Choosing an Analytics Solution in Healthcare


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Choosing an Analytics Solution in Healthcare

  1. 1. Document owner: Dale Sanders Email: Date: March 2013 Guidance for Evaluating and Choosing an Analytics Solution in Healthcare
  2. 2. 2 Overview  General criteria for the options assessment  Framing the analytic options assessment  What are the factors affecting analytics in the industry?  What are the guiding concepts and philosophies?  What’s the trajectory of the industry and how should we adjust?  Specific criteria for choosing an analytic solution  Technical and cultural change management  Vendors in the space… and its crowded
  3. 3. 3 General Criteria For Options Assessment  Completeness of Vision  Lessons from the past, understanding of the present, vision of the future  Ability to Execute   References and scalability Time to Value  Culture and Values of Senior Leadership  Do they align with yours?  Technology Adaptability & Supportability  How fast can the system adapt to the market and your unique needs for differentiation?  Total Cost of Ownership  Affordability  Company Viability  Will they be around in 8 years? If not, can you live without them? I score these on a 1-10 basis, for each vendor and option
  4. 4. 4 Framing the Analytic Environment for Healthcare
  5. 5. 5 The Core Analytic Issue Healthcare Value = Quality of Health Cost of Care Everything we do analytically should relate back to a better understanding of both the numerator and denominator, in an integrated fashion. They are inseparable.
  6. 6. 6 Technology x Change = Solution  “The prerequisite is the technological infrastructure. The harder thing is to get the set of skills…and that includes not just the analytical skills, but also a set of attitudes and understanding of the business. And then the third thing which is the subtlest, but perhaps the most important is this cultural change…this attitude about how to use data. There are a lot of companies who think they are using data…but historically that sort of data has been used to confirm and support decisions that had already been made by management, rather than learn new things and discover what the right answer is. So the cultural change is for managers to be willing to say, „That‟s an interesting problem, that‟s an interesting question. Let‟s set up an analysis to understand it; let‟s set up an experiment.” They have to be willing to open up and in some ways show some vulnerability and say “Look we are open to the data.” Erik Brynjolfsson, the Schussel Family Professor of Management Science at the Massachusetts Institute of Technology, Director of the MIT Center for Digital Business
  7. 7. Technology Adaptability: The Evolving Data Ecosystem Analytics are driven by ACOs, mergers, acquisitions and need for “system-ness” ACO IDN Hospital Clinic Data content is essentially non-existent at present in healthcare delivery Social Care Community Home 7
  8. 8. Adaptability: The Evolving Analytic Motives We need to be more driven by these… Quality of Life & Health Prevention & Intervention Utilization This is where we are analytically, right now Billing & Compliance 8
  9. 9. 9 What have we learned from EMR adoption?  Best-of-breed, point solutions are challenging to operate     Fragmented data Redundant technology infrastructure High TCO Multiple skill sets required  The fully-integrated platforms such Cerner and Epic are more effective  “Meaningful use” of the technology is critically important  We are seeing the same patterns in analytics  Numerous fragmented point solutions, data quality problems  Producing reports but not applying the analytics to affect quality and cost
  10. 10. Healthcare Analytics Adoption Model Level 8 Personalized Medicine & Prescriptive Analytics Tailoring patient care based on population outcomes and genetic data. Fee-for-quality rewards health maintenance. Level 7 Clinical Risk Intervention & Predictive Analytics Organizational processes for intervention are supported with predictive risk models. Fee-for-quality includes fixed per capita payment. Level 6 Population Health Management & Suggestive Analytics Tailoring patient care based upon population metrics. Fee-for-quality includes bundled per case payment. Level 5 Waste & Care Variability Reduction Reducing variability in care processes. Focusing on internal optimization and waste reduction. Level 4 Automated External Reporting Efficient, consistent production of reports & adaptability to changing requirements. Level 3 Automated Internal Reporting Efficient, consistent production of reports & widespread availability in the organization. Level 2 Standardized Vocabulary & Patient Registries Relating and organizing the core data content. Level 1 Enterprise Data Warehouse Collecting and integrating the core data content. Level 0 Fragmented Point Solutions Inefficient, inconsistent versions of the truth. Cumbersome internal and external reporting.
