VIDEO: 9 key areas where AI is being implemented in healthcare


Julius Bogdan, vice president and general manager of the Healthcare Information and Management Systems Society (HIMSS) Digital Health Advisory Team for North America, explained several key artificial intelligence (AI) trends he sees across healthcare. These include AI use in:
   • Medical imaging
   • Population health
   • Telehealth and remote patient monitoring integration into clinical practice
   • Health tracking apps
   • Identifying and addressing gaps in health equity
   • Revenue cycle management streamlining
   • Hospital-wide monitoring for length of stay, bed turn over rates, early sepsis detection and readmissions
   • Data analytics for key performance indicators 
   • Enabling better patient wellness and preventative care

Bogdan discusses the critical need to involve clinicians or the business administration and staff in whatever area an AI application is being deployed. Without buy-in from the staff using it, the AI will often not be well-received—and, subsequently, will not be used. Bogdan said early involvement is needed to show staff how the AI works and what data points are used, noting that this will help build a better understanding and buy-in. 

"With the advent of the modern electronic health record (EHR), healthcare became more of a data industry, and the analytics of that data has begun to play or more prominent role across healthcare," Bogdan explained. "So, AI has started to gain some momentum in healthcare in a lot of different areas."

He said AI can help sort through the vast amounts of patient data to pull out relevant pieces of information for specific patient encounters, to search for population health traits that might target some patients for additional care or resources, to help streamline diagnosis or workflow, or to help in data mining or identifying patterns in data—which can be very difficult for humans to do easily.

"Artificial intelligence is really good at discerning patterns within the data. There has been a lot of work in the medical imaging space, where AI can really help improve diagnostic capabilities with image recognition," Bogdan said. 

As we move from a fee-for-service payment system toward a value-based-care model, AI offers healthcare systems a way to better assess patient risks using population health algorithms. "This includes the ability to identify issues in the population that we can address to help improve patient outcomes and bend the cost curve," Bogdan said. 

He said there was a rush into telehealth during COVID, and AI can help with remote monitoring and answering basic questions or basic diagnostics for patients using these devices so they know when to contact their physician. Similarly, a growing number of companies and health systems are now using health tracking apps. This is another area where AI can help, namely by monitoring this large amount of patient data and identifying people who may require physical followup, or identifying people who may have issues with mental health or burnout. 

"There is a growing body of research that is being incorporated into these health and wellness applications, and AI is at the heart of a lot of them. That might include how to de-stress or offering information about nutrition, suggesting medical tests you may need or a list of medical providers you can see," Bogdan explained.

Health inequity was something that really became apparent during the COVID pandemic, and AI can help address this as well.

"You can't really address health equity without understanding the needs of the population and environmental factors, and that is all related to data," he explained. "So health analytics from the EHR can do a lot in regards to addressing health equity. But to really address health equity, you really need to broaden that lens by looking at patient needs, health outcomes, environmental factors, social determinants, and socioeconomic data to really understand the community you are serving," he said. 

Health system administrators also are turning to AI to help better manage and automate tasks to understand and manage issues related to staff burnout, turnover rate, lack of resources, and utilization of resources. AI can also help identify bottlenecks in workflow, or issues that prevent clinicians from working at the top of their license, sometimes causing issues that ultimately make them leave their jobs and go to competitors. 

On the hospital administration side, AI is being used to help automate revenue cycle management and to help identify issues. 

Bogdan said most of the large health informatics vendors have incorporated AI into their software to help with all of these areas. 

This is part of a 5-part series of interviews with Bogdan on various aspects of AI in healthcare. Here are the other videos in the series:

VIDEO: How hospital IT teams should manage implementation of AI algorithms

VIDEO: AI can help prevent clinician burnout

VIDEO: Use of AI to address health equity and health consumerization

VIDEO: Understanding biases in healthcare AI

Find more AI news and video

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