Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Healthcare 2.0: The Age of Analytics


Published on

While Healthcare 1.0 was broadly defined by a focus on defensive medicine, billing, and fee-for-service, culminating in the mass adoption of EMRs, Healthcare 2.0 is a new wave focused on improving clinical efficiency, quality of care, affordability, and fee-for-value; culminating in a new age of healthcare analytics. This new age of analytics will require a new set of organizational skills and a foundational set of analytic information systems that many executives have not anticipated.

Join Dale Sanders, a 20-year healthcare CIO veteran and the industry's leading analytics expert, as he discusses his lessons learned, best practices in analytics, and what the C-level suite needs to know about this topic, now. Listen to Dale discuss 1) A step-by-step curriculum for analytic adoption and maturity in healthcare organizations, 2) the basic approach to a late-binding data warehouse, 3) pros and cons of early versus late binding, 4) the volatility in vocabulary and business rules in healthcare, 5) how to engineer your data to accommodate volatility in the future

Healthcare 2.0: The Age of Analytics

  1. 1. © 2013 Health Catalyst © 2013 Health Catalyst The Age of Analytics Healthcare 2.0: Dale Sanders, Aug 2013
  2. 2. © 2013 Health Catalyst Overview 2 KeyPrinciples • Every Industry: How Data Management Evolves • Preserving Agility: Analytic Data Binding • The Triple Aim: Closed Loop Analytics • The Roadmap: Healthcare Analytic Adoption Model EvaluatingOptions • Strategy & Vendor Options • A Checklist: Population Health Management OrganizationalIssues • ACO Data Acquisition Timeline • Data Governance Time Allowing: Health Catalyst Screen Shots
  3. 3. © 2013 Health Catalyst Evolution of Data Management Every industry follows the same pattern 3 Data Collection1 2 Data Sharing 3 Data Analysis Billing, Radiology and Lab systems; EMRs, etc. Local Area Networks, Health Information Exchanges Enterprise Data Warehouses (EDW)
  4. 4. © 2013 Health Catalyst Who or what are we monitoring? What are our goals? What are we measuring? How will we achieve them? Data and Technology Organization and Culture Regularly return to these fundamentals The Four Questions of Analytics
  5. 5. © 2013 Health Catalyst 55 Atomic data must be “bound” to business rules about that data and to vocabularies related to that data in order to create information Vocabulary binding in healthcare is pretty obvious ● Unique patient and provider identifiers ● Standard facility, department, and revenue center codes ● Standard definitions for gender, race, ethnicity ● ICD, CPT, SNOMED, LOINC, RxNorm, RADLEX, etc. Examples of binding data to business rules ● Length of stay ● Patient relationship attribution to a provider ● Revenue (or expense) allocation and projections to a department ● Revenue (or expense) allocation and projections to a physician ● Data definitions of general disease states and patient registries ● Patient exclusion criteria from disease/population management ● Patient admission/discharge/transfer rules Binding Data to Create Information
  6. 6. © 2013 Health Catalyst Data Binding Vocabulary “systolic & diastolic blood pressure” Rules “normal” Pieces of meaningless data 115 60 Binds data to Software Programming
  7. 7. © 2013 Health Catalyst Why Is This Concept Important? Two tests for tight, early binding 7 Knowing when to bind data, and how tightly, to vocabularies and rules is THE KEY to analytic success and agility Is the rule or vocabulary widely accepted as true and accurate in the organization or industry? Comprehensive Agreement Is the rule or vocabulary stable and rarely change? Persistent Agreement Acknowledgements to Mark Beyer of Gartner
  8. 8. © 2013 Health Catalyst ACADEMIC STATE SOURCE DATA CONTENT SOURCE SYSTEM ANALYTICS CUSTOMIZED DATA MARTS DATA ANALYSIS OTHERS HR FINANCIAL CLINICAL SUPPLIES INTERNALEXTERNAL ACADEMIC STATE OTHERS HR FINANCIAL CLINICAL SUPPLIES RESEASRCH REGISTRIES QlikView Microsoft Access/ ODBC Web applications Excel SAS, SPSS Et al OPERATIONAL EVENTS CLINICAL EVENTS COMPLIANCE AND PAYER MEASURES DISEASE REGISTRIES MATERIALS MANAGEMENT 3 Data Rules and Vocabulary Binding Points High Comprehension & Persistence of vocabulary & business rules? => Early binding Low Comprehension and Persistence of vocabulary or business rules? => Late binding Six Points to Bind Data in an Analytic System 421 5 6
  9. 9. © 2013 Health Catalyst Words of Caution 9 Healthcare suffers from a low degree of Comprehensive and Persistent agreement on many topics that impact analytics The vast majority of vendors and home grown data warehouses bind to rules and vocabulary too early and too tightly, in comprehensive enterprise data models Analytic agility and adaptability suffers greatly • “We’ve been building our EDW for two years.” • “I asked for that report last month.”
