Enhancing the Use of AI in Chronic Disease Management

Enhancing the Use of AI in Chronic Disease Management 

Introduction

The coronavirus pandemic has not only put the world’s healthcare under extreme pressure, but it has also exposed the various shortcomings of the healthcare system across the world. There is an enormous shortage of nurses, clinicians, hospital beds, medical supplies, and also various other medical infrastructure. All usual care and elective surgeries have taken a backseat as they are not being considered essential and are not receiving the needed attention. This situation has made matters worse for patients who suffer from chronic diseases. First, they are not getting any care and second, they are also most vulnerable of getting infected with Corona. As COVID-19 has forced billions of people to stay in their homes, the only reasonable solution for chronic patients is to get remote and digital care.

Management of Chronic Diseases

Chronic diseases (such as hypertension, heart failure, and diabetes) consist of the majority of healthcare spending. The challenge is that there are too many patients but too few physicians. Other industries address this problem by using structured algorithms that can make product development more efficient, scalable, and with minimized variation in quality. Can healthcare do the same to make patient care more scalable, algorithmic, and cost-effective, along with optimizing patient outcomes and decreasing its variability?

AI can Play a Huge Role

To efficiently use Artificial Intelligence (AI) in chronic disease management, providers must have access to high-quality and accurate data. The University of Pennsylvania Medical Center (UPMC) Chief Health Care Data and Analytics Officer Oscar Marroquin, MD, states that data scientists require real-world data to gain insights to be integrated into clinical programs.

“We need to use real-world data as we are focussed on delivering care. If we apply the right analytics to the data, we can gain the insights that we require in order to drive our organization by coming up with new real-world based indications,” Marroquin stated.

Managing and maintaining the data warehouse is a collective and collaborative effort amongst clinical analysts, IT, data engineers, and business intelligence specialists. Through their work, UPMC has established many self-service analytics tools that enable clinicians and businesses to gain access to insights, data, and evidence gained by the medical center. The analytic tools include AI-based, predictive, and statistical models.

AI can Help in Streamlining Care for Chronic Diseases

A study published in Harvard Business review explained how Paschalidis and his team in Australia worked on a project in the year 2017, in which they used patients’ Electronic Health Records (EHRs) and Machine Learning (ML) to predict hospitalizations due to diabetes and heart disease. Using this method, the group found that they could predict hospitalizations almost about a year in advance, with an accuracy rate of almost 82 percent. Now, Paschalidis and his team will create and develop even more advanced predictive capabilities using EHRs and real-time health data, including information from implantable devices, wearables, and home-based networked diagnostic devices.

Conclusion

For AI to help in chronic disease prevention, broad data collation and well-trained algorithms are required. Algorithms can then be used in ML to recognize or identify persons at high risk for a chronic disease. With access to such data, physicians can encourage preventive health strategies and inspect the social determinants of health that could turn into the illness. AI, along with sensor technology, can unlock the potential of data, offering actionable insights to guide clinical decisions to identify, treat and handle chronic conditions remotely, thus, providing the right clinical intervention to the right patient at the right time.