uk health data analytics network inaugural workshop output · uk health data analytics network...

UK Health Data Analytics Network Inaugural Workshop Output

@Man_Inf @EPSRC @herc_farr #UKHDAN

In association with:

Wednesday 6 January 2016, 1530 - 2100Thursday 7 January 2016, 0900 - 1600

Chancellors Hotel, Manchester

2-11 Healthcare Opportunities Breakout Session

12-21 Research Challenges Breakout Session

22-25 Research Landscape Breakout Session

Group Name: Learning Health Systems _ Blue Healthcare Opportunity #1:

An opportunity to make a paradigm shift from Evidence Based Medicine to Learning Health Systems

Healthcare Opportunity #2:

Redefining organisational/professional/civic/ relationships to a more data-centric culture


High resolution longitudinal data

If you wish, draw a diagram which represents your group’s area, below:

Group Members: Neils Peek Ruth Norris John Ainsworth George Moulton Alex Casson Tjeerd Van Staa Ed Conely Allan Tucker Pete Bower Samhar Mahmoud David Prieto- Merino Amitava Banerjee Catherine Castillo


Group Members: Olly Butters Becca Wilson Michael Stone Emanuele Trucco Jo Knight Andrew Dowsey

Group Name: A Data Lifespan Carol _ Orange

A Healthcare Opportunity #1: Ghost of data past

• Data reuse/repurpose • Confidence/trust in meta-data • Data decay/obsolescence • Missing data

Healthcare Opportunity #2: Ghost of data present

• Harmonisation • Availability

Healthcare Opportunity #3: Ghost of data future

• Standardisation – centrally pushed good standards/SOPs etc. • Broad consent

Group Name: UX _ Orange

B Healthcare Opportunity #1: Collecting, integrating & availability

• Across databases, from point of care • Anonymise for linkage and research => publication Security; access; how to improve – scale

Ideas: 22, 24, 25, 29, 32, 34*

Healthcare Opportunity #2: Using and understanding data

• Enabling clinical decisions • Analytics • Knowledge discovery from databases • “Right tool for the right job”

Ideas: 23, 26, 27, 35, 36

Healthcare Opportunity #3: Cross-talk/end-UX

Taking collection and integration and understanding output to maximise output Stakeholders + end-user benefit experience (UX)

Ideas: 28, 30, 31, 33, 37


*Numbers relate to ideas initially submitted to WellSorted

Group Members: Stephen Swift Kayleigh Mason Grant Thiltgen Alison Noble Sandra Bucci Maarten De Vos George Demetriou

Group Name: Data Lifecycle _ Orange

C Healthcare Opportunity #1:

Data aggregation • Hypothesis generation • Methodological developments


Data analysis methods and models • Methodological developments • Anomaly detection • Decision support


Integrating knowledge into practice • Improved user experience • Improved decision support to patients and healthcare

professionals


Group Members: Chris Smith Natalie Shlomo Tarani Chandola Duncan Appelbe Para Vezyridis Adele Marshall Dai Evans

Data analysis methods and modules

Data aggregation

Integrating knowledge into knowledge

Group Name: Mythbusters _ Orange

D Healthcare Opportunity #1: Feedback on Data

Improve quality of data collection by rapid feedback • Incentives to do it right/contribute • E.g. proportion of patients with blood pressure measures

(relative to others)

Healthcare Opportunity #2: Participatory design of the analytical workflow

• Use results/data to bring experts together • Improve quality of results • Avoid meaningless results/conclusions and thus wrong policy • Improve acceptance of future results

Healthcare Opportunity #3: Better understanding/diagnosis using multi-modal data

• Bring together different data sources • Different views on data • Avoid inappropriate treatments • Stratification


Group Members: Tim Cootes Caroline Jay Roy Ruddle Edwin Beggs Carole Goble Krysztof Poterlowicz Mike Pearson

Good Data

Good Analysis

Bad Data

Bad Analysis

Group Name: Delivery of preventative & personalised Healthcare _ Red Healthcare Opportunity #1:

Development of personalised medicine through engagement involvement & inter-disciplinary collaborations