  10. 10. Closed Loop Analytics: The Triple Aim 10 Google: “Intermountain antibiotic assistant”
  11. 11. © 2013 Health Catalyst Center of the Universe Shifts 11 EMR Analytics & EDW Analytics & EDW EMR CopernicusPtolemy
  12. 12. © 2013 Health Catalyst Healthcare Analytic Adoption Model Level 8 Cost per Unit of Health Reimbursement & Prescriptive Analytics Contracting for & managing health. Customizing patient care based on population outcomes. Level 7 Cost per Capita Reimbursement & Predictive Analytics Diagnosis-based financial reimbursement, managing risk proactively, measuring true outcomes Level 6 Cost per Case Reimbursement & The Triple Aim Procedure-based financial risk and applying “closed loop” analytics at the point of care Level 5 Clinical Effectiveness & Population Management Measuring & managing evidence based care Level 4 Automated External Reporting Efficient, consistent production; agility, and governance Level 3 Automated Internal Reporting Efficient, consistent production; widespread access to KPIs Level 2 Standardized Vocabulary & Patient Registries Relating and organizing the core data Level 1 Integrated, Enterprise Data Warehouse Foundation of data and technology Level 0 Fragmented Point Solutions Inefficient, inconsistent versions of the truth
  13. 13. Level 8 Cost per Unit of Health Reimbursement & Prescriptive Analytics: Providers Analytic motive expands to wellness management and mass customization of care. Physicians, hospitals, employers, payers and members/patients collaborate to share risk and reward (e.g., financial reward to patients for healthy behavior). Analytics expands to include NLP of text, prescriptive analytics, and interventional decision support. Prescriptive analytics are available at the point of care to improve patient specific outcomes based upon population outcomes. Data content expands to include genomic and familial information. The EDW is updated within a few minutes of changes in the source systems. Level 7 Cost per Capita Reimbursement & Predictive Analytics: Analytic motive expands to address diagnosis-based, fixed-fee per capita reimbursement models. Focus expands from management of cases to collaboration with clinician and payer partners to manage episodes of care, using predictive modeling, forecasting, and risk stratification to support outreach, triage, escalation and referrals. Patients are flagged in registries who are unable or will not participate in care protocols. Data content expands to include external pharmacy data and protocol-specific patient reported outcomes. On average, the EDW is updated within one hour or less of source system changes. Level 6 Cost per Case Reimbursement & The Triple Aim: The “accountable care organization” shares in the financial risk and reward that is tied to clinical outcomes. At least 50% of acute care cases are managed under bundled payments. Analytics are available at the point of care to support the Triple Aim of maximizing the quality of individual patient care, population management, and the economics of care. Data content expands to include bedside devices and detailed activity based costing. Data governance plays a major role in the accuracy of metrics supporting quality-based compensation plans for clinicians and executives. On average, the EDW is updated within one day of source system changes. The EDW reports organizationally to a C-level executive who is accountable for balancing cost of care and quality of care. Level 5 Clinical Effectiveness & Population Management: Analytic motive is focused on measuring clinical effectiveness that maximizes quality and minimizes waste and variability. Data governance expands to support care management teams that are focused on improving the health of patient populations. Permanent multidisciplinary teams are in-place that continuously monitor opportunities to improve quality, and reduce risk and cost, across acute care processes, chronic diseases, patient safety scenarios, and internal workflows. Precision of registries is improved by including data from lab, pharmacy, and clinical observations in the definition of the patient cohorts. EDW content is organized into evidence-based, standardized data marts that combine clinical and cost data associated with patient registries. Data content expands to include insurance claims. On average, the EDW