1. Renaissance Computing Institute (RENCI)
VisualDecisionLinc A Comparative Effectiveness Approach To Advance Decision Support in Psychiatry
Ketan K. Mane
Renaissance Computing Institute (RENCI)
14th March 2011

2. Members RENCI Duke University Kenneth Gersing (Psychiatry Dept.)
Ketan K. Mane Charles Schmitt
Chris Bizon
Phil Owen
Duke University Kenneth 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
US healthcare - Most advanced … but cost paid is too high … numbers
Moore’s Law
Informatics perspective on the factor we can take care of …. that contribute to the cost of health care involve –
Ineffective initial treatment … the amt of titration phase involved …. To get to the dosage that would work is trial and error
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
EMR data – treatment and outcomes related data
Embedded knowledge about … what works for what patients and under what circumstances
Essentially …. The evidence to practice evidence-based medicine
EHRs data is likely to improve quality, if the evidence is available at the point of care.
Health IT provides access – but data not in readily interpretable format ….Tabular format
Large data results and basic result presentation formats, the complexity of data interpretation also increases with data size.
Volume of data – 5 min Dr. Time - Information overload – Need for external aid to make sense of data
Big constraints in EMR data usage

5. Similar based on medical profile
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
Promising approach to use of EMR data …. Is CER
CER – offers to build treatment options with patient centric view
EMR data available – but Therapeutic choices are widely unclear (What works best for whom, when, and in what circumstances)

6. Comparative Effectiveness for Decision Support
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)
CER as DS .. Aligns with the Evidence-based Medicine principle … way to bridge gap …. And help identify right treatment to the right patient at the right time
CER – data driven approach

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
Clinician’s use clinical protocols/guidelines for decision support.
Protocols encapsulates the best treatment related evidence for patients in different stages in their treatment.
Build by experts – with informed knowledge gathered from clinical trials
Non-adherence …
These guidelines are built from clinical trials by using controlled population in a closely monitored clinical setting.
bcos in the real world setting, there exists very little overlap in the conditions that were used in the clinical trials to build the protocols.
Not updated
Lack of specificity
But guidelines do incorporate the best of the decision point … an capture the expert view …. So can we leverage of that information
And use it in conjunction with EMR data which defines the real world population …. To gather insight
Use of EMR data as supplement information with guideline – offers potential to use data to create personalized treatment profile plan 7

8. Clinical Guidelines and CER Approach
What works and what doesn't?
‘Clinical Trials’ – determine comparative effectiveness
Clinical trials
Next best thing? – EMR data
Expensive
Cheap
Slow
Fast
Controlled population
Real-world population
Current Setup
Data Collection
Clinical Care
/EMR
Warehouse
So … based on the previous slides, we know that …. CER based data driven approach and protocol based
approach offer insight into what works and what doesn’t
Traditionally, we know … clinical trials are the way to determine CER Alternatively, we can use EMR data … to passively collect the information …. And use it with the protocols
Current setup – no knowledge readily available at the point of care….
Thus overview of the motivation is –
How can we make meaningful use of the massive amt of EMR patient related data And present it to the clinicians in a format that is meaningful, Or facilitates their decision making process … at the point of care…
What is the best way to understand the CER … and use it to identify what works and what doesn’t
How can one best leverage of the decision points available in protocols/clinical guidelines …
Research
Knowledge 8

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)
All Patients (N = )
Demographics
Primary Diagnosis
Child
14809
Additional
9582
Adolescent
13804
Adjustment
11114
Adult
70028
Anxiety
10427
Senior
11294
Bipolar
9189
Childhood
10484
Cognitive
8881
Gender
Depression
20462
Male
50217
Dissociative 54
Female
59163
Eating
1452
Factitious 26
Race
GMC
223
Black
19714
Impulse Control
1314
White
44923
Mood
6038
Other
12115
1856
Race unknown
33250
Personality
791
Psychotic
5511
Schizophrenia
3150
Sexual
130
Sleep
704
Somatoform
494
Substance
9649
Table 1: Characteristics of patients in MindLinc

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
The emerging field of Visual Analytics is focusing on combining related research areas such as
visualization, data analytics to handle large and heterogeneous volumes of data, such as
EHR.
Visual Analytic Approach – away to address the above needs, and to facilitate the decision making capability at the point of care, dynamic in nature 11

12. VisualDecisionLinc: Dashboard for Clinical Decision Support1 4 2 3 5 6 7 8 1
Patient demographics 3
Comorbid conditions 5
Patient Treatment Response 71
Prescribed Rx info 2
Response to Rx 4
Guideline view 6
Projected Response to Rx 8
Patient visit type info

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
So far …. We covered how the CER can be presented to the user … for them to interact with it … and narrow it down to the comp population
Of their interest … to make facilitate their decision making process …
Now, onto the guideline piece …to use the guideline as template to view patient information

16. Clinical Guideline – Prior Approach
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
XML representation of guidelines

17. Clinical Guideline – Our Approach

18. Clinical Guideline – Our Approach
emphasize heavily at this point that we are looking at various approaches and would welcome feedback and thoughts on best approaches.

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