1. EMIS Supporting Information-based Decision-making in Montréal’s Healthcare System Community Data Canada Roundtable March 9, 2011, Toronto Carl Drouin Direction de santé publique Agence de la santé et des services sociaux de Montréal Direction de santé publique

2. Presentation outline  Context in which EMIS was created  Key principles guiding our strategy in public health data dissemination  Demo of EMIS Website  Content and structure  Tools  Concluding remarks

3. Organizational context of EMIS Ministry of health and social services 18 regional ‘’agencies’’ Public health division 12 local Health and Social Services Centres (CSSS) in Montreal Plan and provide services according to population health needs Data-related responsibilities  Medical service providers  Municipalities and boroughs  Community organizations  Schools and school boards  Etc. Local networks  Track the health status of Montrealers (surveillance)  Inform the population and decision makers about health and its determinants  Manage and provide information on the health care system (services delivered, resources, performance, etc.) INFO ON THE HEALTH SYSTEM + INFO ON PEOPLE’S HEALTH = SUPPORT PLANNING IN HEALTH

4. Advantages of EMIS  Organizational perspective  A single platform using same tools and technologies  More integration in data collection  Uniformity in data treatment  Less duplication in production and dissemination processes  Development and maintenance supported by a larger group with complementary skills (IT, GIS, analytical expertise related to Service programs and PH data)  User’s perspective  A single reference on health data in Montreal (with same structure, interactive Web-based tools and analytical publications)  Same data can be used for different purposes  Participate in Website development and validation of further improvements  Provide settings to enhance data-driven decision-making

5. Key principles  Go local when possible  Diversify products  Standardize analytical products and tools  Develop products and Website with partners and data users  Integrate within a larger Web strategy (design, technology)

6. Provide local data  Census geography (CTs and DAs)  Health administrative units  CSSS, CLSS, voisinagesvoisinages  By health institutions (e.g. hospitals)  Sometimes adapted to other geographies (school boards)  More and more of our PH publications are done in series of 12 (CSSS)  Working on a local survey program

7. 111 neighbourhoods

8. Different products for diversified users More meaningful information = more efforts = more users? = used for decision- making?

9. Interactive toolsStandardized analytical products

10. Chiffres-clés (Key numbers)

11. Place-based (neighbourhoods) characteristics - Montreal

12. Partnerships PartnersRoles Local health centres (through Data Users’ Committee and working groups) Express needs, validate proposals; test applications; promote use of data Information providers within the organization (mainly PH subdivisions) Provide surveillance data within their field of expertise; use available technologies and products Communications departmentWeb strategy; support information process; advise on best practices Computer departmentAdvise on technological orientations; support development

13. Agency’s corporate Website EMIS Public health director’s Website Regional portal Zone for professionals Zone for physicians Agency’s Website redevelopment Other health data Websites Local Websites

14. DEMO

15. Concluding remarks Challenges to be met  Allow the time required to increase the value of the data  Keep a large amount of data and information up-to-date  Continuous feeding of those involved in surveillance  Support data utilization among local and community partners  Work on structural (e.g. menus) and technological (e.g. indicators module instead of Excel and PDF files) improvements Key message  Such type of Website requires to be part of a larger information system (i.e. data – qualified HR – tools – standardized processes)

16. Ideas for discussion  How can data really provide bases for reduction in social health inequalities?