Articles

AI could transform diabetes management to improve patient outcomes for many, but can it pave the way for future AI use in healthcare?

August 24, 2022

Introduction

Around the world, it is estimated that 422 million people have diabetes, that’s almost 20% of the global population1. Diabetes is a chronic condition where a patient’s blood glucose level is too high. This happens if your body doesn’t produce enough insulin or the insulin that it produces isn’t effective.  For all type 1 diabetics and around an 8th of type 2 diabetics2 the treatment is self-monitoring of blood glucose through ‘finger-prick’ blood tests and self-administering the required dose of insulin to try to keep blood glucose levels within range. This means patients are having to make many treatment decisions on the insulin dose frequency and amount. Miscalculations occur frequently which can lead to hypoglycaemic events if too much insulin is administered, this can be dangerous and lead to hospitalisation. Furthermore, regular poor blood sugar control can lead to several long-term complications including nerve damage, kidney damage and sight loss, among others3.

Advances in artificial intelligence (AI) are promising to revolutionise diabetes care by collecting real time data to aid both the diagnosis and treatment. On a simple level AI can be described as “leveraging computers and machines to mimic the problem-solving and decision-making capabilities of the human mind”4. AI is being leveraged within diabetes to improve5:

  • Predictions on who will develop diabetes – based on genetic factors and previous health records
  • Glycaemic control – such as the development of the so called “artificial pancreas” which aims to automate insulin dose based on continuous glucose monitor (CGM) data
  • Prediction of glycaemic events – make hypoglycaemia and hyperglycaemia predictions based on CGM data
  • Detection and diagnosis of diabetes related complications – Prediction of risk of retinopathy, nephropathy, neuropathy or cardiovascular events by using baseline clinical and biochemical data and diagnosis classification of some complications by AI analysis of images

Here we discuss hybrid closed loop systems, one of the key AI driven technologies to come to market in recent years. We outline what they are, their benefits and drawbacks and some of the challenges they have faced in gaining market access.

Hybrid Closed Loop Systems

What are they?

Closed loop systems, also sometimes referred to as an artificial pancreas, monitor blood glucose levels through a CGM. CGMs consist of a tiny sensor under the skin of the abdomen or the arm and are covered with an adhesive patch to hold the transmitter, which sends data from the CGM  to a control algorithm, which instructs an insulin pump to administer a specific insulin infusion rate. Insulin pumps are about the size of smart phones and house the algorithm to deliver insulin and the insulin itself. They are attached to a patient with a needle or a cannula which goes under the skin usually on the abdomen and is held in place with an adhesive patch. Thus, the closed loop system removes the need for the patient to monitor their own blood glucose levels and determine the correct dose of insulin. Current closed loop systems are referred to as ‘hybrid’ because patients are still required to calculate bolus insulin before meals as currently available insulins do not act quickly enough to be able to wait for the CGM to detect rising blood glucose levels after a meal. There are several different insulin pumps that have been approved for use in the UK including the mylife Omnipod, Minimed 640G/ 670G, and the Aviva combo amongst others.

Figure 1: Schematic of a hybrid closed-loop insulin delivery system. Glucose levels are read by the CGM and sent via Bluetooth to the control algorithm which is hosted on an insulin pump (or smartphone). The algorithm calculates the amount of insulin required and instructs the insulin pump to deliver it. The system continuously modulates the insulin delivery every 5–10 minutes6.

What are their benefits?

Prior to the use of hybrid closed loop systems, patient management of their diabetes was very time intensive with diabetics or their carers/parents having to regularly take glucose measurements and make calculations on insulin dose. The hybrid closed loop system has a number of benefits over this7:

  • Reduced time spent managing the disease
  • Reduced anxiety over ensuring glucose readings have been taken and calculating the insulin dose
  • More precise and more frequent insulin dose given, keeping blood sugar more stable
  • As blood sugar is more stable hypos are reduced, HbA1c is lowered, and risk of diabetes complications are lessened

How are they funded?

In England, medical devices, such as the components of the hybrid closed loop system, will have to be registered on HealthTech Connect to be considered for a NICE evaluation. If selected the devices will be assessed through the medical technology evaluation program8. However, unlike with technology appraisals a positive recommendation does not lead to a funding mandate and each ICS will decide individually whether to fund the product.

On top of this, the large price tag associated with hybrid closed-loop systems mean they are not available to all comers. In the UK, for example, unless acquiring privately, guidelines states that closed-loop systems are not available for any type 2 diabetics and are only routinely given to type 1 diabetics who are either9:

  • Less than 12 years of age
  • Have disabling hypoglycaemia when trying to reach their target HbA1c levels.

However, this method of reimbursement where the AI has been bundled with the hardware is more established than if reimbursement was sought for the AI as a standalone digital therapeutic. Unlike some countries such as Germany and Belgium with their DiGA and mHealthBelgium processes, the UK system to reimburse digital therapeutics is still maturing with NICE reforming the process in the near future10.

You can read more about what is required for digital therapeutic reimbursement in the UK and how they are funded in our previous articles.