Development of analytical tools and guidance to facilitate #1


Development of research infrastructures to share and manage #1 and #2


1 2 3 4 5 6

Group Members: Lydia Drumright Alistair Sutcliffe Philip Quinlan Daniele Soria Jane Sarginson Samantha Crossfield

Ian Craddock Simon Harper Daniel Neagu


Group Members: Jens Rittscher Chris Taylor Anthony Coolen Paula Williamson Theodore Kypraios Thomas Ploetz Theodoros Georgiou Sacha Manson-Smith

Group Name: Rich Data and Feedback _ Healthcare Opportunity #1:

Richer better time-resolved data would enable: Improved prediction and early intervention • Preventing relapse • Detecting need for therapy change • Targeted further investigations • Dynamic management of care intervention


Involving the individual • Personalised feedback • Influencing lifestyle • Understanding individual needs • this may increase inequality


Better decision making • Objective rich real life data • Joint decision-making • Opportunity for better analysis of models • Contexualisation

Group Name: Computable Models _ Green Healthcare Opportunity #1: Predictive Analytics

• Integration of text mining and time series

Healthcare Opportunity #2: Natural Language Processing

User issues • Clinician training • Human & machine learning • End user analysis • Clinician buy-in

Healthcare Opportunity #3: Data Integration

• What data sources are needed? • Where’s the complexity? • Practice deployment under uncertainty


Group Members: Owen Johnson John Keane Robert Stevens Colin McCowan Andrew Renehan Alistair Willis Behzad Bordbar David Rew Arief Gusnanto Albert Burger David Clifton Emily Petherick Jim Weatherall Charlie McCay


Group Members: Ibrahim Habli David Tian David Hogg Ann Blanford

Group Name: Patient safety _ Pink Healthcare Opportunity #1:

Real-time monitoring and responses • E.g. Intensive Care Unit • Adverse drug reactions • Patient pathway


Learning from failures of safety critical systems & learning new analysis/learning systems • Proof of correctness of patient pathway


Population-level learning for safety = 8-12%

??


Group Members: Andy Brass Yi Wang Kieran O’Malley

Group Name: Leftfield Data _ Purple Healthcare Opportunity #1:

Geodata – location Health in the community Need to understand what is going on in the community • Twitter location data • Ambulance service data • Pollution • Weather • Smartphone/wearable tech • Environment/people • Part of forecasting process


• Genome data • Diagnosis • Treatment • Engine of precision medicine • Larger scale than ever • Ability to link biology/medicine • Variant have clinical significance • Tools used – variant has consequences


Combining Genotype and Phenotype data Real data/real problems How we capture phenotype correctly

Group Name: Data Integration Orange Research Challenge #1:

• Linkage of multi-stakeholder data sources • Silo’d information (e.g. patient collected data vs. EHR data vs.

HCP points of care) • CDIM

Research Challenge #2:

• Identifying relevant data to particular caregiving scenarios & personalised views of such data

• HCL - visualisation


• Clinical image data integration • Semantics of images • Data analytics over PACS systems


Group Members: Albert Burger Sheldon Steed Samhar Mahmoud

EHR EHRz

HCP People

Silo’d information Problem


Group Members:

Group Name: Damn! P_ Red Research Challenge #1: Barriers

• Information governance: confidentiality and privacy (Caldicott report expected in Jan)

• Social: fear • Organisational: bureaucracy & individuals (dog in manager,

conspiracy/mess up, technical blocks – don’t understand the coal face)

• Technical: open pseudonymiser • Solution – locality warehouses

Research Challenge #2: Open-ness & Transparency

• Fear: professional/patient – dispel – publish data held & what used

• Data sets : patient level = few. Aggregate – anonymised – publish “CDC – Wonder”

• Build trust

Research Challenge #3: Infrastructure & funding

• Data Quality a problem (technical, coding, architecture, standards)

• Connecting silos • Best practice • SNOMED-CT is coming! Primary Care

Dai Evans Duncan Appelbe Allan Tucker Para Vezyridis Dave Robertson Natalie Schlomo

Chris Smith Lydia Drumright Grant Thiltgen Becca Wilson Olly Butters

Group Name: HCI P_ Research Challenge #1:

• How to design interfaces to better effect behavioural change • Encourages data collection – non litigious • Applies to both patients/healthcare professionals • There is a reason/benefit for more data


• How information is presented • Universal platform/more personalised • Talking into account all stakeholders/users


• High quality data – minimum effort • How do we support data collection to minimise noise?


