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Decision Support Capability Breast Cancer Scenarios

Decision Support Capability Breast Cancer Scenarios. Class: 406-DL – Decision Support Systems and Health Care Final Project: Breast Cancer Decision Support Capability Scenario Group: Elizabeth Acord, Brian Frazior and Theresa Veith. Introduction. Breast Cancer Statistics

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Decision Support Capability Breast Cancer Scenarios

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  1. Decision Support CapabilityBreast Cancer Scenarios Class: 406-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; 2010. Available at: http://www.cdc.gov/uscs.

  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 • 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 • 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?

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