19 OpenTox Principles and Best Practices for Trusted Reproducible …
397
19.10 Enhancing Workflow Solutions Including Trust
and Data Provenance Using Blockchain Technology
Applied to Healthcare Applications
In addition to the above best practices in reproducible workflows, we are currently
exploring the use of blockchain technology to add an independent verification of trust
and data provenance. We are comparing the use of the major blockchain platforms
in their utility for trust and provenance goals. In particular, we have commenced a
collaboration with Guardtime where the technology has already been successfully
used to secure electronic health records in Estonia. The goal is to take the above
QSAR and ITS models and workflows and run them on the enterprise blockchain to
provide signatures of execution history so as to secure the evidence that would be
generated and submitted to a regulator. The case of preclinical evidence provides a
suitable starting case study framework for healthcare applications, which we plan to
subsequently extend to clinical data.
The vision of our work is to establish a trusted healthcare blockchain ecosystem supporting transparent and reliable data exchange, as well as provenance and
completeness of data sharing, between all stakeholders so as to maximize patient
benefits and outcomes. Our case study work draws upon the above preclinical flows
executed by the Edelweiss Connect team in collaboration with the fully functioning
Estonian Ministry of Health data sharing platform from Guardtime which harnesses
blockchain to allow patients full, secure control of their data assets; the Guardtime
technical team has built these systems for the Estonian Health Ministry. The provenance of data and management of consent is critically important. To deliver trust
in the results (of machine learning and AI), there must be trust in the underlying
data (authenticity and integrity). We see Blockchain as a trust layer to take us to the
next level of knowledge about human biology. Blockchain can also serve as a trust
layer in the sense of providing assurance of “how things are done.” For example,
did the supplier of an active substance produce it according to the set standards,
or did the distributors handle the product (e.g., with temperature sensitivity) with
sufficient care? Clinical trials may be sped up if source data verification can be done
automatically.
In addition to providing a trusted interoperability layer for data sharing, we also
believe this framework will additionally enable applications supporting goals such as
regulatory acceptance of new methods and evidence, outcome-based contracts and
moving toward patient-centric value-based healthcare.
In the healthcare and pharma sectors, regulatory oversight and patient privacy
issues provide strong regulatory and legal requirements on the use of new Blockchain
technology and related operation models such as decentralized networks that have to
be fulfilled 100%. The implementation of GDPR in Europe has raised the awareness
of data privacy, but operators are struggling to find workable models for “the right
to be forgotten” and dynamic consent (where it is not only yes/no and when preferences may change over time). As healthcare regulation, including Global Patient
Registration (GPR), implementation is often a national or even regional competency,
397
19.10 Enhancing Workflow Solutions Including Trust
and Data Provenance Using Blockchain Technology
Applied to Healthcare Applications
In addition to the above best practices in reproducible workflows, we are currently
exploring the use of blockchain technology to add an independent verification of trust
and data provenance. We are comparing the use of the major blockchain platforms
in their utility for trust and provenance goals. In particular, we have commenced a
collaboration with Guardtime where the technology has already been successfully
used to secure electronic health records in Estonia. The goal is to take the above
QSAR and ITS models and workflows and run them on the enterprise blockchain to
provide signatures of execution history so as to secure the evidence that would be
generated and submitted to a regulator. The case of preclinical evidence provides a
suitable starting case study framework for healthcare applications, which we plan to
subsequently extend to clinical data.
The vision of our work is to establish a trusted healthcare blockchain ecosystem supporting transparent and reliable data exchange, as well as provenance and
completeness of data sharing, between all stakeholders so as to maximize patient
benefits and outcomes. Our case study work draws upon the above preclinical flows
executed by the Edelweiss Connect team in collaboration with the fully functioning
Estonian Ministry of Health data sharing platform from Guardtime which harnesses
blockchain to allow patients full, secure control of their data assets; the Guardtime
technical team has built these systems for the Estonian Health Ministry. The provenance of data and management of consent is critically important. To deliver trust
in the results (of machine learning and AI), there must be trust in the underlying
data (authenticity and integrity). We see Blockchain as a trust layer to take us to the
next level of knowledge about human biology. Blockchain can also serve as a trust
layer in the sense of providing assurance of “how things are done.” For example,
did the supplier of an active substance produce it according to the set standards,
or did the distributors handle the product (e.g., with temperature sensitivity) with
sufficient care? Clinical trials may be sped up if source data verification can be done
automatically.
In addition to providing a trusted interoperability layer for data sharing, we also
believe this framework will additionally enable applications supporting goals such as
regulatory acceptance of new methods and evidence, outcome-based contracts and
moving toward patient-centric value-based healthcare.
In the healthcare and pharma sectors, regulatory oversight and patient privacy
issues provide strong regulatory and legal requirements on the use of new Blockchain
technology and related operation models such as decentralized networks that have to
be fulfilled 100%. The implementation of GDPR in Europe has raised the awareness
of data privacy, but operators are struggling to find workable models for “the right
to be forgotten” and dynamic consent (where it is not only yes/no and when preferences may change over time). As healthcare regulation, including Global Patient
Registration (GPR), implementation is often a national or even regional competency,