Massimo Bursi (DEDAGROUP LIFE SCIENCE MANAGER) &

Presentazione sul tema: "Massimo Bursi (DEDAGROUP LIFE SCIENCE MANAGER) &"— Transcript della presentazione:

1 Come standardizzare gli studi clinici con SAS Clinical Data Integration
Massimo Bursi (DEDAGROUP LIFE SCIENCE MANAGER) & Alberto Romanelli (SAS DOMAIN EXPERT COMMERCIAL)

2 Chi siamo Mission Numeri 225 M€ 10 1.600+ 40 Dedagroup at a glance
FATTURATO Siamo fra i primi Gruppi IT a capitale italiano. Supportiamo Aziende, Enti Pubblici e Istituti Finanziari nelle loro strategie IT con competenze applicative, tecnologiche e di system integration. L’headquarter si trova a Trento – terra d’eccellenza per esperienze d’innovazione – ma il nostro Gruppo, con filiali in Italia e all’estero, supporta oltre clienti in tutto il mondo. Accompagniamo aziende ed enti nella trasformazione digitale, aiutandoli a progettare il cambiamento. Con i nostri clienti e per i nostri clienti individuiamo strategie, architetture, soluzioni e competenze perché possano crescere e rimanere innovativi nel tempo. Partendo dall’Italia, terra d’eccellenza, portiamo le nostre soluzioni in tutto il mondo. 10 ANNI CONSECUTIVI DI CRESCITA 1.600+ PERSONE 40 PAESI IN CUI ABBIAMO CLIENTI

3 Dedagroup Life-sciences
Il nostro saper fare Life Sciences Processi e Organizzazioni Architettura Governance IT Qualification Documentazione a supporto fasi di Convalida System integration Application Maintenance Servizi, Infrastruttura, IT Outsourcing

4 Perché CDISC?

5 Perche’ tanta attenzione?

6 Perche’ tanta attenzione?
CDISC Business Case 2006… le promesse! Communication & transfer of data among project teams and partners is easier A greater level of accuracy and less training with a constant process Decision making is simplified Scientists can do the science rather than being concerned with the data Opens up a wider choice of tools/technology (as long as they are standards compliant)

7 Perche’ tanta attenzione?
CDISC standards from the beginning of a research study Researchers can save 70-90% of time and resources during the Study Start Up stage (time to first patient enrolled), and ~ 75% of non- patient participation time during Study Conduct and Analysis. Regulatory eSubmissions, using updated baseline numbers for the time and cost of getting a drug to market, it can be saved per submission: 18% of the total cost.

8 Perche’ tanta attenzione?
An average of two years can be saved off of an average 12-year clinical development program lifecycle – just by standardizing data. Regulators have stated they will begin requiring CDISC standards for eSubmissions in the U.S. (FDA) and in Japan (PMDA) because standards enable them to use sophisticated review tools and conduct higher quality reviews Regulators do not plan to require CDISC standards for data collection; however, they are encouraging the use of standards from the start because this helps in maintaining traceability and improves data quality at the end of the study (therefore in the eSubmission).

9 Perche’ tanta attenzione?
It is now possible to use sophisticated tools to analyze and aggregate data across studies for specific disease areas, and potentially enable the creation of databases that can serve as a repository for cross-consortia data. This type of shared data leads to opportunities for disease modeling and biomarker qualification that was previously impossible with non-standardized data. In the case of safety reporting, it was found that the time to complete an Adverse Drug Experience report was reduced from 35 minutes to less than 1 minute, on average.

10 Perche’ tanta attenzione?
CDISC adoption encourages re-use across disease areas that require common information. CDISC allows the ability to capture patterns of data, match controlled terminologies, and reuse research concepts across different therapeutic areas.

11 CDISC COME OPPortunità
CDISC non è solamente uno Standard Regolatorio… CDISC è oramai uno Standard Industriale! (impariamo da altri mercati!) CDISC non è un obbligo… CDISC è un’opportunità : per introdurre processi efficienti ed efficaci lungo tutta la filiera dei dati (preclinici e clinici) per utilizzare i dati al meglio i dati presenti al fine di evitare di effettuare nuovi studi (preclinica e clinica) CDISC è la metodologia che va unita ad un approccio tecnologico end-to-end

12 LA REALTA’ CDISC E’ ORA! Vision: Medical Innovation

13 Un approccio end-to-end

14 analytics platform for life science
Un approccio end-to-end ai Life science data SAS Analytics Platform for Life Science Analisti SAS

15 Analytics platform for life science
SAS Classical Programming Clinical Data Integration Self-service Reporting Self-Service Exploration Integrazione con Office Clinical Data IT Management Labs Data Pharmaco Vigilance Data SAS Analytics Platform for Life Science External Data Others Data… Asset Performance Analytics is feeds on data. It has a defined data model – that manages the complexity of sensor data, especially when combining it with other data like events, or product data. We have developed business rules and processes that understand the complexity of the data – for example, providing ways for users to make business decisions around transposing data, or how the solution will help visualize 100,000 data points on a screen. A range of workspaces that allow a number of personas to interact with the data to build models, run RCA, or manage data for desktop applications.