  11. 11. Progression in the Model The patterns at each level • Data content expands • • Data timeliness increases • • To support faster decision cycles and lower “Mean Time To Improvement” Data governance expands • • Adding new sources of data to expand our understanding of care delivery and the patient Advocating greater data access, utilization, and quality The complexity of data binding and algorithms increases • From descriptive to prescriptive analytics • From “What happened?” to “What should we do?”
  12. 12. The Expanding Ecosystem of Data Content 1. Billing data 2. Lab data 3. Imaging data 4. Inpatient EMR data 5. Outpatient EMR data 6. Claims data 7. HIE data 8. Detailed cost accounting data* 9. Bedside monitoring data 10. External pharmacy data 11. Familial data 12. Home monitoring data 13. Patient reported outcomes data* 14. Long term care facility data 15. Genomic data 16. Real time 7x24 biometric monitoring data for all patients in the ACO Now 1-2 years 2-4 years * - Not currently being addressed by vendor products 12
  13. 13. Closed Loop Analytic Experience 14 • Culture & Organization Knowledge Systems • • • • EMR, pharmacy, lab, imaging, RCM, materials management, cost accounting Care process algorithms Triage criteria, order sets, protocols Provider and patient education material Patient and care management reports Technology Deployment System • • • • • Organizational data literacy Process improvement training Clinical leadership teams Data & knowledge asset governance Steering and guidance committees Analytics System • • • • • • Quality of Care vs. Cost of Care Enterprise data warehouse Data visualization Data access & production Metadata management Patient cohorts
  14. 14. 15 Knowledge Systems Deeper In the Details The Technology Deployment System Analytics System
  15. 15. 16 The Technology Components 1. ETL 2. Data Modeling & Analytic Logic 3. Master Reference/Master Data Management 4. Metadata 5. Visualization 6. Security 7. EDW Performance and Utilization Metrics 8. Hardware and Software Infrastructure
  16. 16. 17 Master Data Management Source Systems ETL Data Warehous e Visualization Security Metadata EDW Performance Metrics Hardware & Software Infrastructure
  17. 17. 18 ETL  Key issues: Reliability, supportability, reuse  What tools does the solution use? Who owns the licenses?  What is the ETL design for updates? (Full, incremental, both)?  Does the solution have a library of ETL “accelerators” to common source systems?
  18. 18. Data Modeling  Options, in order of preference  Bus Architecture  Kimball Dimensional Star Schema  Inmon Corporate Information Model  I2B2  Hybrid  Bus architecture is rapidly adaptable and very flexible. It places more emphasis on data marts that support specific analytic needs and scenarios, rather than a general analytic model to support all analytic needs, especially those that are focused on patient cohorts and registries.  Dimensional models have a very limited scope of usefulness in healthcare– typically best suited for finance and materials management/supply chain analytics, only.  Purchasing an Enterprise Model might seem like a good idea, but the ETL is very difficult to maintain; the model is not easily adaptable to new source systems; and analysts prefer more specific models to suit their needs.  I2B2 is very specific to healthcare, particularly designed to support academic medical centers, but it is very complex. Few people in the country understand it and can support it, and its usefulness in meeting more typical analytic scenarios is questionable.  No single data modeling strategy will meet all analytic scenarios. 19
  19. 19. 20 Data Mart Data Modeling  The data models are important, but the analytic logic associated with the content of the data marts and reporting is more important EDW Clinical Financial Other High value logic Oncology Data Mart
  20. 20. Data Models and Reporting Logic  Does the solution support each layer? Prove it… 21
  21. 21. Master Reference Data/Master Data Management  What is the vendor’s strategy?  Mandatory or voluntary compliance and mapping to master data content?  Mandatory compliance and mapping is unnecessary and can lead to disaster  What data model and structures are used to support the content?  How does the vendor accommodate international, national, regional, and local master data management?  Do they use an external vendor partner?  Do they support mappings to RxNorm, LOINC, SNOMED, ICD, CPT, HCPCs?  Do they support a user-friend interface terminology? 22
  22. 22. 23 Metadata Repository  Can you browse and search metadata from a web interface?  Does the solution require an expensive add-on tool?  Does it collect metadata from ETL jobs and the database engine?  Does it allow a “wiki” style contribution of content?
