The United States spends undoubtedly a high amount of money on healthcare, nearly 18% of its GDP. Its expenditure is almost double that of other high-income nations like the UK, Germany, Canada, and Australia. Nevertheless, more spending does not equate to better outcomes.
According to studies, many of the nations that have lesser healthcare expenditures compared to the US experience better results in the overall health of their nationals. The Journal of the American Medical Association (JAMA) recently published a new report citing that slightly less than half of the US healthcare expenditure goes into planning, controlling, and managing medical services, especially at the administrative level.
Industry experts are convinced that massive spending on healthcare can be reduced through the assistance of artificial intelligence. Pamela Hepp, an expert in digital health records, health care regulation, and data security at Buchanan, Ingersoll $ Rooney, said that several studies show that the US healthcare administrative costs continue to increase or remain relatively higher compared to other countries. Despite such findings, she added that AI could boost efficiencies in health care delivery.
Population Health Management
Hepp believes AI could help in reducing costs in population health management, a discipline within the healthcare industry that not only studies but also facilitates care delivery across a group of people or the general population. According to Prashanth Kini, Avasdi’s VP of product management for health care, population health is not a destination but rather a moving target with goals that when once achieved, they will be promptly replaced by new ones that are currently out of reach.
Kini added that population health management resources or tools currently depend on analysts questioning healthcare data sets. Nevertheless, numerous trends and patterns will end up being uncovered due to clinicians failing to ask the right questions. Alternatively, AI can solve this issue via unsupervised learning, which is a subset of machine learning that evaluates data and discovers anomalies and patterns with little human involvement.
According to Hepp, AI may come in handy, especially in the development or identification of evidence-based treatment and medicine protocols that can be used for treating specific diseases. Evidence-based medicine entails making treatment decisions based on observations derived from population clinical studies. Although this practice is not new, the rate of human processing power inhibited its use in the past. Currently, AI algorithms are assisting in the improvement of such efforts through the evaluation of millions of data points and the rapid identification of important patterns.
Research and Discovery of Medication
AI could also help in minimizing the costs of creating new vaccines and drugs. This process is both expensive and time-consuming. However, AI can reduce expenditure and expedite the drug development process by improving research efforts and analysis.
Although AI has the potential to optimize the administration of health care services, it is yet to overcome various challenges. In fact, the deployment of AI itself is an expensive exercise. Furthermore, regulatory problems can also be expected to slow the adoption process.