Future of patient data global summary - 29 may 2018

Global Ambition
There are great expectations on the benefits from access to more and better
patient data - A common aim is to ‘give health data back to the individual’
so the patient becomes the point of integration and control.

Improving Efficiency
Implicit within many healthcare systems is the need to use data to improve
efficiency and reduce costs. Without a fundamental shift driven by enhanced
information use, several care services may become stressed to breaking.

Expert Insights | 12 Major Discussions Around the World (Sep 2017 to Jan 2018)

Location Criteria
These twelve events were held in a selection of countries with different
levels of health spending and average life expectancy - as well as
varied combinations of public and private healthcare systems.

More Data | The volume of health data is evidently growing rapidly

Changing Definition of Patient Data
The patient data set is expanding: It includes high-quality clinical information,
more personal data from apps and wearables plus a broadening portfolio
of proxy data, as well as insights on the social determinants of health.

Gaps and Interoperability
Given the multiple data gaps in existing systems, the expectation is that technology
will provide solutions that better bridge these and ensure interoperability.
Common standards and cleaner data will be fundamental drivers of change.

EHR Integration | A core ambition in combining data sets

Increasing Control
The question of ownership of health data is in flux - especially on access vs. use.
Patients may have increasing ‘control’ of their data, but whether they
become ‘custodians’ depends on culture, regulation and need.

Building Trust
In many regions, trust needs to (re)built between payers, providers
and patients as well as with new entrants. New technology platforms
and improving communication with the public both play a major role.

Managing Distrust
Concern about ulterior motives for the use of data is high and
some see AI adding to the challenge. Many recognise the need
for greater transparency on practice in some pivotal areas.

Data Sharing | Who we trust with our health data is critical

Data Breaches | Health data breaches have been amongst the biggest globally

Enhanced Protection
Anonymized, aggregated data is more easily re-linked and sensitive health data
is a target for cyber-attacks. Questions are raised around the benefits of
centralized vs. decentralized data, encryption and the impact of localisation.

Individualized Medicine
The prospect of more individualized ‘n=1’ healthcare is accelerating.
Predictive analytics and genetic profiling transform medicine:
But will the benefits be for all or just a lucky few?

Personal Data Stores
New platforms help patients and providers to manage and curate
their data across multiple partners. Universally accepted credentials
help to drive greater personalisation of health services.

Health Data Marketplaces
Embedded in the future of access to health data, is its value, exchange and
what will be public commons vs. what is for commercial purposes. Personal and
clinical data will increasingly be represented in healthcare data marketplaces.

The Initial Impact of AI
There are great expectations around AI. Initial advances from
machine learning and pattern recognition will be most significant
in enabling more efficient diagnosis and better prediction.

AI and Unstructured Patient Data
As deep, self and reinforced learning develop, the ability to deal with
unstructured data delivers major improvements in diagnosis and treatment.
AI agents learn by trial and error and AI is embedded into many clinical decisions.

AI and Mental Health
With voice and facial recognition increasingly analysing users’ patterns of behaviour,
AI is applied to identify stress and anxiety. Some patients are more comfortable
and honest talking to machines rather than humans in high-stress situations.

AI Companies | China is rapidly growing its AI capability in healthcare

Re-engineering from Within
Change is coming from governments and major existing healthcare companies.
More patient-focused and collaborative business models are targeted on changing
reimbursement mechanisms and driving shared risk across the payers and providers.

India and China Setting Standards
Significant new approaches for global healthcare may emerge from India where
the scale of Aadhaar and related platforms drives integration and innovation.
China is also building momentum across surgery, AI and predictive analysis.

Big Tech Health
Led by Amazon, big tech will disrupt and reinvent some core elements and unify
fragmented systems. All of the big 5 are investing heavily in major ‘special’ projects
focused on the radical transformation of healthcare centred on the individual.

Data Localisation and Control
Driven by national security, commercial interest and privacy standards,
more governments seek to restrict the sharing of health data beyond
their borders - and so push-back against some global ambitions.

Access Inequality
As advances roll out, there is growing concern for those being left behind.
Some hope that, with more and better data, health inequality can be reduced.
Others see a widening divide between those with access and those without.

Ageing Populations | More remote and caregiver support is seen as critical

Digital Skills
Some healthcare professionals lack the skills for digital transformation.
Whether we need to learn, unlearn and relearn new skills, or if new systems can
evolve fast enough to provide seamless support for doctors, is a growing debate.

