There are a number of major drivers for the development and adoption of AI in Healthcare solutions (Figure.) I would like to think that before we speak about anything else, we start by saying that the fact that more data is available in digital format is the main driver. Without it, we don’t have much to talk about in AI. Then, we can get into topics such as improved machine learning methodology, increased computing power, cloud computing, healthcare resource shortage, opportunity to reduce costs, precision medicine, and more.
I start with technical issues such as the availability of data and increasing computing power because I think without these, the use of AI to solve key issues such as outcome improvement and cost reduction would not be possible. The issues of outcomes and costs are not new and will continue into the distant future if we do not develop and use AI or other technologies to address them. However, given the huge amounts of money we invest in healthcare and the fact that our outcomes are not commensurate with our investments, there is increasing appetite for any technologies that may address these issues.
The primary macroeconomic growth drivers of AI in healthcare include increasing individual healthcare expenses, a larger geriatric population and an imbalance between health workforce and patients. Global expenditures on healthcare increased to 9.9% of total GDP in 2014, up from 9.0% in 2000. The US witnessed the highest expenditure on healthcare, 17.8% of total GDP, in 2015. The world’s population, aged 60 years and above, is likely to grow by 56% from 2015 to 2030. The shift towards an aging population will strain the current healthcare system. Because of these trends, the U.S. has a continuous shortage of nursing and technician staff. The number of vacancies for nurses will be more than 1.5 million by 2025. The trend toward consolidation in the US healthcare has meant that larger health systems that combine hospitals, clinics, and ancillary services are now a dominant form of care delivery. That means more professional management; increased investments in technologies that improve volumes, operations and margins; and lead to competitive differentiation.
AI is positioned to help medical practitioners efficiently achieve their tasks with minimal human intervention, a critical factor in meeting increasing patient demand. Not only that, it can improve the quality of care by digesting data and identifying patterns and helping with diagnosis and treatment selection. It can reduce costs due to more intelligent automation and reduction in mistakes. It can improve care by enabling better diagnosis and treatment selection. All of this means that there is increasing public and private investments to realize the future potential for AI and the regulatory and reimbursement bodies are taking positive steps to facilitate its adoption.