1. Decision Support Capability Breast Cancer Scenarios
Class: DL – Decision Support Systems and Health Care
Final Project: Breast Cancer Decision Support Capability Scenario
Group: Elizabeth Acord, Brian Frazior and Theresa Veith

2. Introduction Breast Cancer Statistics Devastating dianosis
191,410 women were diagnosed with breast cancer.
40,820 women died from breast cancer.
Source: U.S. Cancer Statistics Working Group. United States Cancer Statistics: 1999–2006 Incidence and Mortality Web-based Report. Atlanta (GA): Department of Health and Human Services, Centers for Disease Control and Prevention, and National Cancer Institute; Available at:

3. Introduction Existing Decision Support Systems ONCOCIN
The Kasimir Project
Comprehensive Health Enhancement Support System (CHESS)
Integrated information
Referral
Decision and social support programs

4. The System Intuition Clinical Decision System
Targets Difficulties and Shortcomings
Integration with Oncology Management System
Clinical Objectives of Stakeholders

5. Clinical Objectives Prevention of Errors Optimize Decision Making
Reduce mistreatments
Correct diagnostic testing
Proper data collection
Optimize Decision Making
Adherence to breast cancer clinical guidelines
Patient participation in treatment decision
Aid oncologists on breast cancer protocols
Customized workflows
Improve Care Processes
Increase patient knowledge and understanding
Promote patient-physician communication
Greater access to medical information

6. Intuition CDS Model
Knowledge-based paradigm targeting clinical objectives
Integrated into the existing Intuition Oncology Management System
Accessed by both clinicians and patients
Treatment protocols may be based on national standards or customized departmental guidelines
System workflow is configurable for improved integration into current departmental clinical workflows
Based on PROACTIVE approach for clinical decision making
Utilizes classification decision tree algorithm
Data used in the classification algorithm include:
Diagnosis
TMN staging
Patient treatment preferences
Treatment history
Risk factors

7. System Component Diagram
This diagram shows the main components involved with the decision support system.

8. Knowledge Engineering
Acquisition
Data is captured by the oncology management system through manual data entry and interfaces to external systems
HL7 version 3
SNOMED CT
Intuition CDS acquires patient treatment preferences
Representation
Clinical Data: Structured Data from Database Management System
Clinical Guideline Model:
Leverage SAGE and KON research projects
Context
Action
Decision
Selection & Maintenance
Clinical decision is based on conditions met in the clinical guideline contexts and patient treatment preferences
NCCN clinical guidelines are maintained by a guideline interface using HL7 version 3 and GELLO

9. System Workflow
This diagram depicts the inbound data interaction of the system components when processing a breast cancer treatment decision

10. Lobular Carcinoma In Situ (LCIS) Treatment Decision Algorithm

11. Lobular Carcinoma In Situ (LCIS) Treatment Decision Algorithm

12. Patient and Clinician User Interfaces

13. Evaluation
How the system will help users evaluate their own processes?
Identify (& share) best practices
Track outcomes data
Can be used to identify trends in patient behaviors
Identify where additional education may be needed based on system use
How is the system evaluated?
Turn Around Times for providing a treatment plan
Physician Satisfaction (UAT)
Patient Satisfaction (UAT)
Alignment with the HIMSS Framework
Testing Effective Deployment
Scorecard

14. Discussion Limitations Assumptions Future Extensions
Continual updating of NCCN guidelines
Interoperability with other systems
Clinical trials
Assumptions
User acceptance
Computer literacy
Future Extensions
Increase guideline knowledge base
Support other cancer types

15. Questions?