VRAI MEDICAL | CARDIOLOGY

Value-based Reimbursement Model

In the past, major MedTech companies have not been concerned about the impact of insurance reimbursement models. In 2015, however, the U.S. Department of Health and Human Services (DHHS) and Medicare Access and CHIP Reauthorization Act (MACRA) set a goal to link traditional payments to a value-based reimbursement model by end of 2018.4,5 The value-based reimbursement model is likely to replace the fee-for-service model completely, suggesting that doctors will receive their payments based on the outcomes of the treatment for patients. Thus, in order to increase their value-based payments, companies are investing money in developing medical devices that offer superior therapeutic results.

…And let us not forget the Increase in Aging Population in the US

According to a recent report funded by the National Institutes of Health (NIH) and developed by the U.S. Census Bureau, currently 8.5% of the global population, equating to approximately 617 million people, are aged 65 and above.4 This number is expected to more than double by 2050 to 1.6 billion.4,5  An aging population is increasing the need for medical devices, especially those used in the diagnosis and treatment of orthopedic, cardiovascular and eye disorders. In addition, the increase in diabetes, obesity and cardiovascular disorders in elderly patients has accelerated the development of advanced digital technologies such as healthcare apps and wearable devices that enable patients to self-monitor glucose, heart rate and blood pressure.

The Rise of Artificial Intelligence

AI has extensive applications in the fields of drug discovery and medical imaging, being capable of performing tasks that require human-like qualities of judgment and perception.6 Currently, the majority of AI-based technologies are developed by start-up companies. For example, Sense.ly developed the first virtual nurse, Molly, to help people monitor and treat their conditions between doctor’s visits. Major companies such as IBM, Google and Apple are also involved in the development of AI-based technologies, such as the IBM program, Medical Sieve, which has been designed to assist in clinical decision-making in radiology and cardiology.

AI applications for medical devices generally fall into three main categories:

1. Management of chronic disease: Machine learning is used to monitor patients in order to automate the delivery of treatment using connected mobile apps (e.g., diabetes and automated insulin delivery)

2. Medical imaging: AI technologies present multiple opportunities to detect subtle signs of a disease in medical images more accurately than humans. Companies are incorporating AI-driven platforms in medical scanning devices to improve image clarity and clinical outcomes by reducing exposure to radiation and detecting early signs of disease (e.g., GE Healthcare CT scans for liver and kidney lesions; Picture Archiving and Communications [PAC] deep learning algorithm for detecting signs of disease from MRI, CT scans, ultrasound and X-ray)

3. AI and the Internet: Companies are integrating AI and the internet to better monitor patient adherence to treatment protocols and to improve clinical outcomes

Despite its potential for transforming the healthcare industry, AI technology development is currently hindered by:

• Cybersecurity issues

• Challenges of high-volume data storage

• Training of medical personnel

• High cost

• Lack of satisfactory software for rare medical conditions

• Reluctance by patients to adopt new technologies in their treatment plan

Despite such barriers, investments by MedTech companies in the development of AI-based technologies are likely to increase, with AI becoming a standard-of-care for many healthcare providers.