Hospital 2040: InfoBionic’s Stuart Long on Implementing AI in Cardiovascular Care

Research by GlobalData shows that the market for artificial intelligence (AI) was around $81.3 billion in 2022. Credit: Shutterstock / greenbutterfly.

The American Heart Association (AHA) predicts that by 2050, 184 million Americans, or about 61% of the population, will have some form of cardiovascular disease (CVD). This increase in cases is expected to drive costs up to $1.8 trillion.

In the UK, the British Heart Foundation reports that 7.7 million people are currently living with CVD. With these alarming statistics, investment in cardiac health is on the rise.

As hiring medical staff becomes more challenging, healthcare providers and medical device companies are looking for ways to maintain essential services. AI automation offers a promising solution by improving efficiency, which could lead to better patient care.

GlobalData’s research also indicates that the market for remote patient monitoring was valued at around $600 million in 2022 and is expected to grow to $760 million by 2030. In previous discussions in the Hospital 2040 series, we explored how AI and automated systems can streamline hospital operations and optimize workflow.

Today, Hospital Management speaks with Stuart Long, CEO of the US-based virtual cardiac care firm InfoBionics, to learn how AI-backed patient monitoring systems can help hospitals manage the expected increase in cardiac cases.

Joshua Silverwood: With so many Americans expected to develop CVD, how important is at-home patient monitoring?

Stuart Long: At-home monitoring is no longer a novelty. Many people already use devices like blood pressure cuffs or thermometers at home. The COVID-19 pandemic has also made this concept more familiar.

Now that at-home monitoring is more accepted, we are still in the early stages, but I believe it will become more widespread over time.

JS: How can AI and at-home monitoring help reduce hospital backlogs?

SL: The main cause of hospital backlogs is the global shortage of healthcare workers. The population and disease rates are growing faster than companies can hire staff. Automation and AI can help by quickly assessing information and handling routine tasks, allowing healthcare workers to focus on more complex issues.

While AI won’t replace humans in healthcare, it will make certain jobs less common. AI can autonomously monitor large groups of people and identify potential problems, which will become a common practice.

JS: What safeguards should be in place to prevent AI monitoring systems from making errors?

SL: In healthcare, AI can be divided into three main areas. One is generative AI, which can be used for tasks like note-taking and summarizing clinical notes. These AI systems will be trained on smaller, specialized language models and overseen by the FDA.

The more focused the AI is, the fewer errors it will make. In healthcare, AI can either help find diseases earlier or improve workflow and efficiency to reduce patient backlogs.

At InfoBionic, we use AI to process clinical data. Our system cleanses the data and identifies specific markers. We train multiple signal processors to look for different issues, and then combine them to analyze various markers.

Our AI can differentiate between electrical signals from different parts of the heart. We then use machine learning to train the AI on thousands of studies to recognize normal and abnormal patterns. Clinicians test the AI’s accuracy before we submit it to the FDA.

JS: How can AI-assisted cardiac systems improve outcomes for stroke patients?

SL: AI can help identify atrial fibrillation, a common cause of strokes, early on. Currently, people often don’t get monitored until they have symptoms. AI can detect subtle signs of atrial fibrillation before symptoms appear, allowing for earlier intervention.

In the near future, AI will be able to analyze a person’s electrocardiogram (ECG) results and predict their risk of developing atrial fibrillation. This will enable early treatment to prevent strokes.

JS: What are the cost benefits of implementing AI systems?

SL: Early detection of any condition saves money. Treating someone who has had a stroke is much more expensive than preventing it in the first place. Even if a disease has progressed, early intervention is still cheaper than treating advanced cases.

AI systems will save money for patients by reducing the need for expensive treatments and medications.