What should you know about AI in US healthcare?
In 2021, 85% of healthcare executives said they had an AI strategy, and these numbers continue to grow. From predictive analytics and increased accuracy in disease detection, to enhanced personalised treatment and medicine - AI has the potential to revolutionise healthcare in ways that were previously unimaginable.
While the potential of AI and ML in healthcare is promising, there are ethical considerations surrounding data privacy, security, bias, and accountability.
People need to be able to trust these systems for wider deployment - and the public is not fully there yet. Pew Research found that 60% of Americans are uncomfortable with their provider relying on AI in their healthcare. Similarly, 37% of Americans think using AI in health would worsen patient record security.
In order to fully harness the transformative power of AI in healthcare, it is crucial to address these concerns and ensure a responsible and transparent approach to its implementation.
Predictive analytics and proactive care
AI and ML can perform predictive analytics and enable proactive care, which allows for accurate and timely diagnosis, and effective treatment and management of diseases.
AI algorithms can analyse large datasets from diverse sources, such as electronic health records (EHRs), lab results, and social determinants of health, to identify patterns and trends. X-rays, CT and MRI scans, and data from wearables can be efficiently analysed to detect early signs of diseases like cancer, cardiovascular conditions, and neurological disorders - with higher accuracy rates compared to human radiologists.
This can lead to early detection and intervention, lower hospital readmission rates, and improved patient outcomes.
In the US, AI is being leveraged for predictive analytics to identify patients at risk of developing chronic conditions, enabling healthcare providers to intervene early and provide proactive care interventions.
However, algorithmic bias is an ongoing challenge in AI innovation, which can be particularly dangerous in predictive healthcare as individuals may be misdiagnosed or discriminated against. If algorithms wrongly predict a patient’s likelihood of developing certain health conditions, insurers can use this to deny coverage or charge higher premiums to those deemed higher risk. Those already facing health challenges may be further burdened by higher healthcare costs or even denied coverage altogether. This is likely to have a disproportionate impact on specific demographics, e.g. the elderly and people with pre-existing conditions.
It is crucial to ensure that patient data is protected. Algorithms need to be designed and trained with diverse and representative datasets, to avoid bias and discrimination in healthcare decision-making, and to build trust with patients, healthcare providers, and regulatory authorities.
Personalised treatment plans
By analysing patient data such as medical history, genetic information, lifestyle factors, and treatment outcomes, AI and ML algorithms can identify patterns and correlations to help develop individual treatment plans.
Healthcare professionals can optimise treatment strategies, choose the most effective medications, and predict potential adverse reactions - all of which lead to improved patient outcomes and reduced healthcare costs.
This kind of technology is currently being used in AI-based clinical trials to map diseases better and design new vaccines and drugs. When combined with deep learning techniques, health professionals can better tailor specific treatments. For example, AI and machine learning were used in immunology research - a fundamental during the covid 19 pandemic.
As AI continues to advance in healthcare, personalised treatment plans are expected to become a prominent trend. However, given the sensitivity of patient data involved, regulatory frameworks must be established to govern the ethical and responsible use of AI.
Making healthcare operations more efficient
Natural language processing and conversational AI is now commonly used to streamline healthcare operations, leading to increased efficiency and cost savings.
AI-powered chatbots and virtual assistants can handle routine patient inquiries, appointment scheduling and medication reminders.
This can be patient scheduling, resource allocation, and inventory management, leading to more efficient use of healthcare resources and reduced costs. In the US, AI was being used to develop chatbots to encourage vaccine uptake among the public, providing key insights on patient experience and more.
Not only in the US but across the world, the pandemic stretched healthcare staff to their limits. This kind of technology is already proving useful in the aftermath of COVID-19, by reducing the burden on healthcare professionals and improving patient satisfaction.
However, it will be important to establish guidelines and regulations in relation to these models’ use in healthcare and insurance. This includes rules about how data is collected and used, and requirements for transparency and accountability in the use of AI algorithms.
The integration of AI and machine learning is already reshaping our understanding, development and patient/practitioner experience in US healthcare. As the healthcare industry continues to embrace AI, it is essential for healthcare professionals to stay informed and adapt to these technological advancements.