visualdecisionlinc a comparative effectiveness approach to advance decision support in psychiatry...

VisualDecisionLincA Comparative Effectiveness Approach To Advance

Decision Support in Psychiatry

Ketan K. ManeRenaissance Computing Institute (RENCI)

kmane@renci.org

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

1

2 3

4

56

7

8

1

2

Patient demographics

Response to Rx

3

4

Comorbid conditions

Guideline view

5

6

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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34

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