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Personalized Disease Management: Hope for Thyroid Cancer Survivors.
Sunil NairDalhousie University Health InformaticsHINF6230March 26, 2008
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Thyroid Cancer
Adapted from Oncology Interactive Education SeriesDTC
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“Thyroid Cancer is the most rapidly increasing cancer
among Canadians” Males - 4.9% per year 1994-2003;
Females - 10.4% per year since 1997
Annual Rate of Increase - >5%
Highest 5-yr Relative Survival Rate(RSR)- 96%
Recurrence Rate – up to 20%
“Cancer Care Ontario: Cancer in Young Adults in Canada, Canada, 2006.”“National Cancer Institute of Canada: Canadian Cancer Statistics 2007.”
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Why is Thyroid Cancer Important Improved detection – Early Diagnosis Effective Modern Treatment Mortality rate very low High chance of recurrence
Increasing number of young cancer survivors who need lifelong care & support.
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Thyroid Cancer Care Algorithm
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DTC- Long-Term Disease Management Chronic Disease Management
“a multi-disciplinary, continuum based approach that proactively addresses the patient and physician relationship and the preventive measures and evidence-based practice guidelines required to alter the natural course of the disease and improve overall health.” -Disease management Association of America
Accurate Surveillance and Maintenance to check for possible recurrences
Follow-up (F/U) - Testing for Tg and WBS; F/U is lifelong (more than 30 yrs) F/U treatment is different for patients with low, intermediate
and high risk. F/U is done mainly at the primary care.
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Knowledge support needed for Thyroid Cancer survivors in long-term follow
up – patient empowerment and Self Management.
The Primary Care Practitioner at point of care for decision support
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Barriers to Effective Long-term Thyroid Cancer Management
Primary Care Providers unable to follow Practice Guidelines
Difficulties with active follow-up Lack of care planning and coordination Patients not informed about their disease
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Long-term Thyroid Cancer Management Patient Empowerment
“an individual being an active member of his/her disease management team”
Patient-centered Care Disease specific Patient education
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Chronic Care Model (Wagner et al. 1999)
Thyroid Cancer Survivors CommunityResources
Health System
Self ManagementSupport Delivery
SystemSupport
Decision Support
Critical InformationSupport
Informed ActivatedPatient
Prepared, ProactivePractice team
ProductiveInteractions
Functional & Clinical outcomes
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Decision Support at point-of-care Access to EMR Computerized CPG CPOE Alerts & Reminders Computerization of Referral, Scheduling and Clinical results interpretation
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How can KM Applications help Interactive Health Communication
Applications (IHCAs) Developing Machine readable CPG, CP Decision Aids Semantic Web Web 2.0
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Interactive Health Communication Applications (IHCAs) “The interaction of patient-provider via Information
technology device to access and transmit health information and receive guidance and support on health related issue.”
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Developing Machine readable CPG Several Modeling
methodologies exists GEM, GLIF, EON,
Protégé etc. XML used in
implementation of CPG adds specific
structure to text for content retrieving and presentation
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Challenges of Computerized CPG Adaptation
Non-Interoperable Collaboration tools and systems
Undefined data sharing policy and standards Information tailoring to specific user needs Inadequate Infrastructure Expensive
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Decision Aids in Cancer Patients Participation
in decision making Encourages patient-
centered clinical model
Reduce patients uncertainty
Increase patients knowledge
Realistic expectations of outcomes
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Challenges in Decision Aids
Content Description Priorities vary Content Categorization Simplifying Complex Information
Presentation format Video, text, Notebook or Online
Incorporating in to practice Evaluation
Understanding Risks
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Application of Semantic Web
Community ofThyroid
CancerSurvivors
Population ofExperts inThyroid cancer;Care providers;Specialists
Social NetworksBlogs, Discussionforums, groups, Websites
Thyroid cancerSurvivorInformation System
Data ClusteredBy text, tags &Metadata
RecommendationEngine
SearchEngine
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Semantic Web of Healthcare Knowledge Adaptive Patient-
Specific Healthcare plans
Adapted from “Adaptable Personalized Care Planning via a Semantic Web Framework” – SSR Abidi & H Chen
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Semantic Web Translation Limitations New and evolving technology
Gaps in Standardization & Implementation
Unavailability of Semantically annotated information source
Slow Performance of RDF, OWL
Lack of Standardized rule language
Cross community interactions
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Future direction Personalized Patient focused Programs
To benefit participants with lower self efficacy and health related quality of life
Efficient Decision Support technologies Generating customized Content System independency
A large number of Cancer Survivors, require repeated active treatment and have continuing need for cancer care
resources and support services.“National Cancer Institute of Canada: Canadian Cancer Statistics 2007.”
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Acknowledgement
Dr. Murali RajaramanAssistant ProfessorDepartment of Radiation OncologyDalhousie University, CDHA
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Thank you!