decision support capability breast cancer scenarios class: 406-dl – decision support systems and...

Decision Support Capability Breast Cancer Scenarios

Class: 406-DL – Decision Support Systems and Health CareFinal Project: Breast Cancer Decision Support Capability ScenarioGroup: Elizabeth Acord, Brian Frazior and Theresa Veith

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; 2010. Available at: http://www.cdc.gov/uscs.

Introduction

Existing Decision Support Systems• ONCOCIN• The Kasimir Project• Comprehensive Health

Enhancement Support System (CHESS)– Integrated information– Referral– Decision and social support programs

The System

Intuition Clinical Decision System• Targets Difficulties and Shortcomings• Integration with Oncology Management System• Clinical Objectives of Stakeholders

Clinical Objectives

• Prevention of Errors– 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

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

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

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

System Workflow• This diagram depicts the inbound data interaction of the system

components when processing a breast cancer treatment decision

Lobular Carcinoma In Situ (LCIS) Treatment Decision Algorithm

LCIS

Tamoxifen

Risk Reduction

Pre-menopausal

Tamoxifen or Raloxifene

Post-menopausal

Stage Defaults to Zero and All other diagnostic tests are null/Not Applicable

Bilateral Mastectomy

Lumpectomy

Pre

Post

Bilateral Mastectomy

Post

Lumpectomy

Lobular Carcinoma In Situ (LCIS) Treatment Decision Algorithm

Patient and Clinician User Interfaces

EvaluationHow 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

Discussion

• Limitations– 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

Questions?