Ketan K. Mane Renaissance Computing Institute (RENCI ) kmane@renci 14 th March 2011

1. VisualDecisionLincA Comparative Effectiveness Approach To Advance Decision Support in Psychiatry Ketan K. Mane Renaissance Computing Institute (RENCI) kmane@renci.org 14th March 2011

2. Members RENCI Ketan K. Mane Charles Schmitt Chris Bizon Phil Owen Duke UniversityKenneth Gersing (Psychiatry Dept.) Bruce Burchett Ricardo Pietrobon

3. Healthcare Cost - Overview • US health delivery cost • Current: ~$8,000 per capita, ~$2.4 trillion (16.4% of GDP) • 2015 Projection: ~$11,000 per capita, ~18.4 of GDP • Psychiatric study for year 2000 reveals –16% of MDD patients in US population  Cost ~$84 billion • Factors contributing to healthcare cost include – • Ineffective initial treatment (dose iteration) • Medication error • Adverse events due to medication switching Or Relapse Strong consensus among experts exist that decision support tools that aid clinicians decision making process hold tremendous potential to improve clinical care and reduce cost

4. Electronic Medical Record Systems (EMRs) • EMRs – store massive amount of patient data including treatment and outcomes • Stored data offers great potential to improve quality and care through evidence based medicine approach Ability to determine best treatment options for patient at the point of care is a critical component of patient quality care • Optimal treatment strategies strained by – • Reduced clinician time per patient • Information overload - search for data of interest takes time Big constraints in EMR data usage

5. Comparative Effectiveness Research (CER) • Comparative Effectiveness Research Goal Approaches to help identify best treatment choices for the patient • EMR data: Patient Diagnosis + Treatment + Outcomes EMR Patient Similar based on medical profile

6. Comparative Effectiveness Research (CER) • Comparative Effectiveness Research Goal Approaches to help identify best treatment choices for the patient • EMR data: Patient Diagnosis + Treatment + Outcomes Advantages of Comparative Effectiveness Research Approach • Personalized Medicine - patient’s medical profile based treatment • Speed treatment delivery at the point of care • Help investigate effects at the sub-group levels (e.g. the elderly, racial and ethnic minorities) • Accelerate translation of new discoveries into practice for better outcomes Comparative Effectiveness for Decision Support (offers potential to bridge the gap between evidence and clinical practice)

7. Clinical Guidelines • Define treatment plan to be followed by clinicians • Formed by expert committees (informed through clinical trials) • Non-adherence to guidelines among • clinicians Use of EMR data as supplement information with guideline – offers potential to use data to create personalized treatment profile plan

8. Clinical Guidelines and CER Approach • What works and what doesn't? • ‘Clinical Trials’– determine comparative effectiveness Current Setup Data Collection Clinical Care /EMR Warehouse Research Knowledge

9. MindLinc: EMR • Largest de-identified psychiatry outcome data warehouse(110,000 patients or 2,400,000 clinical encounters over a 10 year span) • Widely distributed across 25 US institutions • academic institutions (25%), • community mental health centers (50%) • private practice, hospitals, other combined (25%) • Sample data for initial analysis: • ~30,000 visits of patients with • Major Depressive Disorder (MDD)

10. Analytics • Identify set of attributes that are clinically relevant to define the comparative population • Approach would help define attributes that – • Makes patients similar to one another • Help extract meaningful patient’s features (if any) to determine treatments • Identify statistically important attributes that define differences in outcomes

11. Requirements of a Decision Support Tool • Part of the workflow - quick and easy to use • Helps reduce information overload • Provides a good overview of evidence (comparative population) • Support clinician’s decision making process • Interactive and provides clinician with control to filter data based on their needs • Provides additional insights Visual Analytic Approach – away to address the above needs, and to facilitate the decision making capability at the point of care, dynamic in nature

12. VisualDecisionLinc: Dashboard for Clinical Decision Support 1 4 2 3 5 6 7 8 Patient demographics Comorbid conditions Patient Treatment Response Prescribed Rx info 1 3 5 71 Response to Rx Guideline view Projected Response to Rx 2 Patient visit type info 4 6 8

13. VisualDecisionLinc – at the Point of Care • Outbound • EMR – Codified • De-identification of Local Data • Interface to Centralized Warehouse • Centralized Data Warehouse • Data Analysis – Statistician • Expert Consensus • Data Warehouse + Clinical Trials • Inbound • Codification of Rules for export • Interface - Transfer rules to local systems • Decision Support • Patient Profile + Business Rules • Contextual Presented at point of decision • Visualization of Data

14. Information Flow in VisualDecisionLinc

15. Clinical Guideline

16. Clinical Guideline – Prior Approach XML representation of guidelines • Guideline Element Model (GEM) • GLIDES • Guidelines – scope restricted to recommendations (alerts, reminders on screening, etc.) • SEBASTIAN system from Duke University – leading the effort to define the national standards toward HL-7 in decision support

17. Clinical Guideline – Our Approach

18. Clinical Guideline – Our Approach

19. VisualDecisionLinc - Next Steps • Integrate it with the MindLinc EMR • Incremental deployment to get feedback from clinicians • Explore alternate approaches to map patient data to clinical guidelines/protocols. • UI level - effectiveness study (NSF proposal submitted with Dr. Javed Mostafa) • Explore potential other domain where can apply this approach where dataset is readily available

20. Summary of the Talk • Decision Support Space • Changing focus of Health IT – to make sense from EMR data • Comparative Effectiveness Research Approach – offers potential to bridge the gap between evidence and clinical practice • VisualDecisionLinc: Visual Analytics + CER approach • Novel way to look at patient data and the comparative data at the same time • Interactive Dashboard – ad hoc define and customize comparative population • Clinical Guideline - New approach to view patient data in the context of the clinical guideline Visual Analytics for Decision Support Approach has the potential to serve as a template that can be extended to other medical conditions

21. Questions