PADDI: A Business Intelligence and Data Quality platform  for Piedmont health Giuliana Bonello Business Intelligence & Dat...
Founded in 1977   by: <ul><li>91   Associated Consortium Members   </li></ul><ul><li>176   Millions €  annual revenues   <...
<ul><li>technologies and architectures  </li></ul><ul><li>document management and related laws </li></ul><ul><li>policy ma...
International activities <ul><li>Since  2005   CSI is actively involved in promoting its international presence both throu...
<ul><li>A  SAS customer  in Italy for more than 30 years </li></ul><ul><li>SAS  top  customer at European scale for variet...
How Many Databases Today in Piedmont PA? Data update September 2010 Data base growing
Data      Knowledge    Decision Knowledge Operational Data <ul><li>Territory and Environment (324) </li></ul><ul><ul><li...
BICC Organizational History 1980 1990 2000 1st DW project (Piedmont region) 2005 2010 From BICC to PSI-CC: BI & DQ Statist...
BICC Business model INFRASTRUCTURE SOFTWARE DATA B I C C PA Institution 3 PA Institution 2 PA Institution 1 PA Institution...
BI&DQ applications BI applications growing
Decisional Service Pyramid
Health numbers in Piedmont Administrative levels Health Ministry  Regional Government Epidemiology research 13 Local Healt...
The decisional pyramid for health: PADDI
DW processes  DQ <ul><li>Information needs </li></ul><ul><li>Monitoring & Evaluation </li></ul><ul><li>Analysis of single ...
<ul><li>The SSN set up in 1978 guaranteees health care  for all  citizens. </li></ul><ul><li>It is mainly financed through...
Hospital activities Health information heritage of the Piemont Region Health personnel Assistiti Territorial  activities P...
OLAP Multidimensional analysis From information to knowledge Users Employees Middle managers Analyst Managers Type of Proc...
Growth curve of knowledge How to optimize  the results? What will happen? Why it happened? What it happened?
Reporting Monitoring Analysis Strategy Formulation Resource  Allocation Data Quality Action plan Data Quality improvement ...
OUR GOALS Epoetine Linee  Guida Ipertens Diabetici Fatt  Coag Aura Biologici  e AR Some example
Data Anonymize engine 4.457.335  Residents 31/12/2010 DQ Data Quality
Planning / Management Control Doctors – District Budget Patients Structures Drugs Hospitalization Emergencies Salaries Hea...
Goals: Implementare l`uso di epoetine biosimilari nei soggetti nefropatici naive per la dialisi. DB : Medical visit Drug i...
<ul><li>DB coinvolti: </li></ul><ul><li>Pathology`register </li></ul><ul><li>Medical visit </li></ul><ul><li>Hospitalizati...
<ul><li>DB coinvolti: </li></ul><ul><li>Rares Diseases Register </li></ul><ul><li>Personal data of assisted </li></ul><ul>...
Biological medicines are particularly expensive; their profiles of safety ad efficacy are not full Known. Ensuring effecti...
Data Mining
Guide lines for the treatment of Hypertension The iue of drugs` association only on risk` subject (comorbidity) Drugs admi...
Pazienti a  rischio Data Mining
<ul><li>Thank you for your attention! </li></ul><ul><li>Giuliana Bonello </li></ul><ul><ul><ul><li>[email_address] </li></...
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PADDI - A business intelligence and data quality platform for Piedmont health

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This presentation highlights CSI experience on the PADDI Program. The project is the integration of all data belonging to health management systems into a Enterprise Data Warehouse. This integration is the result of the implementation of data cleansing services and decisional systems and it enables regional health authorities to appropriately supervise health policies within their territories

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  • Good morning. My name’s Giuliana Bonello and I’m Product Manager of Business Intelligence &amp; Data Quality Solutions in CSI-Piemonte. I’m very pleased to be here today. I share the speech with my colleague V.Berti, who is in charge of the Health division. She is a pharmaceutical and health care expert. The aim of our presentation is to give you an overview of PADDI, the business intelligence and data quality platform realized in Piedmont to support health systems.
  • CSI-Piemonte is a Public Consortium with a public right legal entity status, that operates in the Piedmont Region, located in Northern Italy. We are a leading ICT company, providing with services all segments of the local public sector – from health care to commerce &amp; industry, from cultural heritage to administrative systems, from agriculture to territorial systems. Established in 1977 at the initiative of the Region of Piedmont, the University of Turin and the Polytechnic of Turin, we had a huge and steady growth along these decades. We are presently a consolidated company, with 91 partners that over the years have joined the consortium, including, besides the founding partners, the City of Turin, all the provinces of Piedmont, local councils and associations, health care offices, hospitals and government agencies
  • The role of the Consortium is being a key instrument for Piedmont public sector reform, through the interaction and the interchange of data between the public information systems on the Public Administration Network. Our mission is the setting up of the “Piedmont System” for the implementation of administrative decentralization using ICTs, based on a cloud idea. we act following a systemic approach, in order to promote: the cooperation among different local government; the knowledge sharing ; the internal process innovation and optimization ; the setting up of broadband infrastructure of the territory; the shared supply of on line services ; the promotion of and the cooperation with the business sector. Thanks to our expertise, public administrations can achieve economies of scale and rely on the top-class skills of professional consultants .
