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Insurers around the world have significant technology debt, with many core processes hamstrung by inflexible and costly on-premise legacy platforms. A rapid shift to Distributed, Cloud-enabled Infrastructure and Cloud Data Platforms that aggregate and make data more accessible is being widely pursued and adopted across the industry. There are two main driving factors:
Many insurers are still modernizing their technology stacks and are in the early stages of their digital journey, leaving them susceptible to newer entrants whose innovation velocity is not shackled by cumbersome legacy systems. To compete effectively, rapid modernization based on Cloud-centric solutions is imperative.
However, successful transformations have common ingredients in addition to effective use of modern cloud-based technologies. These include:
A central component of a comprehensive and actionable data strategy is the effective use of AI to create competitive differentiation and drive operational efficiencies. Applied Artificial Intelligence puts advanced machine learning to use to more closely emulate human-like judgment. Automating processes, reducing errors, providing higher levels of accuracy, and adaptation over time to predict outcomes are major benefits to both long-term planning and, of course, long-term success. Put simply, Applied AI contextualizes business models. That’s how AI-centric startups, along with compelling initiatives by industry incumbents, are reshaping the healthcare landscape.
AI has already evolved claims processing, with many Payers now achieving auto-adjudication rates well above 80%. Some of the next high-growth frontiers for Applied AI include:
Governing the pace of innovation and adoption in these areas, however, are complexities related to data sharing, data privacy, and security. We must be able to securely and ethically access, integrate, aggregate, and then analyze the data while protecting individual privacy and company IP. How do we solve that challenge?
Confidential Computing is positioned to be the “white horse” for Payers and Providers to saddle and ride toward accelerated innovation. It offers healthcare data providers, AI application developers, clinicians, and data scientists a solution to the practical problem of protecting electronic health data and the intellectual property contained in AI algorithms. Combined, Confidential Computing and privacy-preserving analytics accelerate the development of healthcare algorithms that enable improved Member outcomes.
At its heart, Confidential Computing protects sensitive applications and data from being compromised or tampered with while in use by processing them in a completely isolated trusted execution environment (TEE), often referred to as a “secure enclave”. The secure enclave isolates the data and code in a secure region of the CPU memory to prevent unauthorized access, even if the infrastructure is compromised. It also renders sensitive information invisible to host operating systems, cloud provider administrators, and external attackers. This protection is environment-agnostic, from on-prem to public cloud and edge devices.
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Insurers around the world have significant technology debt, with many core processes hamstrung by inflexible and costly on-premise legacy platforms. A rapid shift to Distributed, Cloud-enabled Infrastructure and Cloud Data Platforms that aggregate and make data more accessible is being widely pursued and adopted across the industry. There are two main driving factors:
Many insurers are still modernizing their technology stacks and are in the early stages of their digital journey, leaving them susceptible to newer entrants whose innovation velocity is not shackled by cumbersome legacy systems. To compete effectively, rapid modernization based on Cloud-centric solutions is imperative.
However, successful transformations have common ingredients in addition to effective use of modern cloud-based technologies. These include:
A central component of a comprehensive and actionable data strategy is the effective use of AI to create competitive differentiation and drive operational efficiencies. Applied Artificial Intelligence puts advanced machine learning to use to more closely emulate human-like judgment. Automating processes, reducing errors, providing higher levels of accuracy, and adaptation over time to predict outcomes are major benefits to both long-term planning and, of course, long-term success. Put simply, Applied AI contextualizes business models. That’s how AI-centric startups, along with compelling initiatives by industry incumbents, are reshaping the healthcare landscape.
AI has already evolved claims processing, with many Payers now achieving auto-adjudication rates well above 80%. Some of the next high-growth frontiers for Applied AI include:
Governing the pace of innovation and adoption in these areas, however, are complexities related to data sharing, data privacy, and security. We must be able to securely and ethically access, integrate, aggregate, and then analyze the data while protecting individual privacy and company IP. How do we solve that challenge?
Confidential Computing is positioned to be the “white horse” for Payers and Providers to saddle and ride toward accelerated innovation. It offers healthcare data providers, AI application developers, clinicians, and data scientists a solution to the practical problem of protecting electronic health data and the intellectual property contained in AI algorithms. Combined, Confidential Computing and privacy-preserving analytics accelerate the development of healthcare algorithms that enable improved Member outcomes.
At its heart, Confidential Computing protects sensitive applications and data from being compromised or tampered with while in use by processing them in a completely isolated trusted execution environment (TEE), often referred to as a “secure enclave”. The secure enclave isolates the data and code in a secure region of the CPU memory to prevent unauthorized access, even if the infrastructure is compromised. It also renders sensitive information invisible to host operating systems, cloud provider administrators, and external attackers. This protection is environment-agnostic, from on-prem to public cloud and edge devices.