Group Members: Caroline Jay Simon Harper Ruth Norris Kieran O’Malley

Group Name: Getting the Basics Right P_ Green Research Challenge #1: End user engagement

• Sterling end user (health professional) engagement • Paper – paperless transition • Optimising – interfaces/systems • Out of which – better data capture

Research Challenge #2: Agile by-product of Normal Clinical Activity

• Early engagement of health informatics community in system design

• Agile – iterative • “Natural by product of activity” • Designing “values” & “ethics” into software systems

Research Challenge #3: Education

• Workforce education – digital transformation (understanding) Skills upgrade + gaps

• Capacity issues • Dilemma agile vs “enterprise

hardening”

• Universities, • Schools • Nat Comp Curriculum • Post/Grad institutions • e.g. Farr institute, NHS


Group Members: David Rew Theodoros Georghiou George Moulton Jon Whittle

Group Name: Rivers of Data P_ Pink Research Challenge #1: Marriage of Human & Machine

• Interpretability • Role of expert • Personal responsibility • Co-development of models

Research Challenge #2: Characterising complex temporal structure

• High dimensionality vs low-N diversity • “N of 1” models powered by population models • Temporal model evolution • Exploiting system characteristics (e.g. homeostasis)

Research Challenge #3: Guaranteeing robustness (of models)

• Calibration drift • Quantifying/managing uncertainty • Characterising generalisability (or predicting it?!) • Verification


Group Members: Jim Weatherall Chris Taylor David Hogg John Keane Alistair Willis Adele Marshall

Theo Kypraios David Prieto-Merino David Clifton Arief Gusnanto

Thomas Ploetz Joe Mellor Stephen Swift Anthony Coolen Maarten De Vos Owen Johnson


Group Members: Amitara Banerjee Alistair Sutcliffe Ann Blanford Peter Bower Ibrahim Habli David Schultz

Group Name: People and Data P_ Research Challenge #1:

One size doesn’t fit all when people want to view/interact/share/collect their data


Communication: advancing clinical decision-making/for improving patient outcomes/health


How do we use data to change patient behaviour?

Purple

Group Name: Analysis Pipeline P_ Brown Research Challenge #1: Data Integration

• Bring together data from different groups/sources • How to encourage collaboration, data sharing and common

standards

Research Challenge #2: Learning Structure from Data

• Finding useful features + explanation • Need new algorithms for huge data sets • Effective use of prior info/models

Research Challenge #3: Stratification & disease subtypes

• Find disease subtypes • Early diagnosis • Personalise treatment


Group Members: Tim Cootes Robert Stevens Sacha Manson-Smith Jens Rittscher Jo Knight Edwin Beggs

Group Name: From dodginess to rigour P_

Research Challenge #1: Understanding dodginess in all its guises

• Provenance recorded • Assessing data quality • Statistics, visualization, automation • The human factor • Understand data “fingerprint”

Research Challenge #2: Reduce preparation cost to only 30%

• Re-use by remote 3rd parties • Assimilation/data integration • Data standards/formats/interchange

Research Challenge #3: Allow low-effort rigorous analysis that is holistic

• Linked to resources & established knowledge about the data • Sensitivity, uncertainty due to data and processing • Propagation of confidence intervals across analysis workflow • Validate assumptions made during analysis, at each stage of the

processing pipeline


Group Members: Roy Ruddle Kayleigh Mason Daniel Neagu Niels Peek Jane Sargison Michael Stone Alex Casson

Group Name: Standardisation & Scaling P_


• Appropriate standards (data collection) • Appropriate data/metadata


• Understanding the barriers to implementing standardisation


• Building on the best of the past • Boosting at scale


Group Members: Charlie McCay Emanuele Trucco Matthew Sperrin Daniele Soria Emily Petherick Tim Croudace Paula Williamson Tarani Chandola

