facility, this initiative is supporting many research groups, and provides data analysis and modelling expertise, structured methods for data governance and processing, project design and implementation, management of ethical and privacy aspects. We share our perspective on future project steps and areas to be addressed and strengthened, given that the project is evolving day by day
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