Download Variety of Decision Support Systems and Their Implementation

Variety of Decision Support Systems and Their Implementation Timeline
1. Low-hanging Fruit
2. Improving Current System
3. Personalised Medicine Pilot
4. Implementing Personalised Medicine
• Standardised process/interface for data input in clinical
specialist interfaces enabling solid data analytics (usingICD-10, NCSP,
SNOMED, …)
• Improved data visualisation and health condition overview with
highlights
• Improved data aggregation across various EHR-s
• Structured personal health declaration
• Transformation from document based to event based data model
• Integration of test-patients’ up to date phenotype data through
questionnaires
• Integration of test-patients’ family history data through questionnaires
• Integration of test-patients’ gene data for genetic disease risk
assessment
• Interface for DSS rules management
• Interface for assessing the validity/invalidity of DSS provided
rule-based suggestions
• Patient disease risk score calculation
• Personalised lifestyle management guidelines
• Integration of patient’s own fitness and quantified self data
• Integration of patients gene data for genetic disease risk assessment
• Possibility to share medical data with family members for genetic risk
assessment
• Possibility to share medical data with medical specialists for enhanced
monitoring and risk notifications
• Patient stratification for screening selection
• Personalised long-term treatment schemes
• Patient and medical specialist communication platform
Perspective timeline from today to full implementation of Personalised Medicine in Estonia
Clinical Decision Supports
Scientific Gene Research
Health Management
Patient Monitoring
Diagnosing and treatment suggestions
based on genome data, patient life style, medical
and family history data. Notifications for conflicting
prescriptions. Checklists for medical procedures.
Estonian Genome Center scientific research
project.
Personalised health management and disease
prevention screening process based on genome
data, life style data, medical history and
family history.
Chronic disease and post-clinical episode
monitoring and management. Telemedicine
solutions for chronic patient regular checkups.
Patient diary enriched with life style data.
Notifications for expiring prescriptions. Treatment
adherence monitoring.
Enablers:
Implementation and management of digitised clinical scheme
algorithms. Benefiting of genome data. Standardisation of
data (ICD-10, NCSP, SNOMED, etc.) for implementing
preexisting DDSS.
Benefits:
Higher average treatment quality. Saving time and improving
certainty of clinical decisions.
Services:
• Notifications for conflicting prescriptions
• Diagnosing and treatment scheme selection support
• Medical procedure checklists
Enablers:
Sequencing existing tissue samples. Building applications and
algorithms for scientific studies.
Benefits:
New scientific discoveries related to specifics of Estonian
genome data.
Services:
• Estonian gene based gene risk model
Enablers:
New processes for GP-level screening, including genome data
(moving healthcare from volume-based to value-based and
preventive).
Enablers:
New processes (e.g. GP, family nurse monthly reviews). IT
solutions for health and medical diaries, notifications).
Benefits:
Better quality from more efficient prevention.
Cost effectiveness through more focused screening programs.
Benefits:
Better patient engagement. Resource savings from more
efficient prevention and treatment. Time savings through
more effective regular checkups.
Services:
• Patient disease risk score calculation
• Screening and clinical visits prioritisation based on patient
risk score
• Personalised lifestyle management guidelines
• Health diary enabling systematic collection of quantified
self and fitness data
Services:
• Notification system for expiring prescriptions
• Personalised long-term treatment schedules
• Patient and medical specialist communication platform
• Patient diary for collecting patient measurements and
drug/treatment scheme adherence
• Virtual medical appointment for chronic patients based on
patient diary data
Improved Data Usage and
Visualisation
Genome, health and medical data from various
information systems aggregated and visualised in
a usable medical doctor application.
Enablers:
Aggregating current data. Implementing better processes for
data collection (standardised process for data input, patient
QS + life style data, patient medical profile)
Benefits:
Saving GP, clinical specialist, work time during clinical visits.
More accurate health overview for patient and clinician.
Improved treatment quality. Notifications based on
aggregated data.
Services:
• Standardised process for data input in clinical specialist
interfaces enabling solid data analytics
• Improved data aggregation across various EHR-s
• Improved data visualisation and health condition overview
with highlights
• Inclusion of genome data
Public Health Analysis Tools
Better overview of general health trends,
opportunities for prevention and resource allocation
management.
Enablers:
Analysable aggregated patient data.
Benefits:
Measuring treatment quality. Better resource management.
Assessing effects of prevention and treatment.
Services:
• Public health and disease trend analysis support