Agreed Standards
Many want standardisation of outcome-based measures. With regulators behind
the curve, compliance, consent and privacy are shared concerns. How countries
deal with these is as much political and commercial as it is technological.

PRIVATISATION OF HEALTH INFORMATION

Open vs. Private Knowledge
Escalating privatisation of medical knowledge and more ‘secret software’ challenge
the view that healthcare information, especially concerning AI, should be open source
or shared within agreed governance systems: Deep pockets have greatest impact.

Value of Data | Health data is seen as being increasingly valuable

Financial vs Social Value
As organisations retain as much information as possible, health data has a price.
It is increasingly prized and what may be public vs. commercial is a major debate.
Many compete to prioritise the social value of heath data over the financial.

Ensuring Impact
There is lots of potential, but also many challenges. Change may occur more at a
regional than global level but, to have impact, it must deliver clear advantage for
those who most need better healthcare – often the weakest and most vulnerable.

Level of Privacy Regulation:
DLA Piper https://www.dlapiperdataprotection.com
Heavy Robust Moderate Limited
Current Healthcare Expenditure
as a %GDP (2015)
COUNTRY TOTAL GOVT PRIVATE
San Francisco 19 JAN 2018
C Top 3 Challenges O Top 3 Opportunities E Top 3 Emerging Issues
London 14 DEC 2017 Oslo 30 OCT 2017
Dubai 27 SEPT 2017
C Data Gaps
Infrastructure
Digital Skills
O Predictive Analysis
Artificial Intelligence
Genetic Profiling
E Standardised Measures
Mental Health
Ulterior Motives
Johannesburg 10 OCT 2017
Frankfurt 25 JAN 2018
Brussels 9 NOV 2017
Boston 17 JAN 2018
Toronto 16 JAN 2018
Future of Patient Data (2017/18)
Locations and Key Insights
Australia 9.4 6.5 2.9
Belgium 10.5 8.6 1.8
Canada 10.4 7.7 2.8
UK 9.9 7.9 1.9
Germany 11.2 9.4 1.7
India 3.9 1.0 2.9
Norway 10.0 8.5 1.5
Singapore 4.3 2.2 2.0
South Africa 8.0 4.4 3.6
UAE 3.5 2.5 1.0
USA 16.8 8.5 8.4
C Combining Data Sets
Digital Skills
Resistance from HCPs
O Personal Data Sharing
Genetic Profiling
E Inequality
Privatization of Health Data
Data Sovereignty
Sydney 15 NOV 2017
C Linkability of Open Data
Data Gaps
Ulterior Motives
O Genetic Profiling
Predictive Analysis
Data Marketplaces
E New Models
Informed Consent
New Entrants
Getting Closer to the Patient
Expanding Set of Data
O Predictive Analysis
Personalisation
E Standardised Measures
Inequality
Global Data Sharing
C Ulterior Motives
Resistance from HCPs
Trust
O Artificial Intelligence
New Business Models
Mental Health
E Data Sovereignty
Patient Empowerment
Data Marketplaces
C Data Ownership
Ulterior Motives
Trust
O Data Marketplaces
Personalisation
E New Business Models
Privatisation of Health Data
Informed Consent
C Expanding Data Set
Combining Data Sets
Regulation
O Data Marketplaces
Personalisation
E Informed Consent
Data Sovereignty
Inequality
C Integration of Data
Data Quality
Unstructured Data
O Individualized Medicine
Data Marketplace
E Privatisation of Health data
New Business Models
Value of Health Data
C Getting Closer to the Patient
Combining Data Sets
Data Gaps
O Genetic Profiling
Proxy Data
E Inequality
Standardised Measures
Privatisation of Health data
Trust
Linkability of Open Data
O Embedded AI
Predictive Analysis
E New Business Models
Inequality
Singapore 13 NOV 2017
C Regulation
Combining Data Sets
O Artificial Intelligence
Individual Custodianship
Personalisation
E Data Sovereignty
Value of Health Data
Mumbai 23 NOV 2017
C Data Quality
Ulterior Motives
Data Ownership
O Data Marketplaces
India Setting Standards
E Informed Consent
New Models
Inequality
Project Summary | Locations and Key Insights

Thank You
We would like to thank all hosts and partners for their support in enabling
this important project to take place. In addition, we are hugely grateful
to all participants for their time, insight and willingness to challenge views.

Future Agenda
84 Brook Street
London W1K 5EH
www.futureagenda.org

Future of patient data global summary - 29 may 2018

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Future of patient data global summary - 29 may 2018