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Potential contributions of health data science to Learning Health Systems

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Potential contributions of health data science to Learning Health Systems

Potential contributions of
health data science to Learning
Health Systems
To m K e l s e y
Reader in Health Data Science
twk@st-andrews.ac.uk
Overview
1. Learning Health Systems
2. Hypothesis-led research
3. Data-driven research
4. Potential contribution of the Health Data
Scientist
Learning Health Systems
Learning Health Systems
Deliver'Message
Improve'Practice
Analyze'Data
Aggregate'Data
Interpret'Results
Adapted'from'Friedman'2014
Learning Health Systems
Finding out what constitutes best care is important
How do we deliver this care?
Jason'report:'November,'2014:
The$learning$health$system$needs$to$be$“closed$loop”$to$ensure$
a$continuous$and$transparent$cycle$of$research,$analysis,$
development,$and$adoption$of$improvements$relevant$to$
health$and$wellness$and$to$the$delivery$of$health$care.
The LHS as a Research Challenge
Deliver'Message'
to'Academics
Improve'Practice?
Analyze'Data
Aggregate'Data
After'Academic'
Study'Design
Interpret'Results
Adapted'from'Friedman'2014
Hypothesis-led Research
Study'Design
Sample'Sizes
Consent
Recruit
STEP'1
Collect'&'
Analyse
Study'Data
STEP'2
Carefully'
Interpret'
Results
STEP'3
Publish
Results
STEP'4
Hypothesis-driven Science - Example 1
Hypothesis-driven Science - Example 2
Meta Analysis
Meta-analysis
Published'result Published'result
Published'result
1
2
4
3
Inclusion'&'Exclusion'Criteria
Assess'for'Bias'&'Study'Quality'
Summarise Effects
Publish'Summary'Results''
Meta Analysis - Example 1
Meta Analysis - Example 2
Review
Review
Published'findings Published'findings
Published'findings
1
2
4
3
Identify'Empirical'Evidence
Expert'Appraisal
Synthesis
Publish'Results''
Review - Example 1
Review - Example 2
Hypothesis-led Research
The classical approach to medical research
Fully concordant with Popper’s theories of reproducibility &
falsifiability
Data science tools and techniques are well established
For the most part
In principle, analysis methods are defined a priori
Data are collected after we’ve set out how they will be
analysed
Data-driven Science
Assemble'
Data'of'
Interest
STEP'1
Analyse
Using'
Modern'
Tech n i ques
STEP'2
Carefully
Interpret
Results
STEP'3
Publish'
Results
STEP'4
Data-driven Science - Example 1
Data-driven Science - Example 2
Data-driven Science
The modern approach to medical research
But well-established as a research paradigm
Mendeleev’s Periodic Tables of 1869
Florence Nightingale’s analysis of complex mortality data
Data science tools and techniques are modern
Machine learning & AI
Ensemble techniques in Statistical Learning
Analysis methods are defined a posteriori
Data are collected before we’ve set out how they will be
analysed
Learning Health Systems
Deliver'Message
Improve'Practice
Analyze'Data
Aggregate'Data
Interpret'Results
Adapted'from'Friedman'2014
Data-driven
Research
Potential Contributions of the Health Data
Scientist
1. Ensure the highest quality of data-driven research
outputs
2. Disseminate to non-academic audiences
3. Engage directly with healthcare providers
4. Use case studies from this direct engagement to
promote new research
5. Engage with the health informatics landscape
Potential Contributions of the HDS
Deliver'Message'
to'Academics
Improve'Practice?
Analyze'Data
Aggregate'Data
After'Academic'
Study'Design
Interpret'Results
Adapted'from'Friedman'2014
1.
4'&'5.
3.
2.
1. High-quality of research outputs
The data-driven component of the LHS loop
remains important
Rapidly improving techniques & technologies
For example:
a. Population studies
b. Unstructured data
c. Normative modelling
1a: Population studies
Linking, for example, cancer registry to maternity
data can yield important new insights
1b: Unstructured data
Potentially useful healthcare data is in the form of
notes, comments & audio/video files
Can the techniques we use for financial analysis be
adapted to these data?
