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Personalized Disease Management - Thyroid Cancer - Knowledge Management - Sunil Nair Health Informatics Dalhousie University

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Information about Personalized Disease Management - Thyroid Cancer - Knowledge Management...
Health & Medicine

Published on December 8, 2008

Author: snair

Source: slideshare.net

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Personalized Disease Management: Hope for Thyroid Cancer Survivors. Sunil Nair Dalhousie University Health Informatics HINF6230 March 26, 2008

Thyroid Cancer Adapted from Oncology Interactive Education Series DTC

“ 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.”

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%

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.

Improved detection – Early Diagnosis

Effective Modern Treatment

Mortality rate very low

High chance of recurrence

Thyroid Cancer Care Algorithm

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 .

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 .

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

Thyroid Cancer survivors in long-term follow up – patient empowerment and Self Management .

The Primary Care Practitioner at point of care for decision support

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

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

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

Patient Empowerment

“ an individual being an active member of his/her disease management team”

Patient-centered Care

Disease specific Patient education

Chronic Care Model (Wagner et al. 1999) Thyroid Cancer Survivors Community Resources Health System Self Management Support Delivery System Support Decision Support Critical Information Support Informed Activated Patient Prepared, Proactive Practice team Productive Interactions Functional & Clinical outcomes

Decision Support at point-of-care Access to EMR Computerized CPG CPOE Alerts & Reminders Computerization of Referral, Scheduling and Clinical results interpretation

Access to EMR

Computerized CPG

CPOE

Alerts & Reminders

Computerization of Referral, Scheduling and

Clinical results interpretation

How can KM Applications help Interactive Health Communication Applications ( IHCAs ) Developing Machine readable CPG, CP Decision Aids Semantic Web Web 2.0

Interactive Health Communication Applications ( IHCAs )

Developing Machine readable CPG, CP

Decision Aids

Semantic Web

Web 2.0

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.”

“ The interaction of patient-provider via Information technology device to access and transmit health information and receive guidance and support on health related issue.”

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

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

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

Non-Interoperable Collaboration tools and systems

Undefined data sharing policy and standards

Information tailoring to specific user needs

Inadequate Infrastructure

Expensive

Decision Aids in Cancer Patients Participation in decision making Encourages patient-centered clinical model Reduce patients uncertainty Increase patients knowledge Realistic expectations of outcomes

Patients Participation in decision making

Encourages patient-centered clinical model

Reduce patients uncertainty

Increase patients knowledge

Realistic expectations of outcomes

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

Content Description

Priorities vary

Content Categorization

Simplifying Complex Information

Presentation format

Video, text, Notebook or Online

Incorporating in to practice

Evaluation

Understanding Risks

Application of Semantic Web Community of Thyroid Cancer Survivors Population of Experts in Thyroid cancer; Care providers; Specialists Social Networks Blogs, Discussion forums, groups, Websites Thyroid cancer Survivor Information System Data Clustered By text, tags & Metadata Recommendation Engine Search Engine

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

Adaptive Patient-Specific Healthcare plans

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

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

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.”

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

Acknowledgement Dr. Murali Rajaraman Assistant Professor Department of Radiation Oncology Dalhousie University, CDHA

Dr. Murali Rajaraman

Assistant Professor

Department of Radiation Oncology

Dalhousie University, CDHA

Thank you!

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