1. 1jointlearningnetwork.org JLN PPM/IT Collaborative on Data Analytics for Monitoring Provider Payment Systems Third In-Person Meeting Accra, Ghana March 21-23, 2016 Session 3: Healthcare Analytics – Moving up the Maturity Ladder

2. 2 Session 3: Healthcare Analytics  Where we are in our framework steps  What goes in the toolkit  Guidance on data analytics capabilities  Country experiences  Additional resources  Panel discussion – Healthcare Data Analytics  Presentation – Healthcare Informatics 101

3. jointlearningnetwork.org3 Health Data Analytics Step 1. Identify the Policy Questions: What questions do the stakeholders need answered? Step 2. Select Indicators Which indicators can help answer the questions ? Step 3. Map the Data Where can the data be found and how should it be captured? Step 4. Monitor and Improve Data Quality What needs to be done to improve data quality ? Step 5. Data Analysis and Reporting What kind of analysis and output would be most useful? Step 6. Decisionmaking How will the analysis be used for decisionmaking? Data reporting and analysis infrastructure Analysis and ToolsPlatformData management

4. 4 Infrastructure for data reporting and analysis Data Foundations Data Management Data governance Organization structure Change management Staff roles, responsibilities and skills Data dictionaries, data/metadata definitions Semantic interoperability, standardized terminology and code sets Data integrity, data quality, data cleansing Data models Platform Hardware and software for data storage, databases, data warehouses Cloud based solutions Data integration, interface engines Disaster recovery, data security, data audit Analysis and Tools Healthcare analytics subject matter expertise Reporting and analysis tools Visualization and dashboard tools

5. 5 Develop reporting and analysis in a way that increases the organization’s informatics maturity over time Advanced Healthcare Informatics Maturity Level IntermediateBasicManual Data reporting and analysis infrastructure

6. 6 Healthcare Informatics 101  Science of processing data for storage and retrieval; information science.  Interdisciplinary study of the design, development, adoption, and application of IT-based innovations in healthcare services delivery, management, and planning. (US National Library of Medicine)  The science of informatics drives innovation that is defining future approaches to information and knowledge management in biomedical research, clinical care, and public health. (AMIA) Informatics Defined

7. 7 Infrastructure for data reporting and analysis Data Foundations Data Management Data governance Organization structure Change management Staff roles, responsibilities and skills Data dictionaries, data/metadata definitions Semantic interoperability, standardized terminology and code sets Data integrity, data quality, data cleansing Data models Platform (Infrastructure) Hardware and software for data storage, databases, data warehouses Cloud based solutions Data integration, interface engines Disaster recovery, data security, data audit Analysis and Tools Healthcare analytics subject matter expertise Reporting and analysis tools Visualization and dashboard tools

8. 8 A Healthcare Analytics Adoption Model Source: Dell Healthcare Services (November 2013) Healthcare Analytics: The Way Forward Prescriptive Predictive Performance management Enterprise-wide dashboards/scorecards Basic reporting Operational, regulatory Data foundation Data integration, data warehouse, master data Data management Analytics Business intelligence

9. 9 Healthcare Informatics 101  Data Warehouse  Analytics  Security and Privacy  Technical and Administrative safeguards  Access and data sharing policies  Practices and Culture of Data Use: Ongoing collection and systematic review (e.g. Surveillance)  Predictive analytics in healthcare is the wave of the future : how to harness big data, implement the right infrastructure for generating actionable insights from new sources. how to harness big data, implement the right infrastructure for generating actionable insights from new sources. Components

10. Data Warehouse Provides routine functionality to integrate http://dhis2.github.io/dhis2- docs/2.21/en/implementer/html/ch05.html

11. Data Warehouse and Visualization Example

12. Benefits of Maintaining a Data Warehouse Consistency: It provides a common data model for all relevant data and acts as an abstraction over a number of data sources and feeding systems which makes it a lot easier to perform analysis. Reliability: It is detached from the sources where the data originated from and is hence not affected if data in the operational systems is purged or lost. Analysis performance: It is designed for maximum performance for data retrieval and analysis in contrast to operational system which are often optimized for data capture.

13. 13 Healthcare Informatics 101 1.Should easily integrate data sources. Minimum data and standardized formats for ELT must be established 2.Build vs. buy/adopt 3.Interoperability 4.Visualizations (within or separate/3 rd party platform): only as good as your source data. Timely, multiple source, integrated data can provide rich insights! 5.Early warning system: establishment of rule based alerts: modeling, baseline and thresholds 6.Define Monitoring measures and reports first 7.Define and know your users 8.Establish ‘Cost of Ownership’ so systems and resources can be allocated and sustained  Timeliness and completeness  defined and known Near real time? Data Warehouse Considerations

14. Enterprise Architecture (OpenHIE)

15. 15 Healthcare Informatics 101  Philippines: Disease trends information (Pneumonia) by hospital, by year. Claims valuation. Led to clinical disease guidelines change for length of stay. Matching priority diseases to enhanced benefit plan (cost benefit and analysis). Policy actions. Dashboards. Development of benefit plan.  Kazakhstan: Moving up on the the maturity scale. Monitoring reports, data standards, interoperability  Canada: Monitoring in comparing databases for potential fraud. Patient surveys for quality of care. Will this get tied to provider payments?  Ghana: Unique IDs and coding is critical. Use of analytics (Excel and Tableau) valuable for outliers, dashboard for Early Warning System for unintended consequences or gaming behavior.  India: Smartcards with biometrics in use, with some limited exclusions. State insurance schemes for tertiary care. Claims process is automated. Examples from Countries Today – all inspiring!

16. 16 Healthcare Informatics 101 Questions when designing a system:  How and when do you involve providers and end users? And provide analysis access  How much data is enough? Who decides?  Timeliness – near real time? Or is monthly, quarterly ok?  Who should see what, when?  How to obtain funding for ICT / IT?  How will your monitoring evolve?  What systems are feasible? (centrally and hospital level) Examples from Countries Today – all inspiring! Now Questions…

17. 17 Healthcare Informatics 101  Shows systems lead to improve services, resource allocation, improved patient outcomes.  Elaborate the maturity model best practices  Informs policy changes (updated circulars) Best practices:  Establish questions to be answered, requirements of system, maturity model. May need to strengthen underlying systems  Establish Access information – where ever it resides  Creating dashboards  Establish national presence and guidance – TWG, HIS leadership  Establish national ICT Policies so this can inform other policies! Evidence and Data for Decision Making:

18. Intersection of Data Management and Analytics for quality data Quality Assurance and Quality Control – Analytics will, when designed right, show trends AND data quality problems - This is what? Not bad, but not ideal. -Ideally, we want Quality Assurance -Best Practices in Data Management: IT and data collection processes assure quality data in, not needing to be fixed later, less errors -Example -BOTH

19. 19 Resources  ISDS - http://www.who.int/workforcealliance/members_part ners/member_list/isds/en/ http://www.who.int/workforcealliance/members_part ners/member_list/isds/en/  OpenHIE - https://ohie.org  DAMA - https://www.dama.org/content/body- knowledge  Asia eHealth Information Network: http://www.aehin.org  WHO eHealth - http://www.who.int/ehealth/en/  Tableau: http://www.tableau.com/stories/http://www.tableau.com/stories/

20. 20 www.jointlearningnetwork.org