  • The value added by the consortium is also exported beyond regional borders through joint projects promoted in Italy and abroad, promoting the reuse and transfer of best practices to other regions and local administrations. At international level we have managed several research and development projects and also cooperation projects.
  • Our company has been working with SAS for 30 years. We received the Enterprse Intelligence Award for the Public Sector at the SAS Forum 2007. CSI-Piemonte was selected for its wide range of BI applications supporting the Piedmont local institutions, its innovative use of SAS technologies and solutions and the setting up of the Business Intelligence Competency Centre .
  • The catalogue of data and services provided for the members of the consortium makes it possible to draw a costantly upgraded picture of the existing data bases. We supports and maintains more than 1,400 databases for our members and 180 of those are datawarehouses
  • With so many databases, you can understand the importance of Business Intelligence (BI) solutions to support the business decision making processes of our customers. The Consortium manages the Piedmont Public Sector Data Governance Program, through the normalization of existing data banks, the promotion of the data sharing and the design and development of new integrated data banks, like population and enteprises registries, making them available and usable overall in the network, the reuse of BI solutions.
  • So we set up a Business Intelligence Competency Centre . This figure show the history of our BICC, from the eighties till now. The roots of the Competency Center date back to the 80’s, the first years in which the Consortium began its activity. During the ‘mainframe’ period we had, on one hand a support center on statistical aspects for the university researchers , and on the other hand, some experts that produced statistical reports for the members of the consortium in mainframe environment. The statistical experts were gradually grouped into a single unit from 1990 The first Datawarehouse project that crossed all areas was launched in 1996 for the Piedmont Region , to try and give a more homogenous approach to the different existing applications In 2000 we decided to converge to a single infrastructure both the hardware and the softwares of our 3 main customers, beginning the transfer of all applications from a client-server architecture to web architecture. The concept of BICC emerged from 2001 to 2005, with the extension of the overall DW project also to the Municipality of Turin. From 2006, the BICC was extended to the treatment of operational data and master data and was thus gradually transformed into a PSI-CC (Public Sector Information). From 2009, we launched an overall Data Governance initiative . Now we are experimenting some semantic aspects (like a semantic engine) and we are extracting knowledge from different typologies of data (texts, images, etc.) , so we are heading towards an I-PSI-CC (Intelligent PSI Competency Center)
  • The competency center works for all our associated institution. It is based on five layers a common hardware and network infrastructure A shared software layer for BI&amp;DQ solutions. We have a Framework contract for all stakeholders. The layer of data: some basic information is shared between all the entities (eg coding tables). Each institution and sometimes all areas of Business has a blueprint for the construction of the overall Data Warehouse structured in different levels (the BI pyramids that we will see later), A Metadata layer: There is a single asset register for all customers, related to data and IT services, including business information and technical information. For data and decision-making applications , this catalog is partly fed by the BI metadata repository. The technical and business metadata in the BI are partitioned for large customers. BI &amp; DQ applications : We Have carried out some transversal functions common to all customers and sometimes we share BI applications between different local governments
  • In the period 2005-2010 as you can see in this graph, the number of BI applications has grown significantly.
  • In this slide you can see a general blueprint for the construction of the overall Public Sector Data Warehouse, structured in different levels The basic layer, relating to the operational level, includes all the “operating” components of the sub-systems of the Information System of a certain Local government and the external data sources, that create as a whole, the original data sources. The decisional level is divided into two parts: The information layer for the middle management based on reporting and analytical function The information layer for the top management based on dashboard and gauge function
  • And now we go deep into the health world. In this slide we collected some significant numbers relating this topic in Piedmont so you can have a general overview.
  • After 10 years of different and specific BI application development for the Piedmont health government agencies, in 2009 the Piedmont Region adopted a comprehensive approach for Health regional Data Warehouse, gradually evolving to a complex integrated platform. After a feasibility study carried out in 2008, we began the setting up of the information – decisional layer (Data Warehouse) following this blueprint, named PADDI Program . The project is the integration of all data belonging to health management systems into a Enterprise Data Warehouse. This integration is the result of the implementation of data cleansing services and decisional systems and it enables regional health authorities to appropriately supervise health policies within their territory.
  • The goal is to answer to different information needs with this integrated platform. And now I introduce Veronica Berti, that will explain some detailed experience based on this platform.