Group Name: Improving modelling of real world data P_


• Machine learning to provide robust models for working with real world data in a variety of applications


• Develop methodologies to improve current practice and lack of consensus in decision making: efforts to improve underlying data to get closer to “perfect” data against using methods to report on uncertainty of results based on complexity of data


Can we better understand and improve the transparency of domain specific knowledge: • Accuracy of results • Provenance of data items • Steps/assumptions made • Human/clinician/computer interactions


Group Members: Colin McCowan Ian Craddock George Demetriou Andrew Dowsey David Tian Samantha Crossfield Tjeerd Van Staa Alison Noble

Raw Output from Landscape Breakout Groups 7th January 2016

Group 1

What have we missed?

Do you have data or algorithms that you would be prepared to share – if so do you know

how to do this?

What disease area?

Are you patient facing/interacting with patients?

Added value of UK-HDAN

Find concrete problems and hold National thematic meetings about those problems

Problem solutions matchmaking (virtual or otherwise)

SME support group and resources for healthcare data

Tie-ups with AHSNs

________________________________________________________________________

Group 2

Verification of Protocols

Model checking

Multi-levelled

Are the underlying assumptions correct

How to regression test a theory


Distributed systems Communities (e.g. privacy)

o What to drop

o Distributed analytics

o Exploiting data redundancy

Industry

o Wide range of interests

________________________________________________________________________

Group 3

Survey Observations:

Weakness – more to analyse, univariate analyses

Strengths – good range of questions, good coverage

Omissions – multivariate, longitudinal surveys


Working case studies

Description who is here?

Lists of communities, projects, agencies


Website

LIST SERV

Searchable information about member’s research

Hackathons?

________________________________________________________________________

Group 4


Focus on network adding value

Professional development

TED talks/how to guides – hackathon

Researchers educated on data science capabilities

Data science receive feedback on healthcare needs

________________________________________________________________________

Group 5


Secondments (“Springboard Fellowships”)

Summer schools

Sharing MSC materials

Hackathons/Dataset challenges

________________________________________________________________________

Group 6

Survey Observations:

Strengths: it exists

o Got many stakeholders in one place

o Good balance of disciplines

o Agenda open to all

Weaknesses: insufficient engagement from industry and charities

o Ambiguity about what kind of healthcare experience

o May be missing some disciplines (e.g. oncology?)

o Patient representation


System assurance

Legal and governance issues

Healthcare policy

Data ownership and access

Engagement with industry (inc. wellbeing, EHRs, etc.)

Citizen science/health communities

Getting HDA on the radar for students and others – capacity building


Identifying top priorities for funding /action/resourcing

Bridging disciplinary gulfs

Group 7


Risk communication

Consent to use of data

Behaviour change – “NUDGE”

Data sharing

Trust

Altruism vs self-interest

Usability

Accessibility

“Patient groups”

Nature of clinical problem

________________________________________________________________________

Group 8

Survey Observations

Massive variation in barriers to access to data

Strengths: basic snapshot – balancing time/return rate

Omissions

Level of experience in methodological scale

Basic user – world leading export

Types of data people using

How well this network is already networked?


Statisticians

Clinicians

Industry involvement


Skill data – speed dating

________________________________________________________________________

Group 9

Survey Observations

Access to data Barrier – “Not at all” – 10% - who?

What data sources did they tick? Could be people not yet working in healthcare (20% of

responders)


Agreed the group should continue

Next workshop – based on case studies of success or partial success stories

Prioritisation exercise

Identification of major issues faced by the community – joined up approach to tackling them

e.g. Administrative Data Research Network access for accredited researchers

We would like to feed in news from ourselves as the health informatics community into the

Farr Institute which we believe might be the organisation with a structure that could help

make things happen

________________________________________________________________________

Group 10


Clinicians and industry

Could ask the question – multidisciplinarity

Would be useful to ask if people would find their research enhanced by working with the

persons in the list where don’t already

Network! People!


Find ways the group would like to collaborate. What knowledge and info there is and

provide it

Develop & deliver the different levels of training needed by community

uk health data analytics network inaugural workshop output · uk health data analytics network...

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