What can the industry learn from financial services?


Of jon payne, Manager – Sales Engineering, InterSystems

As the healthcare industry continues on its digital transformation (DT) journey – which has only accelerated since the pandemic began – it has much to learn from other sectors that have followed a similar path. Maybe no more than financial services. Both sectors have been under pressure to digitize faster, but they also operate in highly regulated environments that have historically been fairly isolated.

Changes in the way they are regulated are now helping to grease the digital wheels of these industries. In financial services, Open Banking was launched in 2018, which has really helped level the playing field for new entrants by facilitating the secure exchange of customer data between companies. Now a similar change is imminent in healthcare. From July 2022 all 42 Integrated Care Systems (ICSs) across England are due to become new statutory bodies. This will represent a major shift in the way health and care services are planned and delivered, away from the model of fragmentation and competition that has been followed for decades, and towards a model of collaboration between services.

However, for healthcare to benefit from these organizational changes, the industry needs to start using the right technology. Proper integration and innovation relies on the coding, processing, and analysis of huge and complex datasets encompassing millions of data points that currently exist in online and offline environments. The financial services sector — which is several years ahead of healthcare on its DT journey — has already made great strides here, leveraging data through technology to drive cost savings, create new products and services, and attract new customers. As we now approach a major evolution in healthcare, here are some of the lessons the industry can learn from finance organizations to achieve a similar outcome and set themselves up for success:

1. Promoting interoperability

The emergence of open banking in financial services meant that banks were required to open their application programming interfaces (APIs) to allow third parties access to financial information needed to develop new apps and services, and to give account holders more options for financial transparency to offer. However, a lack of standardization between the APIs has meant that this has so far not provided the data liquidity hoped for by the industry. In contrast, in healthcare there are institutions like HL7 and IHE that have a strong focus on true interoperability and, by and large, also have the support of healthcare organizations and suppliers who understand that it can solve some of their own technical problems. Although there are still limits to how far some suppliers are willing to go when it comes to data sharing, the NHS is asking and listening.

However, the NHS is a complex web of institutions and this is not the technology itself, but the main obstacle to the interoperability of all its interconnected organisations. To facilitate data sharing, it must adopt a flexible attitude towards technology and standards, ensuring that one can support the other and that budgets can be used effectively to achieve interoperability in months rather than years, without then technical debt to cause in the connection areas. For example, the digitization of handover of care stalled because the definition of the standard was so complex that people found it extremely difficult to implement.

2. Leverage AI to streamline existing workflows and make better decisions

Financial service providers have adopted artificial intelligence (AI) to lower the cost of doing business and reduce risk by minimizing the number of people processing transactions and interacting with customers. It has also enabled organizations to make predictable decisions based on data. As an industry, financial services firms are investing in IT in general, including AI, much larger than in healthcare, certainly as a percentage of their total budget. Most organizations see competitive advantages in having the best algorithms or systems and the most efficient way to execute in the business. To that end, every major bank has a significant data science team, and many of those teams are now setting up AI Ops — a DevOps pipeline that’s all about deploying AI models in enterprises. Having tools and platforms that can support the adoption of updated models is key to making AI an integral part of the business.

While budgets are certainly leaner in healthcare organizations across the NHS, the premise is the same. AI can significantly improve the way we arrive at recommendations for clinicians and improve care. What is needed is a loop that helps to quickly and appropriately get data from operating systems into machine learning environments in a useful form, and then feed the output back into operations. Making this simple and practical is key to making AI useful in a general healthcare setting.

3. The security and protection of customer or patient data at all costs

Healthcare organizations want to ensure their data is open and accessible, but there are also lingering concerns about data security, privacy and governance – similar to financial services. A series of high-profile data loss incidents in the public and private sectors have only exacerbated this. Incorrect data protection can result in commercial, defamatory, regulatory, and legal penalties. As such, certain technical innovations understandably need to be approached with caution. This is one reason why cloud adoption, for example, is still happening on-premises or in a private cloud rather than a public one.

Like health data, financial data is sensitive and highly regulated. To stay compliant, it is imperative that healthcare organizations like financial services firms implement robust data governance strategies and compliance frameworks Good In front Tech innovations are deployed.

As the healthcare sector approaches its impending transformation in the planning and delivery of health and care services, it is crucial that individuals and organizations alike seize the opportunity to look outside. There is a wealth of learning from other sectors that have followed a similar path and I am certainly excited to see what DT goals could be achieved through greater cross-sector collaboration and knowledge sharing.


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