Paul Galvin

US-style health insurance will accelerate AI uptake in our hospitals – Dr Paul Galvin, Tyndall National Institute

Submitted on Tuesday, 14/05/2024

Healthcare systems the world over are under huge pressure as rates of chronic disease rise, along with the cost of drugs and procedures.  Meanwhile skills shortages in key specialisms, particularly in Europe, are creating further challenges to the provision of care. The result? Long waiting lists for  procedures, delayed diagnostics and treatments, poor outcomes for patients.

Ageing populations in the west herald an increase in pressure on limping systems over the coming decades. Change is coming – albeit slowly – and there are two main change agents: insurance companies and digital health development.

Traditional systems of reimbursement by insurance companies go a little something like this. When a patient is admitted to hospital for a procedure, the payout from their insurance company is based not just on the procedure but on the number of nights spent in hospital afterwards and any follow-on care that might result from poor outcomes.

With this model of reimbursement, a poor outcome is not just disadvantageous to the patient, it puts the insurance company out of pocket too. While healthcare providers want speedy recovery for patients from a professional point of view it has not, until now, been a financial consideration. If the patient has to spend ten nights in a hospital bed, that bed will be paid for.

This model is starting to change. In the US, the model now is to pay a fixed sum for a procedure and let the hospital find ways to make sure that money covers the entire cost of the treatment. In that instance, ensuring that the patient is discharged quickly, and does not end up back again because of a poor outcome, is a matter of financial life or death for the healthcare provider.

That’s where digital health and AI are starting to play a role, at least in the US – more slowly in Europe. Hospital administrators are motivated now to seek ‘value-based’ systems and they are increasingly turning to medtech companies to provide these. A value-based system is designed to optimise outcomes for patients by improving and shortening the clinical pathway.

There are four main ways that AI can support this – I call them the four Ps: prediction, prevention, personalisation and participation.

Prediction is easy to understand: early diagnosis of disease results in fewer interventions and more impactful ones. AI is a very sophisticated spotter of anomalies, and with expert human oversight, can deliver more accurate screening results.

Prevention, again, is straightforward. AI’s role here is a holistic one. The rapid development of wearable and implantable sensors means that we have the technology to monitor our physical biomarkers, such as blood pressure, frequently and outside clinical settings. Many of us are already monitoring ourselves via smartwatches. This info can inform local health clinicians and flag markers that might need attention before disease sets in.

Once disease is present and treatment required, personalisation is the pathway to better outcomes. We are all different, and require different dosages, interventions, therapies. Again, digital health and AI have a role to play here, employing sensors to supply personalised information to clinicians about a patient’s reaction to treatment. Sensor tech does not just measure traditional physical biomarkers such as heart rate and blood pressure, it is increasingly effective at measuring biochemical markers (for cancer, for example) and electrophysiological markers (cardio). A ‘closed-loop’ system is one where AI-on-the-edge (a sensor that has the capacity to perform its own AI-decision making) is attuned to the individual and capable of informing decision-making without needing leave the system (to the cloud, for example) at all.

The final P, participatory, empowers the patient to monitor their own progress via AI-enabled sensors, and adjust their behaviours accordingly. Recovery at home with a supportive digital health system feeding into the dashboard of a local healthcare provider is the holy grail here.

Each of these digital and AI interventions has the capacity to shorten the healing pathway and reduce costs. Add to these the rapid development of AI-powered surgical tools, robotics, augmented reality in theatres and the scope for remote treatment of patients by specialists anywhere in the world, and it all adds up to long term savings. Little wonder then that US hospitals are proactively partnering with medtech companies to help them with, or in some cases take over, aspects of patient care.

It’s a slower transition in Europe for a number of reasons but here are two. Centralised reimbursement in the US means that the changing payment protocols are impacting right across the system. In Europe the insurance sector is far more fragmented; system-wide change of the sort that drives investment decisions is more challenging.

The other obstacle is an important one – data protection regulation in Europe is tighter and when it comes to personal data it doesn’t get much more personal than health data. GDPR regulation is a valuable protective principle here, as well as the incoming European AI regulation.

How soon will we see change? A €30 catheter that isn’t functioning optimally should be replaced by a smart catheter that costs a great deal more. Right now, when making investment decisions, hospitals administrators have bigger fish to fry. However, once the reimbursement models start to change, that cheap catheter will start to look like a financial liability rather than a money saver. That works for everyone.

By Dr Paul Galvin,  head of the ICT for Health Strategic Programmes, head of the Life Sciences Interface Group, head of the Bioelectronics Cluster at the Tyndall National Institute and Funded Investigator with the Insight SFI Research Centre for Data Analytics