  • I servizi decisionali devono quindi fornire diverse funzionalità di analisi a seconda del tipo di utente a cui sono indirizzati e della richiesta che devono soddisfare in base anche al tempo che si ha a disposizione
  • I’d like to thank you for your attention. I hope you found our presentation interesting. If you have any questions, we’d be happy to answer them. Please, use the mail addresses showed in this slide to send us your questions.
  • PADDI - A business intelligence and data quality platform for Piedmont health

    1. 1. PADDI: A Business Intelligence and Data Quality platform for Piedmont health Giuliana Bonello Business Intelligence & Data Quality Practice Manager, CSI-Piemonte Veronica Berti Health expert, Health Division, CSI-Piemonte
    2. 2. Founded in 1977 by: <ul><li>91 Associated Consortium Members </li></ul><ul><li>176 Millions € annual revenues </li></ul><ul><li>6 sites in Piedmont </li></ul><ul><li>1,200 employees </li></ul>CSI-Piemonte is a leading ICT company (among the biggest 20 Italian companies in the ICT sector), providing with services all segments of the public sector Who We Are Polytechnic of Turin University of Turin Piedmont Region
    3. 3. <ul><li>technologies and architectures </li></ul><ul><li>document management and related laws </li></ul><ul><li>policy making support systems </li></ul><ul><li>mission, organisation, business models of different PA bodies </li></ul><ul><li>Web sites and portals for the Public Administration </li></ul><ul><li>Cross-Authority-Databases and Business Intelligence applications </li></ul><ul><li>Training for civil servants </li></ul>Expertise more than 4 million hits every day more than 110 services are available
    4. 4. International activities <ul><li>Since 2005 CSI is actively involved in promoting its international presence both through EU Initiatives and External Cooperation Policy Measures: </li></ul><ul><ul><li>Relations and cooperation with our local Public Authorities and in cooperation with ICT partners of our territory (Think up project), promoting our best practices </li></ul></ul><ul><ul><li>International relations with donors (EC,WB,UN…). </li></ul></ul><ul><ul><li>Relation with Italian Ministries (Foreign Affairs, Public Administration, Environment, Health…) and for cooperation programme </li></ul></ul><ul><ul><li>Awarded projects : 45 </li></ul></ul>
    5. 5. <ul><li>A SAS customer in Italy for more than 30 years </li></ul><ul><li>SAS top customer at European scale for variety of tools used </li></ul><ul><li>A pool of skilled staff on SAS technology </li></ul><ul><li>A SAS competency centre (since 2004) </li></ul><ul><li>One of the first companies signing the accreditation process to the EMEA Professional Services Partner Program </li></ul><ul><li>The owner of a SAS R&D laboratory set up with Piedmont Region </li></ul>Relationship with SAS CSI-Piemonte is the Winner of the Enterprise Intelligence Award for the PA at the SAS Forum 2007 in Stockholm.
    6. 6. How Many Databases Today in Piedmont PA? Data update September 2010 Data base growing
    7. 7. Data  Knowledge  Decision Knowledge Operational Data <ul><li>Territory and Environment (324) </li></ul><ul><ul><li>Institutional activities (212) </li></ul></ul><ul><ul><li>Educational, culture and free time (62) </li></ul></ul><ul><ul><ul><li>Health-care systems (172) </li></ul></ul></ul><ul><ul><ul><ul><li>Producing activities and work-related topics (362) </li></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Human Resources (376) </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li> Demography </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Land Register and Taxes </li></ul></ul></ul></ul></ul><ul><ul><li>Agriculture </li></ul></ul><ul><ul><ul><li>Labour </li></ul></ul></ul><ul><ul><ul><ul><li>Internet and ICT </li></ul></ul></ul></ul>D A T A D A T A D A T A D A T A D A T A Information Decisions D A T A
    8. 8. BICC Organizational History 1980 1990 2000 1st DW project (Piedmont region) 2005 2010 From BICC to PSI-CC: BI & DQ Statistical center (mainframe) Towards I-PSI-CC BICC DQ First BICC phase: Centralisation for all customers
    9. 9. BICC Business model INFRASTRUCTURE SOFTWARE DATA B I C C PA Institution 3 PA Institution 2 PA Institution 1 PA Institution 4 METADATA APPLICATIONS All inclusive project dev. Central server farm Framework contract for all stakeholders Common “Core” functions Specific applications Single data and service catalogue BI Metadata are partitioned between the large customers Single coding tables Master Data for each customer All inclusive service
    10. 10. BI&DQ applications BI applications growing
    11. 11. Decisional Service Pyramid