1c: Normative Modelling
Age-related models in physiology & endocrinology
Normal scores for new patients allow personalisation of
care
Uterine volume – Kelsey et al., 2016
Inhibin B – Kelsey et al., 2016
Ovarian follicle density – McLaughlin, Kelsey et al., 2015
Testosterone – Kelsey et al., 2014
Ovarian volume – Kelsey et al., 2014
Anti-Müllerian hormone – Kelsey et al., 2011
Human ovarian reserve – Wallace & Kelsey, 2010
2. Dissemination and Outreach
The standard approach is to publish in traditional
journals
With access via library/personal subscription
Or $30 to download the paper
I prefer the open-access paradigm, making outputs
available to anyone with a browser
Data and code are also made instantly available
Facilitating both reproducibility and wider dissemination
2. Dissemination and Outreach
Specialist Meeting talks
Keynote, ISFP 2015, Shanghai
Invited, CFAS 2015, Nova Scotia
Expert panel membership
Launch event on the future of artificial intelligence and
machine learning 2018, London
Research Council Impact Acceleration
EPSRC Impact Festival 2018, Edinburgh
3. Direct Engagement with Healthcare
Make a positive contribution to better practice
involves a detailed understanding of the stresses,
issues and culture within healthcare systems
A. At the strategic level
§Setting standards for best practice
B. At the operational level
§Involvement with the deployment of new care structures
3A. MSN Children & Young People with Cancer
NHS Scotland Managed Services Network
Directly funded
Cabinet Office oversight
Tasked with the provision of high-quality &
standardised care throughout Scotland
I am on the main board of the MSN
Quarterly board meetings
Funding, appointments, strategies, risks, eHealth, KPIs,
3B. MSN CYPC Specialist Advisor
eHealth, data quality, risk assessment, IG, compliance, etc.
1. Operational Delivery Group
§Detailed oversight of neuro-oncology, palliative care,
psychosocial care, pharmacy, MDT organisation, etc .
2. MyStoryNow smartphone app
§Detailed digital record of treatment for survivors
3. Safety Checklists Project
§Migration of surgical checklist methods to oncology &
haematology
4. Case studies to motivate new research
MSN: Scheduling of MDTs
EPSRC £929,076 – PI Ian Miguel
MSN: MyStoryNow data privacy
EC 826278 £800,000 of 4.5m – PI Kevin Hammond
Imperial College: Individual luteal-phase support
MRC Experimental Challenges
Wellcome Trust Career Development Fellowship
MSN: Teenager & young adult transition
NCRI TYA subgroup award
Funded
Under
Review
5. The Health Informatics Landscape
Governance and oversight are vital
Subject privacy must be a leading principle
Scotland has a complex & effective landscape
Safe Havens for data
eDRIS assist with study design & compliance
Public Benefit & Privacy Panel
SHARE opt-in
SPIRE resource for GP data
In addition to standard ethical approval
5. The Health Informatics Landscape
I am an advocate for the use of synthetic data
1. The potentially-identifiable data is in a safe haven
2. A safe haven colleague produces data with the same
headings and descriptive stats
3. I produce code for analysis and test it on the
synthetic data
4. I send the code to the safe haven
5. The code is deployed on the actual data
Compliance is built-in to the framework
Summary
Deliver'Message
Improve'Practice
Analyze'Data
Aggregate'Data
Interpret'Results
1.'
New'
techniques
New'
technologies
Focus'on'
personalised
medicine
2.'
Open'access
Non-academic'talks
3.'
Direct'
engagement
Strategic'
oversight
Operational'
delivery
4'&'5.'
Case'studies'->'New'research'projects'
HI'landscape'navigation
Any Questions?
twk@st-andrews.ac.uk
ResearchGate has not been able to resolve any citations for this publication.
Age-related models in physiology & endocrinology • Normal scores for new patients allow personalisation of care • Uterine
  • Kelsey
• Age-related models in physiology & endocrinology • Normal scores for new patients allow personalisation of care • Uterine volume-Kelsey et al., 2016