About this Research Topic
Global healthcare’s most pressing concern is the simultaneous increase in the complexity of disease and the rising costs of healthcare services and pharmaceutical drug development. Together these exacerbate the already burdened systems facing heavy tension. We can see the ripples of this tension in times of crisis when immediate and urgent needs for effective healthcare delivery arise.
Concurrently, there’s a drive towards effective targeted medicine. This involves directed approaches for specific patient subgroups to reduce adverse events and costs, and to improve patient well-being sooner and more effectively. This type of disease management requires the incorporation of numerous genetic associations, and various epigenetic and environmental factors such as diet, exercise, smoking habits, in addition to other lifestyle factors. Such a vast range of relevant information makes it challenging to understand the direct mechanisms of disease. This is further complicated by heterogeneous patient populations where individuals present with various degrees of pathology, making it difficult to model these diseases adequately in preclinical settings to enable effective and rapid drug discovery.
Together this leads to a greater need to be able to effectively collect and assimilate data from patients and across systems to enable relevant and focused analysis and outcomes measurement. However, existing data sources are often untapped, inaccessible, or siloed, and therefore not amenable to assimilation into larger datasets. Many processes are either inadequate or lacking to be able to do this effectively. These include clinical trial data, genomic datasets, health records, and patient-generated data, all of which can be used to derive biomarkers of disease and treatment targets. Moreover, newly gathered data is slow to assimilate with existing sources to make effective connecting links and there is little existing inter-organizational collaboration for data sharing; yet all this can aid in more rapid clinical decision making, diagnostics, and drug discovery and development.
There’s much scope for improvement in effectively combining datasets, cleaning up data, and extracting information that is of value to health systems stakeholders and pharmaceutical companies. Blockchain and distributed ledgers are technological tools that can be leveraged to better collect, manage, share, and disseminate healthcare data, for example via secure and trusted information highways. This can enable the unlocking of data siloes, increasing interoperability and rapid collaboration when and where they are needed most, as well as delivering novel insights to provide effective solutions to be fed back into healthcare systems, industry, and ultimately patients.
The focus of this Research Topic is on the implementation of blockchain in precision medicine ecosystems to describe how pharma, diagnostics companies, clinical systems, and patients can benefit from solutions that can efficiently deliver more relevant and targeted healthcare. Potential topics include, but are not limited to:
• Data and information highways
• Patient data sharing systems
• Institutional data sharing systems
• Open data systems and platforms
• Trusted data systems and platforms
• Enabling analytics pipelines assembly
• Federated learning systems
• Clinical trials
• Healthcare delivery optimization
• Self-sovereign patient records
• Pharmaceutical discovery
• Drug development systems and processes
• Regulatory, ethical and information governance considerations
• COVID-19 response and learnings
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Disclaimers:
- Topic Editor Natalie Pankova is employed at Metadvice, a digital health company that is developing clinical decision support applications with technologies including distributed ledger. She is also an Advisor for Blockchain Reply and sits on the Blockchain and Distributed Ledger Technologies committee at the British Standards Institute.
- Topic Editor Jonathan Passerat-Palmbach is employed by the company ConsenSysHealth, which focuses on privacy-preserving machine learning and decentralization.
- Topic Editor Mirko De Malde is CEO of Tharisis, a consultancy company focusing on digital transformation with a focus on healthcare.
Concurrently, there’s a drive towards effective targeted medicine. This involves directed approaches for specific patient subgroups to reduce adverse events and costs, and to improve patient well-being sooner and more effectively. This type of disease management requires the incorporation of numerous genetic associations, and various epigenetic and environmental factors such as diet, exercise, smoking habits, in addition to other lifestyle factors. Such a vast range of relevant information makes it challenging to understand the direct mechanisms of disease. This is further complicated by heterogeneous patient populations where individuals present with various degrees of pathology, making it difficult to model these diseases adequately in preclinical settings to enable effective and rapid drug discovery.
Together this leads to a greater need to be able to effectively collect and assimilate data from patients and across systems to enable relevant and focused analysis and outcomes measurement. However, existing data sources are often untapped, inaccessible, or siloed, and therefore not amenable to assimilation into larger datasets. Many processes are either inadequate or lacking to be able to do this effectively. These include clinical trial data, genomic datasets, health records, and patient-generated data, all of which can be used to derive biomarkers of disease and treatment targets. Moreover, newly gathered data is slow to assimilate with existing sources to make effective connecting links and there is little existing inter-organizational collaboration for data sharing; yet all this can aid in more rapid clinical decision making, diagnostics, and drug discovery and development.
There’s much scope for improvement in effectively combining datasets, cleaning up data, and extracting information that is of value to health systems stakeholders and pharmaceutical companies. Blockchain and distributed ledgers are technological tools that can be leveraged to better collect, manage, share, and disseminate healthcare data, for example via secure and trusted information highways. This can enable the unlocking of data siloes, increasing interoperability and rapid collaboration when and where they are needed most, as well as delivering novel insights to provide effective solutions to be fed back into healthcare systems, industry, and ultimately patients.
The focus of this Research Topic is on the implementation of blockchain in precision medicine ecosystems to describe how pharma, diagnostics companies, clinical systems, and patients can benefit from solutions that can efficiently deliver more relevant and targeted healthcare. Potential topics include, but are not limited to:
• Data and information highways
• Patient data sharing systems
• Institutional data sharing systems
• Open data systems and platforms
• Trusted data systems and platforms
• Enabling analytics pipelines assembly
• Federated learning systems
• Clinical trials
• Healthcare delivery optimization
• Self-sovereign patient records
• Pharmaceutical discovery
• Drug development systems and processes
• Regulatory, ethical and information governance considerations
• COVID-19 response and learnings
----
Disclaimers:
- Topic Editor Natalie Pankova is employed at Metadvice, a digital health company that is developing clinical decision support applications with technologies including distributed ledger. She is also an Advisor for Blockchain Reply and sits on the Blockchain and Distributed Ledger Technologies committee at the British Standards Institute.
- Topic Editor Jonathan Passerat-Palmbach is employed by the company ConsenSysHealth, which focuses on privacy-preserving machine learning and decentralization.
- Topic Editor Mirko De Malde is CEO of Tharisis, a consultancy company focusing on digital transformation with a focus on healthcare.
Keywords: Blockchain, distributed, artificial intelligence, machine learning, digital health, medical technology, internet of things, medical device, pharmaceutical, electronic health records, hospital, telemedicine, clinical trials, pandemic, coronavirus, covid, data sharing, trust, ethics, policy
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