Data Analytics in Health Care

Transcription

1 Data Analytics in Health Care ONUP 2016 April 4, 2016 Presented by: Dennis Giokas, CTO, Innovation Ecosystem Group

2 A lot of data, but limited information 2

3 Data collection might be the single greatest advantage that new technologies can offer to health care. In the past, providers have been totally divorced from the results of their work. If you were to ask a family physician how many people they ve helped quit smoking they d never be able to tell you, and that s probably the most effective thing a clinician can do (for a patient s health). Dr. Sanjeev Goel Wise Elephant Family Health Team, Brampton, ON Source: THE MEDICAL POST NEWS, September 15,

4 Insights from EMR Data List patients who are due or overdue for tests or preventive care (e.g., flu vaccine) 69% 48% Generate reminder notices when it is time for regular preventive or follow up care (e.g., HbA1c tests for patients with diabetes) 49% 18% Generate reminders for guideline based interventions and/or screening tests 69% 26% Source: 2015 Commonwealth Fund International Health Policy Survey of Primary Care Physicians 4

5 EMR performance and quality improvement analysis Review clinical performance against targets at least annually 52% 41% Routinely receive information on how the clinical performance of their practice compares with that of other practices 37% 17% Measure patient outcomes or experiences 51% 23% Source: 2015 Commonwealth Fund International Health Policy Survey of Primary Care Physicians 5

6 Health Analytics Defined 6

7 Health Analytics Definition The systematic use of data, information technology and methods to create insights in context that inform clinical and business decision making around the planning, delivery, management and measurement of health care. Adapted from HIMSS Clinical and Business Intelligence Community of Interest, module_ _FINAL.pdf 7

8 Categories of Health Analytics Sources: CIHI and Infoway on behalf of Health System Use Technical Advisory Committee, June 2009 Health System Use Vision Paper, endorsed by Conference of Deputy Ministers, June

9 Types of Health Analytics How? Prescriptive Identifying personalized treatment options best suited for the patient Capability What if? What s next? Predictive Simulation models, learning models, data mining on various data points to predict patterns What is happening now? Descriptive Standardized, static views into the data, drill down and slice and dice data to understand root cause When did it happen? Why did it happen? Timeline and maturity dependent 9

10 Key Characteristics of Health Data 10

11 Analytics example Descriptive Example The simplest type of analytics is focused on what has happened in the past A query that produces a summarized list of patients with diabetes that haven t been seen by their primary care team in more than 1 year for the management of their condition 11 11

12 Second example of analytics Predictive Example An advanced form of analytics that is focused on forecasting what may happen in the future The analysis may use structured and unstructured data from various sources to predict which patients may be at risk of a serious health setback leading to costly interactions with the health care system 12 12

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15 Digital Health Environment 15

16 Health Analytics in a Digital Health Ecosystem Diagnostics EHR Data and Services HIAL for Interoperability and Process Point of Service Applications We assumed that it would be in the EHRS above the HIAL in EHRS Blueprint v2 Devices Social Analytics Reference Mobile Apps Genomics 16

17 We have become like the most primitive Palaeolithic man, once more global wanderers, but information gatherers rather than food gatherers. From now on the source of food, wealth and life itself will be information. Source: "The Agenbite of Outwit, McLuhan Studies, Volume 1 Issue 2, January

18 Health Analytics in a Digital Health Ecosystem Diagnostics EHR Data and Services HIAL for Interoperability and Process Point of Service Applications Data for analytics comes from many sources Devices Social Analytics Reference Mobile Apps Genomics 18

19 Health Analytics in a Digital Health Ecosystem Diagnostics EHR Data and Services HIAL for Interoperability and Process Point of Service Applications Data from many platforms need to be integrated, persisted and linked Devices Social Analytics Reference Mobile Apps Genomics 19

20 Health Analytics in a Digital Health Ecosystem Diagnostics EHR Data and Services HIAL for Interoperability and Process Point of Service Applications Devices Analytics to support care can be performed on any of these platforms Social Analytics Reference Mobile Apps Genomics 20

