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Collaboration for Life © 2016 S23M

Improving Interoperability and Trustworthiness of Healthcare Data Repositories

Jorn Bettin and Martin Elliffe

HINZ Conference 2016


- 1,800,000 years: Cumulative cultural transmission, teaching, imitation, experimentation

- 200,000 years : Spoken human languages — local communication of tacit knowledge

- 5,400 years : Written human languages — communication across time, explicit knowledge

- 600 years : Printing press — 1-to-many communication across space, scale

- 180 years : Electrical telegraph & telephone — global peer-to-peer communication, on demand

- 15 years : Internet — global 1-to-many communication, zero marginal cost, dirt cheap

- Now : Internet of things – machine-to-machine communication, new technologies every month

Exponential change in communication

apps

time


Unhelpful “strategies”

fear busyness


A single repository for all services and all people?

Experiences in both New Zealand and overseas suggest that the probability of success would be low.


A trustworthy advice process includes feedback loopsand engages all stakeholders in a learning process


Creating a learning organisation / system

• The SECI model is a useful conceptual tool for organising and structuring new service / product development, and for extending the concept of continuous improvement into the realm of digital business and knowledge-intensive processes

• Takeuchi, Nonaka, The New Product Development Game, https://hbr.org/1986/01/the-new-new-product-development-game , 1986

• Nonaka, Toyama, Hirata, Managing Flow: A Process Theory of the Knowledge-Based Firm, Palgrave Macmillan, 2008

https://hbr.org/1986/01/the-new-new-product-development-game


A working definition of trust

Trust is a meta-belief that allows propagation and installation of beliefs in a network of agents.

Trust between two agents develops through an ongoing process of maintaining shared understanding, and it correlates with the intensity and duration of maintaining shared understanding.


Trusted collaboration

1. Intentional trust – assurance that shared data will only be used for the intended purposes.

2. Systemic trust – involves inherent/static qualities of data-handling systems.

3. Dynamic trust – the development and destruction of trust over time, and the propagation of trust within social networks.


Intentional trust

• The intentional aspect of trusted collaboration involves assurance that shared data will only be used for the intended purposes.

• Individuals might share healthcare-related data to monitor treatment and/or improve research, but only on the assurance that such data would never be used to increase insurance premiums.

• Healthcare providers might share performance statistics with their funding partners for quality-assurance purposes, but only on the assurance that such data would never be used to influence pricing negotiations.

deception

distrust


Intentional trust – Risks

If data custodianship and management of access rights is centralised …

• Which entity defines allowable use and defines limits for monetisation?

• Who monitors compliance and how effective are the available monitoring tools?

• Complex role based access control definitions become impossible to maintain with confidence.

• A large data security breach is only a matter of time. When a breach occurs, it affects millions of people. Intentional trust can’t be restored.


Decentralising intentional trust

• Data custodians and data owners (healthcare practitioners, patients, local communities) monitor and control the use of data, and may get involved in decisions related to monetisation.

• Monitoring is distributed, and monitoring tools only need to deal with correspondingly small data volumes.

• Data custodians and owners jointly grant access on a need-to-know basis to specific entities and practitioners – the risks of poor access control definitions are minimised and the impact of a mistake is limited to one individual.

• When a security breach occurs, the impact only affects one person or a very small number of people.


The systemic aspect of trusted collaboration involves inherent/static qualities of data-handling systems. The following ten data quality dimensions from the MODA + MODE methodology provide a framework for continuous improvement of data architectures and knowledge repositories.

1. Accuracy 2. Currency 3. Completeness 4. Security 5. Availability

Systemic trust

quality

distrust

6. Unambiguity 7. Findability 8. Traceability 9. Simplicity 10.Usability


Systemic trust – Risks


• What encoded heuristics are used to determine which input data feeds are trustworthy?

• What incentive is there for the system operator to continuously improve and innovate?

• What level of technological redundancy (both hardware and software) must be mandated to minimise the risk of system-wide outages? What outage risk is deemed acceptable?


Decentralising systemic trust

• Data custodians and data owners (healthcare practitioners, patients, local communities) determine which input data feeds they trust.

• System operators, just like Web hosting companies and Cloud service providers today, have to compete for individual customers, and have an incentive to maximise availability and quality of service.

• A federated data architecture provides incentives for open interoperability standards and for open source implementations, encouraging a healthy level of technological redundancy and peer review.


Dynamic trust

• The dynamic aspect of trusted collaboration relates to the development and destruction of trust over time, and to the propagation of trust within social networks.

• The dynamic aspect of trust-building and trusted collaboration can be formalised in a suitable model.