Will access increase to the wider diabetes community?

The use of AI in the regulation of blood glucose levels undoubtedly shows promise for both short-term control and long-term outcomes. However, as with all medical technologies, the ability to demonstrate long-term benefits and convince reimbursement authorities that theoretical benefits will indeed play out in practice is difficult. It is therefore not surprising that access to the closed loop system is currently restricted to a narrow group of patients. However, the NHS is currently trialling the closed loop system in 1000 type 1 diabetics, with the aim of assessing their value in a range of different types of patients11. If they are shown to be beneficial to those patients there may be an expansion of the eligibility criteria for these types of devices.

Though this trial is hopeful, the NHS will usually only think about their funding and budgets for two years ahead with future cost savings being put even further to the back of minds of hospital directors as they have to deal with the aftermath of the pandemic and the effects on their budgets. As diabetes is a chronic disease with complications developing many years after diagnosis, savings from devices such as the hybrid closed loop system are not likely to be seen for a while, despite avoidable diabetes complications costing the NHS £7.7 billion per year12. Until the prices come down for these systems, manufacturers may have to better demonstrate cost savings associated with reduced hypoglycaemic events in the short term, reduced resource usage in well managed patients and show the improved quality of life of patients with these systems, to secure wider access. Therefore, it isn’t expected that the access of hybrid closed loop systems will increase massively in the near future.

Moreover, no such trial is in place for insulin requiring type 2 diabetics which makes up most of the insulin requiring diabetic population in the UK and instead the NHS long term plan has announced that they plan a major expansion of a diabetes prevention programme13.

Conclusion

The technology driving AI is fast moving and though the health care sector has been slow to adopt AI to improve patient outcomes it is becoming more accepted in the field of diabetes. Though most diabetes devices that use AI currently come with high price tags, which are at least partially responsible for the limited access to patients, the value of these AI driven technologies is beginning to be more thoroughly investigated. We can assume that the use of AI in diabetes is the first step in it becoming embedded in the treatment pathways for numerous other diseases. As organisations such as NICE update their processes to accommodate the new technology, the hope is that the pathway to reimbursement will become clearer and pave the way for AI to be adopted in other areas of healthcare. As more people become used to AI in their daily lives from text and voice recognition to facial recognition to unlock your phone, it might be anticipated that patients and healthcare systems also become more comfortable employing AI to help manage their diagnosis, treatment, or administrative processes.


Sources:
  1. Diabetes. https://www.who.int/health-topics/diabetes#tab=tab_1. Accessed 27th July 2022.
  2. Basu, Sanjay et al. “Estimation of global insulin use for type 2 diabetes, 2018-30: a microsimulation analysis.” The lancet. Diabetes & endocrinology 7 1 (2019): 25-33
  3. Complications of Diabetes. https://www.diabetes.org.uk/guide-to-diabetes/complications. Accessed 5th August 2022.
  4. Artificial Intelligence (AI). https://www.ibm.com/cloud/learn/what-is-artificial-intelligence. Accessed 27th July 2022.
  5. Singla R, Singla A, Gupta Y, Kalra S. Artificial Intelligence/Machine Learning in Diabetes Care. Indian J Endocrinol Metab. 2019 Jul-Aug;23(4):495-497.
  6. Hartnell S, Fuchs J, Boughton C, Hovorka R. Closed-loop technology: a practical guide. Practical Diabetes. 2021 Aug;38(4):33-39.
  7. Closed loop systems (artificial pancreas). https://www.diabetes.org.uk/guide-to-diabetes/diabetes-technology/closed-loop-systems#:~:text=Benefits%20of%20hybrid%20closed%20loop%20systems&text=This%20can%20reduce%20hypos%20and,a%20better%20quality%20of%20life. Acccessed 3rd August 2022
  8. How we develop medical technologies guidance. https://www.nice.org.uk/about/what-we-do/our-programmes/nice-guidance/medical-technologies-guidance/how-we-develop. Accessed 3rd August 2022.
  9. Treatment Summary: Type 1 Diabetes. https://bnf.nice.org.uk/treatment-summary/type-1-diabetes.html. Accessed 27th July 2022.
  10. NICE’s Early Value Assessment for Medtech: panning for nuggets of innovation gold. https://www.nice.org.uk/news/blog/nice-s-early-value-assessment-for-medtech-panning-for-nuggets-of-innovation-gold. Accessed 27th July 2022.
  11. NHS announces piolet fir hybrid closed loop tech in England.  https://www.diabetes.org.uk/about_us/news/closed-loop-systems. Accessed on 3rd August 2022
  12. The Cost of Diabetes Report. https://www.diabetes.org.uk/resources-s3/2017-11/diabetes%20uk%20cost%20of%20diabetes%20report.pdf. Accessed 3rd August 2022.
  13. NHS Prevention Programme cuts chances of Type 2 diabetes for thousands. https://www.england.nhs.uk/2022/03/nhs-prevention-programme-cuts-chances-of-type-2-diabetes-for-thousands/. Accessed 3rd August 2022.

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