21 Health Analytics in a Digital Health Ecosystem Diagnostics EHR Data and Services HIAL for Interoperability and Process Point of Service Applications Devices Variety of factors will dictate if a specialized platform or services are required Social Analytics Reference Mobile Apps Genomics 21

22 Health Analytics in a Digital Health Ecosystem Diagnostics EHR Data and Services HIAL for Interoperability and Process Point of Service Applications To support care the resulting insights from analytics may be provisioned on many platforms Devices Social Analytics Reference Mobile Apps Genomics 22

23 Health Analytics in a Digital Health Ecosystem Diagnostics EHR Data and Services HIAL for Interoperability and Process Point of Service Applications Governance is a critical enabler for health analytics in digital health Devices Social Analytics Reference Mobile Apps Genomics 23

24 Health Analytics in a Digital Health Ecosystem Diagnostics EHR Data and Services HIAL for Interoperability and Process Point of Service Applications Many considerations (e.g. privacy and security) for analytics need to be applied consistently across a variety of platforms Devices Social Analytics Reference Mobile Apps Genomics 24

25 Health Analytics in a Digital Health Ecosystem Diagnostics EHR Data and Services HIAL for Interoperability and Process Point of Service Applications EHRS provides a rich set of event and observational data, registries and master data management techniques that enable the correlation of data across a broad range of sources Devices Social Analytics Reference Mobile Apps Genomics 25

26 Health Analytics in a Digital Health Ecosystem Diagnostics EHR Data and Services HIAL for Interoperability and Process Point of Service Applications HIAL provides a rich set of standardized and common privacy and security services to protect PHI data Devices Social Analytics Reference Mobile Apps Genomics 26

27 Implications Every thing, business and person will be (or is) digital, much of it real time Big Data requires IT to aid the human Analytics to aid and alert given the volume and complexity of data Intelligent linkage to knowledge bases for information and evidence Visualization challenges new tools and techniques Interoperability and semantic challenges Data quality Own your database or ensure it is open Consider open data sets 27

28 Enable a High-Performing Health System Functional Considerations 28

29 Health Analytics Functions HIT Vision, Business Plan, IT Strategy (Processes, initiatives and priorities - Clinical and business questions) Source Acquire Persist Analyze Provision Privacy and Security Governance 29

30 Source Source Source of truth Data quality is appropriate for intended use Scope of data Completeness Data is accessible Authorized for use 30

31 Acquire Acquire Acquisition strategy and mechanism appropriate to use: Real-time, time period, event Incremental, full or continuous Summarized or record level Linkage mechanisms preserved Privacy safeguards applied 31

32 Persist Persist Designed for use: Scope of use Type of analytics Granularity of data Linkable as required Data architecture Platform specific considerations Appropriate privacy and security safeguards applied 32

33 Analyze Analyze Appropriate to use: Configurable Fit for purpose and setting Specialized analytics platform: Complex, big data or performance intensive Advanced types Specialized knowledge base 33

34 Provision Provision User experience: Appropriate for device Seamless integration o Based on screen flow and workflow o Fast, simple navigation Visualization o Relevant data in context o Consistent and suitable to use Actions (e.g. re-identification) Appropriate privacy safeguards applied 34

35 Use Case with P&S Considerations Questions: Prevalence of chronic disease in populations Identify patients at risk Outcomes analysis Users: Health system managers Clinical groups and patient s physician Researchers Assumptions: Same data warehouse for uses and users listed above PHI from the EHR, EMR and HIS Pseudonymization of the PHI 35

36 Example of Privacy/Security Considerations Source Acquire Persist Analyze Provision Define Use Define Data Encrypt data Deidentification Consent Link data and preserve confidentiality Data Location Data Security Define Use REB approval Processing Location Consent Define Use User and Role Based Access Control Cross functional: Privacy and Security, Data Sharing Agreements, Safeguards Collection/Use/Disclosure Authority, Analytics Governance 36