16 Analytics platform for life science
SAS Classical Programming Clinical Data Integration Self-service Reporting Self-Service Exploration Integrazione con Office Interfaccia Web Based Consente di programmare come sul proprio PC Pieno riutilizzo del codice già sviluppato Consente di utilizzare la potenza elaborativa del Server Garantisce maggiori opzioni (editor interattivo, versioning) SAS Studio Clinical Data IT Management Labs Data SAS Analytics Platform for Life Science External Data Others Data… Asset Performance Analytics is feeds on data. It has a defined data model – that manages the complexity of sensor data, especially when combining it with other data like events, or product data. We have developed business rules and processes that understand the complexity of the data – for example, providing ways for users to make business decisions around transposing data, or how the solution will help visualize 100,000 data points on a screen. A range of workspaces that allow a number of personas to interact with the data to build models, run RCA, or manage data for desktop applications.

17 Analytics platform for life science
SAS Classical Programming Clinical Data Integration Self-service Reporting Self-Service Exploration Integrazione con Office Automatizza task ripetitivi Gestisce e riutilizza informazioni immagazzinate in un unico repository Standardizza e legge i dati in/da CDISC usando modelli e processi predefiniti. Realizza e documenta i lavori grazie a un’interfaccia user-friendly Abilita la realizzazione di analisi Cross Study …. SAS Clinical DI Clinical Data IT Management Labs Data SAS Analytics Platform for Life Science External Data Others Data… Asset Performance Analytics is feeds on data. It has a defined data model – that manages the complexity of sensor data, especially when combining it with other data like events, or product data. We have developed business rules and processes that understand the complexity of the data – for example, providing ways for users to make business decisions around transposing data, or how the solution will help visualize 100,000 data points on a screen. A range of workspaces that allow a number of personas to interact with the data to build models, run RCA, or manage data for desktop applications.

18 Analytics platform for life science
SAS Classical Programming Clinical Data Integration Self-service Reporting Self-Service Exploration Integrazione con Office Interfaccia per disegnare reports e dashboards Self-service data import Molte tipologie di visualizzazioni con la possibilità di creare grafici custom Stampa di report, sezioni, oggetti, condivisione di report via , schedulazione dei reports per la distribuzione, setup e ricevimento degli alerts via o sms SAS Visual Analytics Clinical Data IT Management Labs Data SAS Analytics Platform for Life Science External Data Others Data… Asset Performance Analytics is feeds on data. It has a defined data model – that manages the complexity of sensor data, especially when combining it with other data like events, or product data. We have developed business rules and processes that understand the complexity of the data – for example, providing ways for users to make business decisions around transposing data, or how the solution will help visualize 100,000 data points on a screen. A range of workspaces that allow a number of personas to interact with the data to build models, run RCA, or manage data for desktop applications.

19 Analytics platform for life science
SAS Classical Programming Clinical Data Integration Self-service Reporting Self-Service Exploration Integrazione con Office Esplorazione delle informazioni per trovare relazioni, trends, non conformità, ecc. Auto-charting, filtri visuali, ecc. Vari tipi di visualizzazione: Crosstabs, Bar Charts, Line Charts, Scatter Plots, Bubble Plots, Geo Maps, Box Plots, Heat Maps, Network Diagrams, Word Clouds, Sankey Diagrams, e altri Analytics Enabler: Correlazioni, Regressioni, Forecasting, Analisi di Scenario, ecc SAS Visual Analytics Clinical Data IT Management Labs Data SAS Analytics Platform for Life Science External Data Others Data… Asset Performance Analytics is feeds on data. It has a defined data model – that manages the complexity of sensor data, especially when combining it with other data like events, or product data. We have developed business rules and processes that understand the complexity of the data – for example, providing ways for users to make business decisions around transposing data, or how the solution will help visualize 100,000 data points on a screen. A range of workspaces that allow a number of personas to interact with the data to build models, run RCA, or manage data for desktop applications.

20 Analytics platform for life science
SAS Classical Programming Clinical Data Integration Self-service Reporting Self-Service Exploration Integrazione con Office Utilizzo delle funzionalità SAS direttamente da Word, Excel, PowerPoint e Outlook Visualizzazione e refresh dei Report create in VA direttamente dalle applicazioni Office Visualizzazione delle informazioni offline. Utilizzo di Microsoft Excel più come un’applicazione che come un data store Utilizzo di quanto si conosce SAS Add In for Microsoft Office Clinical Data IT Management Labs Data SAS Analytics Platform for Life Science External Data Others Data… Asset Performance Analytics is feeds on data. It has a defined data model – that manages the complexity of sensor data, especially when combining it with other data like events, or product data. We have developed business rules and processes that understand the complexity of the data – for example, providing ways for users to make business decisions around transposing data, or how the solution will help visualize 100,000 data points on a screen. A range of workspaces that allow a number of personas to interact with the data to build models, run RCA, or manage data for desktop applications.

21 Analytics platform for life science
SAS Classical Programming Clinical Data Integration Self-service Reporting Self-Service Exploration Integrazione con Office Thin Client Architecture Governance Security Performance Flexibility Clinical Data IT Management Labs Data SAS Analytics Platform for Life Science External Data Others Data… Asset Performance Analytics is feeds on data. It has a defined data model – that manages the complexity of sensor data, especially when combining it with other data like events, or product data. We have developed business rules and processes that understand the complexity of the data – for example, providing ways for users to make business decisions around transposing data, or how the solution will help visualize 100,000 data points on a screen. A range of workspaces that allow a number of personas to interact with the data to build models, run RCA, or manage data for desktop applications.

22 GRAZIE! Subtitle