visualdecisionlinc a comparative effectiveness approach to advance decision support in psychiatry...
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VisualDecisionLincA Comparative Effectiveness Approach To Advance
Decision Support in Psychiatry
Ketan K. ManeRenaissance Computing Institute (RENCI)
14th March 2011
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RENCI
Ketan K. Mane
Charles Schmitt
Chris Bizon
Phil Owen
Members
Duke University
Kenneth Gersing (Psychiatry Dept.)
Bruce Burchett
Ricardo Pietrobon
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
Healthcare Cost - Overview
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
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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
Comparative Effectiveness Research Goal Approaches to help identify best treatment choices for the patient
EMR data: Patient Diagnosis + Treatment + Outcomes
Comparative Effectiveness Research (CER)
EMR
Patient
Similar based on medical profile
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 Research (CER)
Comparative Effectiveness for Decision Support (offers potential to bridge the gap between evidence and clinical practice)
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Define treatment plan to be followed by clinicians Formed by expert committees (informed through clinical trials) Non-adherence to guidelines among
clinicians
Clinical Guidelines
Use of EMR data as supplement information with guideline – offers potential to use data to create personalized treatment profile plan
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Clinical Guidelines and CER Approach o What works and what doesn't?o ‘Clinical Trials’ – determine comparative effectiveness
Clinical trials Next best thing? – EMR data
Expensive Cheap
Slow Fast
Controlled population Real-world population
Clinical Care /EMR
Warehouse
Knowledge
Data Collection
Research
Current Setup
MindLinc: EMRAll Patients (N = 110002)
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 Other 1856Race
unknown 33250 Personality 791
Psychotic 5511
Schizophrenia 3150
Sexual 130
Sleep 704
Somatoform 494
Substance 9649
Table 1: Characteristics of patients in MindLinc
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)
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
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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
VisualDecisionLinc: Dashboard for Clinical Decision Support
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2 3
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Patient demographics
Response to Rx
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Comorbid conditions
Guideline view
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Patient Treatment Response
Projected Response to Rx
718
Prescribed Rx info
Patient visit type info
Local Clinical Repository
Centralized Data Warehouse
Pa
tien
t Pro
fileD
ata
Tra
nsp
ort
Decision Support Engine
Bu
sin
ess
Ru
les
Electronic Medical Record
Interface Engine
Guidelines
Data Analysis
De-identified Data
Expert Consensus
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5
Firewall
Phase 3Phase 2Phase 1
3X
2X
1X
Outcomes Data
Inbound Outbound Outbound
o EMR – Codifiedo De-identification of Local Datao Interface to Centralized Warehouse o Centralized Data Warehouseo Data Analysis – Statisticiano Expert Consensuso Data Warehouse + Clinical Trials
Inboundo Codification of Rules for exporto Interface - Transfer rules to local systemso Decision Support o Patient Profile + Business Ruleso Contextual Presented at point of decisiono Visualization of Data
VisualDecisionLinc – at the Point of Care
Information Flow in VisualDecisionLinc
Clinical Guideline
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
Clinical Guideline – Prior Approach
XML representation of guidelines
Clinical Guideline – Our Approach
Clinical Guideline – Our Approach
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
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
Summary of the Talk
Visual Analytics for Decision Support Approach has the potential to serve as a template that can be extended to other medical conditions
Questions