HTA for artificial intelligence in health care
Early HTA on the value of an AI-based decision support system in multiple sclerosis
In this report you can read how ‘Health Technology Assessment’ (HTA) can be used to assess the added value of the use of AI applications in healthcare. A decision support application in patients with multiple sclerosis (MS) is hereby developed as a practical case of a valuation of an AI application.
- Artificial intelligence (AI) is a promising technique for using data in healthcare to improve health by optimising treatment options, diagnoses or logistics.
- To substantiate which AI investments are meaningful and which healthcare benefits an AI application can have, and to mobilise parties around the use of AI applications in the healthcare sector, economic evaluations can be used that make the value of AI applications tangible.
- Health Technology Assessment (HTA) offers a framework for carrying out economic evaluations of AI applications. It expresses health benefits in quality-adjusted life years (QALYs). Costs and savings in the healthcare sector and elsewhere are taken into account in this assessment. While HTA offers an objective analysis of the added value of a treatment, a positive HTA is not a guarantee for the uptake of an AI application in practice.
- This method has been applied to a case study of a promising AI application in healthcare: MS sherpa. MS sherpa is a medical device (CE-certified) consisting of a smartphone application and an integrated healthcare provider portal/dashboard. MS sherpa is intended for the monitoring of patients with multiple sclerosis (MS) with the aim of providing patients and their practitioners with personalised insight into the presence and progression of MS-related symptoms.
- The assumed added value of MS sherpa is that this insight into the presence and progression of symptoms will allow a proportion of patients to switch to a more effective drug sooner because the disease activity, in terms of MS relapses or disease progression, is detected early.
- The added value of the MS sherpa application has not yet been proven; hence, this report works with the application’s potential added value on the basis of assumptions: a so-called early HTA.
- The results of the early HTA show that the MS sherpa application can be a cost-effective addition to standard of care (more health for an acceptable level of higher costs). The application can be costeffective (better health and fewer costs) if favourable assumptions about effectiveness are made and if benefits outside of healthcare, such as labour productivity, are taken into consideration.
- The early HTA of the use of MS sherpa shows that the application leads to better health and larger healthcare costs. However, when weighing the benefits of better health that are incurred outside of the healthcare sector, such as labour productivity or informal care, MS sherpa can save costs for society: the additional healthcare costs are smaller than the benefits outside of healthcare.
- MS sherpa may have broader effects on patients, positive (e.g., stimulating self-efficacy and shared decision-making) as well as negative (e.g., being confronted with one’s illness and being reluctant to share data). Broader effects of this kind are only weighed in an HTA if they concern patients’ healthrelated quality of life.
- Whether the smart use of MS sherpa can contribute to the early detection of disease activity in MS patients and can thus influence treatment decisions has yet to be studied in clinical practice. In the short term, a clinical study will start at the MS Center of Amsterdam UMC to study this and other potential benefits of MS sherpa.
- Those who want to carry out an HTA will need to make decisions regarding, among other things, the perspective applied, the analysis technique and the structure of the data collection.
- Investing in carrying out an HTA has the most added value if it needs to be determined whether the intervention is to be reimbursed by the basic health insurance package.