Artificial Intelligence is transforming various industries, and healthcare is no exception. It’s truly remarkable how AI is revolutionizing the way we approach medical diagnosis, treatment, and patient care. These early examples, show that AI can improve patient outcomes, reduce costs, and shift the focus from disease to wellness.
In this article, we explore the potential economic impact of AI in the healthcare industry, discussing the predicted cost savings, improved patient outcomes, and shift towards personalized medicine that AI can bring. Additionally, we touch on the importance of addressing industry standards and regulation to build trust in AI systems and avoid bias in AI-driven decision-making. Finally, we highlight Probius’ approach to utilizing AI to improve disease diagnoses and drive new insights into disease, with the ultimate goal of creating a world where decentralized and predictive healthcare helps people stay ahead of disease.
Can Predictive Analytics Impact Healthcare Access?
The U.S. spends more money on healthcare than any other country in the world, however this leadership in spent does not translate in in some of the leading indicatirs of healt (e.g. life expectancy). Current systems are complex and bureaucratic, laden with post-pandemic complications and caregiver burnout. Costs continue to escalate, as do the obstacles that keep the US healthcare systems from thriving.
Change is on its way, however. A recent CNBC post shared this: Accenture estimates that 70% of health-care workers’ tasks could be reinvented by technology augmentation (like AI) or automation.
And at a global scale, Goldman Sachs delivered news that Generative AI could raise global GDP by 7%.
We see predictive analytics as one element of AI with the potential to deliver answers to health sector problems. By combining machine learning and big data, predictive analytics can offer solutions to biases and increase the speed at which viable new therapies are discovered. The results will be significant, reflected both in lives saved and in dollars saved.
The Impact of AI in Healthcare Costs?
A January 2023 report published by McKinsey & Company and Harvard Business Review offers impressive insight into how AI can simplify healthcare processes. This report entitled The Potential Impact of Artificial Intelligence on Healthcare Spending, the study intimates that AI can bring forth new processes which positively impact the cost of healthcare.
They foresee the results being quite impressive. Wider adoption of AI could lead to savings of 5% to 10% of U.S. healthcare spending. That’s roughly $200 billion, with the potential to reach $360 billion in healthcare savings in the U.S. alone.
The challenge now is finding the balance between economic value, clinical merit, and patient benefits in AI-supported activities. For example, companion diagnostics and software, data analytics, and other information technology advancements all have a high potential for AI applications and significant commercial benefits in markets where there is pricing flexibility. With lower development and commercialization costs, healthcare savings will certainly follow.
Cost reduction will be achieved through a variety of factors, including the digitalization of health care, advances in health care IT, novel translational technologies entering clinical laboratories.
These cost-savings can enable the healthcare industry to shift its focus from disease to wellness, bringing with it a range of benefits for society, industry, and individuals, lowering sick leave instances and increasing productivity.
How will AI Impact Healthcare and Treatment?
Machine learning, artificial intelligence, and big data can deliver robust and innovative answers to age-old problems. With the potential to significantly transform healthcare, AI can deliver powerful tools for identifying and addressing health risks, improving patient outcomes, and reducing healthcare costs.
Because of its money-saving potential, AI might be the means by which the healthcare industry will shift its focus to personalized medicine. Improved quality healthcare, increased access, better patient experience, and greater clinician satisfaction are all outcomes of personalized medicine. In fact, an article published by the National Bureau of Economic Research estimates that personalized medicine can increase the impact of treatments by roughly 50%. They also found that this positive impact correlates directly with the cost of care delivery.
As predictive analytics and biological AI begin to play a fundamental role in improving health and reducing mortality rates, it is anticipated that more people will warm to the idea of reliance on artificial intelligence across the medical industry. However, AI has a long way to go.
According to a new Pew Research Center survey, a whopping 60% of Americans would feel uncomfortable if their provider relied on AI for medical care delivery. Worse still, only 38% AI could lead to better health outcomes. AI’s public image needs some work.
What is AI’s Anticipated Impact on Healthcare Standards?
There is mounting public concern over artificial intelligence becoming imbedded in products and processes. There are numerous reasons for this, but it all comes down to trust. Some research suggests that general trust of AI varies with what AI is being trusted to do. For example, subjective judgements (or ‘human’ judgements versus mechanical judgements) are not embraced for AI applications. There appears to be a lack of trust in the systems that support the application of AI in healthcare.
We assume this is because it is hard to keep up with the fast pace of technology, a particularly tricky downside where moral hazard could strike if machines makes decisions in place of an empathetic human. The point where a machine process should be replaced by a human mental process is not clearly regulated or controlled by industry standards.
Therefore, addressing industry standards can move the needle on AI being accepted. Harvard Business Review has discussed such regulation and their key takeaway appears to be addressing bias. Some companies like Amazon and Twitter are working on fairness metrics to address biased outcomes, but the Review suggests that all companies, whether directly involved in AI development or not, should engage with such challenges — or risk eroding trust and triggering unnecessarily restrictive regulation.
Not addressing these issues will significantly limit the benefits of predictive analytics in the health sector.
How can Probius use AI for Positive Fiscal Impact?
At Probius, our MiniQES Bioanalytical Platform, simplifies decentralized biological data collection enabling AI to unravel the complexities of biology.
By making biochemical data easier to access and use in AI workflows, we help drive new and more cost-effective insights into health and disease. To accelerate drug development, predictive healthcare and reduce costs.
Our vision is for a world where decentralized and predictive healthcare helps people stay ahead of disease. The savings AI can bring to this vision is more than money. It’s about saving human lives.
Follow us and let’s continue de dialogue of AI in healthcare and biological research.