New technology in health services: EHRs and AI

The changing face of healthcare delivery involves trends like electronic health records (EHR) being used for underwriting purposes, and AI in the design of tailor-made care regimes delivered by virtual assistants. Even surgery can be performed virtually, with physicians in one location involve real-time in surgical procedures taking place on the other side of the world.

Specialty: A main challenge common to the digitalisation of medical records and the use of technologies like EHR is data security and privacy, as the systems access large amounts of sensitive patient information. This raises concerns about data breaches, unauthorised access to or misuse of information, violating privacy and compromising patient confidentiality. Such outcomes could boost demand for cyber insurance covers.

Casualty: The virtual realm extends to surgical procedures performed remotely, in real time and with advanced robotics. This is with the development of extended reality (XR) technology, including virtual reality (VR), augmented reality (AR) and mixed reality (MR) technologies. Use cases are still mostly in the early design and pilot phase. Platforms with these technologies can enable surgeons to collaborate with teams in other countries, overcoming geographical barriers and ensuring patients receive specialised care regardless of their location. ^1 2 The use of robotics in surgery could improve patient outcomes (eg, faster recovery times) and operational efficiencies for medical practitioners.

However, integrating XR technologies and robotics in surgery raises challenges too, such as hardware limitations, software complexities, computing power constraints, and wireless communication issues. Another issue is the reliability of AI systems due to limitations in the algorithms or biases inherent in training data, in situations where these lead to wrong diagnosis or treatment recommendations. Errors like these could have serious consequences for patient health. ³

There are also concerns around clinical validation and user acceptance as surgeons adapt to new technologies. Potential diagnostic gaps and mis-judgment from reduced doctor-patient interaction could trigger medical malpractice claims. Given the pace of technological advances and growing acceptance of robotic-assisted surgery, the associated risks could result in an increase in demand for, but also claims in, liability insurance. For instance, liability disputes involving manufacturers, software developers and medical practitioners pose challenges in pinpointing accountability in cases of error or failure. Any process failures on account of use of new technologies that lead bodily injury or even death, could trigger product liability and professional indemnity claims.

L&H: The use of AI and virtual technologies can facilitate tailor made coverage options for policyholders and better treatment outcomes. AI-driven personalised health monitoring can lead to healthier insured populations by enabling early detection and preventative interventions, thereby potentially reducing claims frequency and payouts. Virtual assistants can offer personalised health advice, screening and lifestyle suggestions based on AI-driven precision medicine. ⁴ This supports a move to prevention rather than cure. In mental health services, AI algorithms are used to analyse features such as speech patterns, facial expressions and other behavioural cues, during virtual consultations. The analysis can add nuanced value to diagnosis and facilitate more personalised treatments for conditions like depression, anxiety and mood disorders. ⁵ On the downside, new technologies can also introduce risks such as misdiagnosis, potential for failures in technology and over-reliance on automated systems.

Insurance operations: To date, EHR have facilitated better access to patient information, efficiency gains in documentation and record-keeping, and improved communication and coordination among healthcare providers. ⁶ Taking the utility one step further, the vast data sets in EHR, alongside images and other biomedical sources can also be used in underwriting to improve the precision of risk selection and pricing. However, as new technologies become integrated, insurers will need specialised knowledge to manage claims. For example, traditional diagnosis is based on symptoms and clinical observations. Advancements in AI and use of EHR shifts the basis to understanding of genetic, molecular and other detailed biological data.