How can healthcare providers and payers leverage AI to improve health outcomes and reduce costs?

What are the opportunities for payers and providers to utilize AI for Healthcare?

Artificial intelligence technology (AI) is becoming increasingly used throughout healthcare. The incorporation of AI can drastically streamline processes for healthcare providers and payers, as well as help providers deliver more efficient care to patients. This technology is already being used to reduce healthcare costs and ease administrative burden, improve patient diagnosis and early detection, create virtual care programs for chronically ill patients, and more. 

How can AI improve population health management?

Population health management (PHM) involves using population-level data to identify health risks and treatment opportunities for a group of individuals or community. AI can contribute to PHM by combining, synthesizing, and analyzing datasets from third parties with clinical and/ or patient-generated data. Researchers and health providers can use AI to aggregate longitudinal patient-generated data into larger datasets that tell better stories about the incidence and prevalence of disease.

AI can be used to identify populations at risk for adverse health outcomes, like opioid abuse or overdose. One population health management company integrates data on social determinants of health and pharmacy claims to better understand the diverse “spectrum of opioid abuse cases.” PHM can also help in expanding treatment access to resource constrained  environments, such as rural areas through voice assistants, chatbots, and other AI applications. AI-driven chatbots can address routine patient questions and help doctors communicate with patients about their diagnosis and risk evaluations.

How can I use AI to better connect patients to resources and care?

AI has tremendous value to connect patients with available resources and care. This is especially true for patients living in rural areas with limited access to quality healthcare. One example is its  ability to provide patients with personalized healthcare recommendations. Sage Bionetworks launched a series of mobile research studies, known as mPower, to increase understanding of the progression of Parkinson’s Disease in individuals. The study uses surveys and phone sensors to track symptoms of Parkinson’s Disease. The results can help patients, doctors, and caregivers better understand changes over time and the impact of exercise or medication. Using AI, data from mPower also has the ability to help develop specific healthcare recommendations for patients.

What are the opportunities for virtual healthcare using AI?

With the use of AI, providers have the ability to develop virtual healthcare programs for patients with chronic conditions. Verily Health’s Onduo project, which combines a smart device and mobile application, offers virtual care for people with type 2 diabetes. Onduo can measure blood glucose levels as well as provide information on nutrition and medication management. The app also offers a coaching dimension that identifies lifestyle patterns and gives patients feedback to improve their health.

This can especially benefit patients in rural and other resource constrained areas. The use of voice assistants and chatbots has the potential to improve and increase access to treatment in these areas. There is increasing evidence that AI-driven chatbots can address routine patient questions and help doctors communicate with patients about their diagnosis and risk evaluations. 

How can providers leverage AI to improve diagnosis and early detection?

Diagnostic errors are a major problem in the healthcare system, with most patients experiencing at least one diagnostic error in their lifetime. AI algorithms can help by drawing upon large datasets on medical and social determinants of health to better identify patterns and assist physicians in making diagnoses and developing treatment plans. Physicians are already leveraging AI to accurately diagnose medical conditions in their patients and treat illnesses at an early stage.  

AI can deploy technologies like image recognition, natural language processing (NLP), and deep learning to quickly detect life-threatening conditions and assess risk for diseases such as brain cancer or heart disease. Medical stakeholders have noted that it may be more accurate to think of these applications as “augmented intelligence” rather than artificial intelligence. The goal is not to replace the physician’s clinical judgment, but to help physicians rapidly prioritize patient symptoms and assess a range of diagnostic possibilities in a timely and more efficient manner, rather than ask patients a standard slate of questions. For example, AI is helping doctors diagnose diabetic retinopathy, the world’s leading cause of blindness, by using image recognition. Researchers at Google have trained algorithms to analyze images of retinas and diagnose this disease with over 90 percent accuracy. 

What are the opportunities for drug and therapy development?

AI can help in the development of new drugs and therapeutics. Drug development is a costly and time-consuming process. AI can help improve drug development through the entire development lifecycle, from identifying gaps in current therapeutics to bringing new products to market. Pharmaceutical researchers can use AI to sort through huge numbers of research papers and patents, as well as comprehensive lists of chemical compounds and their properties, to suggest opportunities for drug development. By analyzing the growing databases of biomarker data, they can then work to target different treatments to different types of patients. And when drugs or other treatments reach the clinical trial stage, AI can help match ideal patients to the right trials. 

Improving clinical trial participation is a great illustration of this opportunity. HHS recently completed a “tech sprint,” engaging external experts like TrialX and Intel to develop AI applications to match patients to appropriate clinical trials. This kind of matching can help researchers find appropriate subjects for their studies and help patients find potentially valuable treatments at the same time. AI technology can also support precision medicine. The National Institutes of Health (NIH) defines precision medicine as “an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment and lifestyle for each person.”  Researchers at startups like Lam Therapeutics and Lantern Pharma are using supervised machine learning strategies to generate new correlations between genomic biomarkers and drug activity to pilot individualized cancer treatments.