Dynamic trust – Risks


• Which entity decides which data access rights are appropriate for which party?

• What level of visibility do patients have over the flow and accuracy of health information?

• How big are the temptations to “monetise” healthcare data in a way that is detrimental to patients?

• What prevents simplistic approaches from crowding out local perspectives and important learnings?


Decentralising dynamic trust

• Data custodians and data owners (healthcare practitioners, patients, local communities) jointly determine which data access rights are appropriate for further parties.

• Patients have full visibility over the flow of health information.

• Patients are in a much better position to detect attempts to “monetise” healthcare data in detrimental ways.

• Creating an opportunity for further feedback loops to emerge, and for local communities to contribute to learning and greater shared understanding.


Formalising the essence of dynamic trust


The future is decentralised – it’s already happening• Distribute data custodianship and ownership to minimise

the impact of security risks and avoid data quality problems, and as a result, reduce the number of mistakes in diagnostics and patient care

• Empower people across the globe to exchange their health data on neutral and independent platforms

• A consortium that gathers four of Europe’s regions in biosciences, medical technology and health entrepreneurship

• Bringing together market stakeholders in digital health, and empower them to collaborate and learn from each other

• A trust layer for personal health science and a personal health information exchange using blockchain technology

• End-to-end encrypted file sync and sharing for teams who want to protect confidential data


Related research, techniques, and technologies• Cell Platform

http://s23m.org/S23M/cell-platform.html

• Cell Platform use cases in the healthcare domainhttp://s23m.com/healthcare/index.html

• Model Oriented Domain Analysis and Engineering (MODA + MODE) methodology http://s23m.com/glossary/index.html

• How do we mobilise and align NZ’s policy, research, healthcare and commercial capabilities to deliver world-leading health outcomes? https://ciic.s23m.com/expected-results/ciic-5-march-2016-auckland/

• How can we make sure that the knowledge provided by clinical decision support systems is reliable? https://ciic.s23m.com/expected-results/ciic-3-september-2016-auckland/

http://s23m.org/S23M/cell-platform.html

http://s23m.com/healthcare/index.html

http://s23m.com/glossary/index.html

https://ciic.s23m.com/expected-results/ciic-5-march-2016-auckland/

https://ciic.s23m.com/expected-results/ciic-3-september-2016-auckland/


Thank you!S23M

jorn . bettin @ s23m.com

Nothing beats capturing the knowledge flow of leading domain experts to co-create

organisations & systems that are understandable by future generations of

humans & software tools.


Appendix Working at the boundary of knowledge


Motivation for information quality logic (IQ logic)

• When working at the boundary of knowledge, models may contain elements that represent unknown values – knowledge is limited by the accessibility of trustworthy information

• When used at the boundary of applicability, models may contain elements that are not applicable – all models have a limited scope of applicability


4-state information quality logic (IQ logic)1. Not applicable – the proposition is categorically not applicable

“John is pregnant” – when there is trustworthy evidence that John is a male mammal

2. False – the proposition is not true

“Jane is pregnant” – when there is trustworthy evidence that Jane is not pregnant

3. Unknown – the proposition is applicable in the concrete context, but there is insufficient trustworthy evidence to determine whether the proposition is true

“Jane is pregnant” – when Jane has not given anyone any indication of being pregnant or is not aware of being pregnant

4. True – when there is trustworthy evidence that the proposition is true

“Jane is pregnant” – when Jane has received clear pregnancy test results or is in the later stages of pregnancy


Usefulness and relevanceThe usefulness dimension of information

• True and false distinguish between propositions that can be relied upon and misleading propositions. Misleading information may be harmful when used in decision making! That’s the motivation for traditional 2-state logic.

The relevance dimension of information

• Not applicable and unknown distinguish between irrelevant and relevant propositions. Irrelevant information is distracting and adds spurious complexity to the context at hand.The exponential explosion of data and information on the Web goes hand in hand with a corresponding increase in spurious complexity and correlations.http://www.tylervigen.com/spurious-correlations

http://www.tylervigen.com/spurious-correlations


Formal reasoning with IQ logic

Definition of negation

• not(true) = false ; not(false) = true

• not(not applicable) = unknown ; not(unknown) = not applicable

Definition of IQ order <iq

• not applicable <iq false <iq unknown <iq true

Definition of and

• and(a,b) = minimum<iq(a,b)

Definition of or

• or(a,b) = maximum<iq(a,b)


IQ logic is a language for fact-based semantic modellingfor humans and software tools

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Improving Interoperability and Trustworthiness of ... · health information exchange using blockchain technology ... Improving Interoperability and Trustworthiness of Healthcare Data