    12. 12. Health numbers in Piedmont Administrative levels Health Ministry Regional Government Epidemiology research 13 Local Health Agency 8 Hospital Agency 4,5 million residents 4,000 doctors 19,000 beds in hospital 35 million prescriptions per year 66,5 million of professional medical services per year 800,000 hospitalizations 37.000 births per year 22 local health units
    13. 13. The decisional pyramid for health: PADDI
    14. 14. DW processes DQ <ul><li>Information needs </li></ul><ul><li>Monitoring & Evaluation </li></ul><ul><li>Analysis of single health sector field </li></ul><ul><li>Management control/budgeting </li></ul><ul><li>Epidemiological studies </li></ul><ul><li>Clinical trials </li></ul>Operational DB 1 2 Back-end Front-end Enterprise DWH <ul><li>Extraction, Transformation and Loading (ETL) </li></ul><ul><li>Data Warehouse ( anonymous data ) </li></ul><ul><li>Front end (with authentication & profiling) </li></ul>3
    15. 15. <ul><li>The SSN set up in 1978 guaranteees health care for all citizens. </li></ul><ul><li>It is mainly financed through the general taxation but the regions are obliget to keep under control the health care expenses by ensuring effectiveness of treatments. </li></ul>Very rich health information heritage Use of instruments of business intelligence WHY HOW Ensure the effectiveness of treatments by controlling the cost Highlight critical areas WHAT OUTCOME RESEARCH Goals
    16. 16. Hospital activities Health information heritage of the Piemont Region Health personnel Assistiti Territorial activities Prevention activities Health agency
    17. 17. OLAP Multidimensional analysis From information to knowledge Users Employees Middle managers Analyst Managers Type of Process Visualize Explore Discover Dashboard Query / Reporting Data Mining Time
    18. 18. Growth curve of knowledge How to optimize the results? What will happen? Why it happened? What it happened?
    19. 19. Reporting Monitoring Analysis Strategy Formulation Resource Allocation Data Quality Action plan Data Quality improvement Mining From strategy to data analysis
    20. 20. OUR GOALS Epoetine Linee Guida Ipertens Diabetici Fatt Coag Aura Biologici e AR Some example
    21. 21. Data Anonymize engine 4.457.335 Residents 31/12/2010 DQ Data Quality
    22. 22. Planning / Management Control Doctors – District Budget Patients Structures Drugs Hospitalization Emergencies Salaries Health Centres Addictions Booking Screening Training AbsencePresence Home Care Structures Health Personal - Doctors Patients TransversalData mart Sectorial Data mart Registry Data mart <ul><li>Institution : </li></ul><ul><li>Region </li></ul><ul><li>Health agencies </li></ul><ul><li>Type of user: </li></ul><ul><li>Analyst </li></ul>Drugs and Chemistries Decisional System Indicator System Directional Dashboard Query / Reporting Data Mining OLAP Multidimensional Analysis
    23. 23. Goals: Implementare l`uso di epoetine biosimilari nei soggetti nefropatici naive per la dialisi. DB : Medical visit Drug information Drug administration Biosimilar epoetin Dashboard
    24. 24. <ul><li>DB coinvolti: </li></ul><ul><li>Pathology`register </li></ul><ul><li>Medical visit </li></ul><ul><li>Hospitalization </li></ul><ul><li>Drug administration </li></ul>Goals: Determinare se una migliore aderenza alla terapia farmacologica corrisponde una minore spesa per assistenza ospedaliera. Diabetics Dashboard
    25. 25. <ul><li>DB coinvolti: </li></ul><ul><li>Rares Diseases Register </li></ul><ul><li>Personal data of assisted </li></ul><ul><li>Drugs administration </li></ul>Goals: Individuare soggetti che assumono fattori di coagulazione off label. Clotting factors Dashboard
    26. 26. Biological medicines are particularly expensive; their profiles of safety ad efficacy are not full Known. Ensuring effectiveness of treatments and keep under control the health care expenses Pathologys` register Hospitalization, drugs` administration pathologys` register WHY HOW Ensure the effectiveness of treatments by controlling the cost Highlight critical areas WHAT Limited the prenscription only by Hospital doctors. Biologic drugs for rheumatoid arthritis
    27. 27. Data Mining
    28. 28. Guide lines for the treatment of Hypertension The iue of drugs` association only on risk` subject (comorbidity) Drugs administration data WHY HOW Compliance with guide lines WHAT Azione di richiamo dei medici prescrittori che non hanno rispettato le indicazioni delle linee guida. Hypertension’s treatment
    29. 29. Pazienti a rischio Data Mining
    30. 30. <ul><li>Thank you for your attention! </li></ul><ul><li>Giuliana Bonello </li></ul><ul><ul><ul><li>[email_address] </li></ul></ul></ul><ul><ul><ul><li>Veronica Berti </li></ul></ul></ul><ul><ul><ul><li>[email_address] </li></ul></ul></ul><ul><ul><ul><li>www.csipiemonte.it/eu </li></ul></ul></ul>© CSI-Piemonte – Tutti i diritti riservati
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