37 Deployment Considerations 37

38 Analytics in Point of Service Systems Data quality and extraction tools Use of common data standards and pick-lists to reduce free text Ability to extract data sets Analytics and data visualization Basic search, query and reporting functions Ability to use information to track key clinical conditions including reporting for chronic disease Dashboards or other presentation format Uses Assistance with the standardized assessment and treatment of common clinical conditions Notifications, alerts and reminders to support chronic disease screening and immunization through the life span Risk calculators 38

39 Capabilities via an Analytics Service Infrastructure Data warehouse Solution scalability for large data set(s) Natural language processing tools Master data management tools to identify and link patient data from multiple sources Data quality tools Analysis and Reporting Business intelligence tools for analysis and reporting Dynamic analysis reporting (e.g., complex analyses on multiple data dimensions) Query, analysis and reporting on unstructured data Benchmarking to peer practices (e.g., process, outcome indicators) Advanced analytical capabilities (e.g., predictive, prescriptive) Visualization Ability to integrate report output with other related objects (e.g., maps for geolocation) 39

40 Health Analytics Principles Health Analytic Solutions need to: Use highest quality, standardized data Capture data once, use it often Link data from appropriate sources Implement privacy and security safeguards Present timely insights in context Deploy in a services oriented architecture Use open standards 40

41 Strategic Planning and Maturity Models 41

42 Roadmap and Maturity Model Analytics is fundamental to business of health care Driven by the business Aligned with strategy Roadmap and maturity viewed from various levels Pan-Canadian Jurisdictional Regional Organizational Departmental 42

43 Roadmap Principles Align analytics strategy with business plan and digital health ICT roadmap Analytics should not be an after thought. Design into core EHR, EMR and HIS up front, or design in data extracts Evolutionary - deploy in small and gradual steps Focus on functionality and timelines to deliver early value Collaborate where possible Leverage existing EHRS, IT, data warehouse, business intelligence investments and standards, wherever possible 43

44 Health Analytics Maturity Model Outlines levels of analytics maturity Used to measure level of maturity across four dimensions: Data Culture and change management Governance Infrastructure and skills 44

45 Proposed Health Analytics Maturity Model Level 5 Advanced Prescriptive and predictive patient centric analytics Level 4 Mature Data standardized, sophisticated and robust enterprise data warehouse Level 3 Planned Repeatable and automated descriptive analytics Level 2 Ad-hoc Basic descriptive analytics (canned reports) on some data that has been standardized Level 1 Limited Data not standardized, limited analytics or not planned for Data Change & culture Governance Skills and infrastructure Adapted and informed from 1) Dale Saunders, Health Catalyst, 2) HIMMS Analytics - DELTA Powered Analytics Maturity Suite, 3) TDWI Analytics Maturity Model 45

46 Maturity Model by Function 46

47 Maturity Model Principles Each function can be at a different level Maturity can be attained in leaps Maturity for local use (PoS) and interorganizational (service use) may be at different levels 47

48 Summary and Next Steps 48

49 Call to Action Leadership Collaboration Execution Appoint a clinical lead to champion the use of analytics Contribute to the governance of any analytics network you may join Establish quality improvement planning Create strategy roadmap for the use of analytics Recognize and communicate success Collaborate with communities of interest to fast track your start-up activities and achieve economies of scale wherever possible Leverage existing artefacts such as PIA tools, data sharing agreements Collaborate with your user group or community Invest in staff training & development to build skills and capacity Standardize on data entry Deliver value early by focusing on basic descriptive analytics targeting issues that resonate widely with the team Replicate and scale successful processes and initiatives Initiate planning to advance the maturity of use to include predictive analytics and the use of tools such as NLP to analyze unstructured data 49

50 Clinical analytics should be regarded as a core functionality. Success will require business process transformation to reduce variation Good user experience requires access to content with minimal disruption in workflow. A first step is to introduce standardization of data and processes Expect more analytical functionality and capability from vendor solutions Focus on the enablers, algorithms and visualization to optimize value for providers and patients 50

51 Questions 51

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