Aligning incentives with blockchain technology
The individual incentives of the producers (researchers), intermediaries (EHR vendors) and users (healthcare providers) are currently unaligned. The academic environment or EHR market has not incentivised the conversion of technical discovery to integrated product development, limiting the ‘bench-to-bedside’ pathway of CRPMs. To prevent a similar experience for AI models, we must develop strategies that align incentives and create a value proposition for all involved parties.
A potential solution could be a national infrastructure; a marketplace for models, all clinically validated and compliant with medical device regulations. Blockchain, a form of distributed ledger technology (DLT), may facilitate such an infrastructure by securely hosting the marketplace and allowing the producers to be remunerated when their model is used through smart contracts.
Blockchain is an open network of distributed data stored in secure blocks, which are available to all participants (known as ‘nodes’) on a network.7 By distributing blocks across all nodes, the data in the network is difficult to hack, change or corrupt, creating a traceable, immutable and secure record of transactions between nodes.8 Blockchain has therefore been widely discussed in the context of sharing electronic patient records.9 Smart contracts are a digital technology that execute an financial transaction recorded in a blockchain when a predefined condition is met.10
Blockchain and DLT could support the implementation and financial reward for CRPMS: models could be published to the national marketplace, hosted on the blockchain and clinical data could be entered securely to receive results with a micro-payment triggered at every use, via smart contracts. Defining a national vendor-neutral API standard for models would make the marketplace accessible from all EHRs that implement it. A recognised body could regulate this process alongside an established framework, such as the UK government’s guide to good practice for digital and data-driven health technologies.11 The traceability provided through a DLT-based solution would build trust among all stakeholders and allow a shared interest to develop.
An example of a CRPM that this could apply to is the CHA(2)DV(2)-VASc score, which is used to predict the risk of stroke in patients with atrial fibrillation, and thus guide the need for blood-thinning medication.12 The producers would publish their model to the marketplace, who would take the responsibility of assessment conformity and regulatory approval. Once the model is live, EHR vendors could integrate it into their interface using the standardised API.
This would increase the use of CRPMs by clinicians as they are incorporated into their workflow, provide a monetary incentive for researchers to pursue models to implementation and integration and finally, make EHRs that integrate CRPMs more attractive for healthcare providers to procure. Figure 1 illustrates this concept, highlighting how blockchain technology can align incentives and operationalise current and future CRPMs and AI models.
The traditional medical research path is linear with rigid objectives and little concern for commercialisation. However, it is evidence-focused and rightly, prioritises safety and regulation. In contrast, technology development is agile, iterative and focused on real-world application.13 There remains a need to create a joint culture across academic and industry stakeholders to harmonise expertise and develop meaningful digital health solutions. Recent efforts, such as the proposed Decision-support systems driven by artificial intelligence guidelines, support this by calling for early clinical evaluation with a view to bridging the current implementation gap of AI models.14
The introduction of Chief Clinical Informatics Officers and digital strategies by healthcare providers will help regulate and adopt these technologies going forward,15 however, a collaboration across the vendor industry remains essential. A drive towards business success may incentivise researchers, vendors and healthcare providers appropriately to pursue solutions and achieve intended benefits. Interdisciplinary and cross-industry health research, with a long-term focus on clinical impact can thus unlock the potential of CRPMs and AI, leading to radical change in patient care and outcomes.