is updated within one week of source system changes. Level 4 Automated External Reporting: Analytic motive is focused on consistent, efficient production of reports required for regulatory and accreditation requirements (e.g. CMS, Joint Commission, tumor registry, communicable diseases); payer incentives (e.g. MU, PQRS, VBP, readmission reduction); and specialty society databases (e.g. STS,NRMI, Vermont-Oxford). Adherence to industry-standard vocabularies is required. Clinical text data content is available for simple key word searches. Centralized data governance exists for review and approval of externally released data. Level 3 Automated Internal Reporting: Analytic motive is focused on consistent, efficient production of reports supporting basic management and operation of the healthcare organization. Key performance indicators are easily accessible from the executive level to the front-line manager. Corporate and business unit data analysts meet regularly to collaborate and steer the EDW. Data governance expands to raise the data literacy of the organization and develop a data acquisition strategy for Levels 4 and above. Level 2 Standardized Vocabulary & Patient Registries: Master vocabulary and reference data identified and standardized across disparate source system content in the data warehouse. Naming, definition, and data types are consistent with local standards. Patient registries are defined solely on ICD billing data. Data governance forms around the definition and evolution of patient registries and master data management. Level 1 Integrated, Enterprise Data Warehouse: At a minimum, the following data are co-located in a single data warehouse, locally or hosted: HIMSS EMR Stage 3 data, Revenue Cycle, Financial, Costing, Supply Chain, and Patient Experience. Searchable metadata repository is available across the enterprise. Data content includes insurance claims, if possible. Data warehouse is updated within one month of changes in the source system. Data governance is forming around the data quality of source systems. The EDW reports organizationally to the CIO. Level 0 Fragmented Point Solutions: Vendor-based and internally developed applications are used to address specific analytic needs as they arise. The fragmented Point Solutions are neither co-located in a data warehouse nor otherwise architecturally integrated with one another. Overlapping data content leads to multiple versions of analytic truth. Reports are labor intensive and inconsistent. Data governance is non-existent. © Healthcare Analytic Adoption Model
  14. 14. © 2013 Health Catalyst The ACO Data Acquisition Checklist 14 Billing data1 Lab data2 Imaging data3 Inpatient EMR data4 Outpatient EMR data5 Claims Data6 HIE Data7 Detailed cost accounting8 Bedside monitoring data9 External pharmacy data10 Familial data11 Home monitoring data12 Patient reported outcomes data13 Long term care facility data14 Genomic data15 Real-time 7x24 biometric monitoring for all patients in the ACO16 NOW1-2YEARS2-4YEARS
  15. 15. © 2013 Health Catalyst © 2013 Health Catalyst Evaluating Options 15
  16. 16. 16 Strategy and Analytic Options Strategy Option Pros & Cons Example Vendors Buy & Build from an Analytics Platform Vendor  Highest degree of analytic flexibility and adaptability  Requires a data driven culture with high aspirations that views analytics as a clear business differentiator  Best suited for a culture with a higher degree of data literacy and data management skills  Slow initial time-to-value plagues some vendors  Inconsistent ROI track record, but when ROI occurs, it’s big  Caradigm Intelligence Platform  Health Catalyst  Healthcare Data Works  IBM Healthcare Data Model  Oracle Healthcare Data Model  Recombinant (Deloitte) Buy from an Analytics Service Provider  Best suited for cultures that want to avoid the details of analytics and data management, but aspire to improve basic internal and external reporting  Inter-organizational benchmarking and comparative analytics is a natural part of the business model and service  Limited analytic flexibility and adaptability  Substantive ROI is not well-documented nor widely acknowledged  Explorys  Humedica  Lumeris  Premier Alliance  Truven Analytics Suite Buy “Best of Breed” Point Solutions  Leverages expertise and very specific analytics applications in business and