The Future of Analytics in HealthCare: A Significant Evolution

Transcription

1 The Future of Analytics in HealthCare: A Significant Evolution John Glaser, PhD, CEO, Health Services Siemens Healthcare DISCLAIMER: The views and opinions expressed in this presentation are those of the author and do not necessarily represent official policy or position of HIMSS.

2 Conflict of Interest Disclosure John Glaser, PhD Has no real or apparent conflicts of interest to report HIMSS

3 Learning Objectives Recognize the diversity of analytics in healthcare Describe the future direction of BI/analytics over the next four to five years Explain the factors that will shape that direction

4 Analytics has become critical to all industries; health care is no different

5 Analytics in Health Care Is Not New Information contained on this slide is preliminary and describes change to product features that are currently under development. Siemens makes no assurances that the capability described herein will be provided in the product when it is released for general availability.

6 Analytics in Health Care Will Be Altered by Several Intertwined Major Forces Changes in provider reimbursement mechanisms and associated business and clinical models Continued adoption of interoperable electronic health records Maturation of organizational analytics intent Shift in the nature of analytics Advances in information technology and methods

7 Shared Savings Plans Bundled Payments Never Events Non Payment Readmission Penalty Preventable Condition Major Changes in Reimbursement Sweeping the US ACO Value Based Purchasing Global Payment Direct Employer Contracting Medical Home Capitation Healthcare Reform New Payment Models

8 Shifts in Provider Business and Clinical Models

9 Analytics Will Be Necessary to Manage Patient and Population Care

10 Determine Variation from Plan: Readmissions Dashboard

11 Trends in Hospital EHR Adoption Show Increasing Adoption but Low Penetration of Full Function

12 Maturation of Organizational Analytics Intent

13 Concurrent Quality Intelligence: Cohort Monitoring

14 Predicting a Patient s Course Workflow Quality Initiatives Risk Factors & Outcomes Actions Aggregation Date: 08 / 23 / 2008 Time: 13:45:30 EST QI: Stroke LDL INR Rick Factors History of Stroke History of Cancer History of Bleeding Expected LOS Outcomes Expected Costs Re-admission Risk Patient Yes No Yes 8 Days 75K 42% Patient Yes Yes Yes 12 Days 110K 78% Patient No No No 3 Days 15K 10%

15 Managing a Complex Process (MRSA Patient) Hess, The Missing Link to Success, JHIM, 2009

16 Shift in the Nature of Analytics Management Structure Past / Present Hierarchical and consolidated Future Distributed Decision Tools Trending / benchmarking Prediction and prevention Decision Timeframes Periodic, over time Near / real-time Data States Structured but limited Variously structured and expansive Extraction / Aggregation Disciplined Anarchical Data Needs Routine Unpredictable Analytical Environment Analyst at their desk Care provider on the go

17 Machine Reconciliation of Data Inconsistencies

18 Who Is this Person and What Are They Trying to Do?

19 We re Entering a New Era in Information Technology Networked, powerful processors almost everywhere and on almost anything Diverse array of sensitive and specific sensors Massive amounts of data and novel methods for analyzing it Software delivered as a service A wide variety of collaboration, community and knowledge resources This era will enable us to: Use large data volumes to perform real world analysis and experiments Orchestrate complex processes Deliver new services, e.g., location aware and location invariant services Extend and enrich fundamental human activities such as being a member of a community and searching for information

20 Tailoring Cancer Therapy Patient Observations Computational Models Individualized Treatment Cell Level Tissue Level Organ Level Molecular Level Imaging and Sensing Multi-parametric MRI PET/CT Spectroscopy, CEUS Histopathology H&E stained, AMACR, CK903 Circulating Tumor Cells Phase Contrast SNP, mrna, Proteomics Modeling and Estimation Shape and Appearance Tissue Biomechanics (Elastography, Fibrosis) Cell/Tissue Self-Organization Molecular Networks Apoptosis Decision Making, Therapy Selection and Optimization Biologically Guided Radiotherapy Ablative Therapy Chemo Therapy Immuno and Gene Therapy

21 Comparison of Relative Risk of Medications Using EHR Data Source: Brownstein J, Murphy S, Goldfine A, Grant R, Sordo M, Gainer V, Colecchi J, Dubey A, Nathan, D, Glaser J, Kohane I. Rapid identification of myocardial infarction risk associated with diabetic medications using electronic medical records. Diabetes Care 2010;33(3):

22 The Quantified Self

23 HIT Infrastructure and Sophisticated Clinical Decision Aids Fuse Systems Medicine Content: Clinical understanding Outcome orientation Multiple Diseases Disease Procedur e Infrastructure: Data & process integration Episode Multiple Episodes Longitudinal Care

24 The EHR Emphasis Will Shift From Transaction Support to Include Intelligence Support Guide clinical diagnostic and therapeutic decisions Ensure sequence of care activities conform to the evidence and performance contract requirements Monitor the execution of core clinical processes Capture, report and integrate into EHRs quality and performance measures Expand the scope of clinical data Support the interactions of the care team

25 The Evolution of Analytics Will Occur across Multiple Dimensions Retrospective measurement of operations shifts to real time analysis of the quality and efficiency of an organization s care delivery and the health of populations Expansion of the breadth and depth of clinical data to include data from multiple sources Progression of organizational intent from reaction to optimization Shift in the nature of analytics from narrow, structured data to messy data and a focus on complex patterns Leverage of the advances of the fifth information technology revolution The blurring of the distinction between analytics and transactionoriented systems

26 Why Not the Best? Results from the 2011 National Scorecard on U.S. Health System Performance Across 42 performance indicators, the U.S. achieves a total score of 64 out of a possible 100, when comparing national rates with domestic and international benchmarks. Costs were up sharply, access to care deteriorated, health system efficiency remained low, disparities persisted, and health outcomes failed to keep pace with benchmarks. Variations in health care delivery, moreover, persist throughout the U.S., as opportunities are routinely missed to prevent disease, disability, hospitalization, and mortality. The Commonwealth Fund Commission on a High Performance Health System, Oct. 2011

27 And, This Is Why It Really Matters

28 Conflict of Interest Disclosure John Glaser, PhD CEO, Health Services, Siemens Healthcare 2013 HIMSS

29 Please use blank slide if more space is required for charts, graphs, etc. To remove background graphics, right click on selected slide, choose Format Background and check Hide background graphics.