  23. 23. 24 Visualization Layer  Is there a bundled, preferred visualization tool?  Is it affordable and extensible if exposed to all employees and patients?  Is the data model(s) decoupled from the visualization tool?  Does the data model support multiple visualization tools and delivery of data content?
  24. 24. 25 Security  Are there fewer than 20 roles in the initial deployment?  Does the solution employ database level security, visualization layer security, or some combination of both?  Does the vendor’s security philosophy pass the test for maintainability?  Does it balance security with access?  How does it handle patient identifiable data?  How does the security model manage access to extremely sensitive personal health information, such as behavioral health, AIDS, etc.?  How does is handle physician identifiable data?  What type of tools and reports are available for managing security? Can the tools identify “unusual” behavior, such as repeated mass downloads of data?
  25. 25. 26 EDW Performance and Management Metrics  Can the solution track basic data about the environment, such as:  User access patterns  Query response times  Data access patterns  Volumes of data  Data objects
  26. 26. 27 Hardware and Software Infrastructure  Oracle, Microsoft, IBM are the only realistic options  Microsoft is the most integrated, easy to manage, and affordable… from database management through analytic desktop  Scalability is no longer an issue– it scales to multiterabyte databases, easily  Windows is viable and can compete with Unix in all but the largest clusters…years away, if ever, for most healthcare organizations  IBM is a good second choice, but has a small market share  Oracle is expensive and lacks integrated tools
  27. 27. 28 Knowledge Systems Deployment System Analytic System Cultural Change Management Knowledge and Deployment Systems
  28. 28. 29 Change Management  Does the vendor support closed loop analytics that bends analytic knowledge back to the point of care and/or workflow?  What do their customers say about their ability to improve care and reduce costs?  Have they had experience with actually realizing an ROI from the analytic system?  What are the success stories-- where quality of care improved? Cost of care decreased?  What tools and processes does the solution have to support:  Continuous quality improvement and cultural change initiatives?  Cost control initiatives?  Activity based costing?  Prioritization of analytic efforts and improvement programs?  What tools or experience does the solution offer for data governance? Data stewardship?
  29. 29. 30 Clinical Content and Evidence Based Analytics  Does the solution leverage evidence based clinical content in the design?  Data model, patient registries, benchmarking  Are the analytics on the back end integrated with evidence based data collection on the front end, such as order sets and clinical guidelines?  Can the system measure adherence to clinical evidence and guidelines?
  30. 30. 31 Timelines and Costs  Can the solution offer business value in less than 3 months, in constant increments?  Does the solution cost less than $7M over three years for a $1B - 2B organization (scale up and down accordingly)?
  31. 31. 32 Vendors in the Crowded Market              4medica Analytics8 Ascender Cerner CitiusTech Cognizant Crimson Epic Explorys Health Care Dataworks Health Catalyst HealthBridge Humedica              IBM MedeAnalytics MEDecision Oracle Perficient Predixion Recombinant PSCI Sajix SpectraMD Strata Decision Technology White Cloud Analytics ZirMed
  32. 32. 33 In Summary…  The analytic environment in healthcare is rapidly changing, and that’s not going to stop  Adaptability of the technology is crucial  Technology is only 1/3 of the solution  Cultural willingness to embrace analytics is crucial  Cultural processes for sustained implementation are crucial  Look for a vendor that offers a total solution– closed loop analytics