clinical areas that are not always available in other options  Does not facilitate data integration; i.e., does not provide a single analytic perspective on patient care and costs  Costly and complicated to maintain  AltaSoft  Crimson Suite  EPSI  MedeAnalytics  Medventive  Midas  Omincell Buy from your EMR Vendor  Offers the possibility of “closed loop analytics” driving analytics back to the point of care, in the EMR and clinical workflow  No proven track record with analytics to date from the EMR vendors  Tend to be very focused on analytics that are specific to the EMR vendor’s data  Less flexible and adaptable to new sources of data and analytic use cases, especially complex ones  Allscripts Sunrise  Cerner PowerInsight  Epic Clarity & Cogito  McKesson Horizon  Meditech Data Repository  Siemens Decision Support
  17. 17. Population Health Management A Checklist for Requirements and Functionality 1. Evidence based definitions of patients to include in the PHM registries 2. Clinician-patient attribution algorithms 3. Discrete, evidence based methods for flagging patients in the registries that are difficult to manage in the protocol, or should be excluded from the registry, altogether 4. Evidence based triage and clinical protocols for single disease states 5. Evidence based triage and clinical protocols for comorbid patients 6. Metrics and monitoring of clinical effectiveness and total cost of care (to the system and the patient) 7. Risk stratified work queues for outreach that feed care management teams and processes 8. Access to test results and medication compliance data outside the core healthcare delivery organization 9. Patient engagement and communication system about their care, including coordination of benefits 10. Patient education material and a distribution system, tailored to their status and protocol 11. Patient reported outcomes measurement system, tailored to their status and protocol 12. Inter-physician/clinician communication system about overlapping patients
  18. 18. © 2013 Health Catalyst Data Governance Govern to the least extent required for the common good Base your committee charter on… 18 Encouraging more, not less, data access Increasing data content in the EDW Campaigning for data literacy Resolving analytic priorities Enhancing data quality Establishing standards for Master Reference Data
  19. 19. © 2013 Health Catalyst In Summary • Analytics is the R in the ROI of IT investments • This is a new chance for healthcare to do the right thing, the first time, with IT strategy and investments • Unlike the bumpy road of EMR adoption • C-level executives need to be highly literate in data management • You can’t delegate all the details. The consequences to the business are too significant. • All industries now move at the speed of software and analytics • Test for Comprehensive and Persistent understanding • Follow the curriculum of the Analytic Adoption Model • Be deliberate and stick to the fundamentals 19
  20. 20. © 2013 Health Catalyst Thank You! • Questions? • Contact information • • • @drsanders • 20
  21. 21. © 2013 Health Catalyst © 2013 Health Catalyst Catalyst Screen Shots 21
  22. 22. © 2013 Health Catalyst Key Process Analysis Identifying Variability Opportunities For Waste Reduction and Quality Improvement
  23. 23. © 2013 Health Catalyst
  24. 24. © 2013 Health Catalyst
  25. 25. © 2013 Health Catalyst
  26. 26. © 2013 Health Catalyst
  27. 27. © 2013 Health Catalyst Hospital and Clinical Operations
  28. 28. © 2013 Health Catalyst
  29. 29. © 2013 Health Catalyst
  30. 30. © 2013 Health Catalyst
  31. 31. © 2013 Health Catalyst
  32. 32. © 2013 Health Catalyst
  33. 33. © 2013 Health Catalyst
  34. 34. © 2013 Health Catalyst Executive Dashboard
  35. 35. © 2013 Health Catalyst
  36. 36. © 2013 Health Catalyst Population Explorer On The Path To Population Health Management
  37. 37. © 2013 Health Catalyst
  38. 38. © 2013 Health Catalyst
  39. 39. © 2013 Health Catalyst
  40. 40. © 2013 Health Catalyst Population Health Management
  41. 41. © 2013 Health Catalyst
  42. 42. © 2013 Health Catalyst
  43. 43. © 2013 Health Catalyst The Age of Analytics Taking the next step… • Download the Late-Binding Data Warehouse White Paper • Download the Analytics Adoption Model White Paper • Contact us to learn more about our solutions and communication tools 43