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$1.2M AHRQ Grant Helps Montefiore Build AI CDS Tool for Lung Failure

Montefiore Health System will use the grant to help providers identify a rare form of lung failure with artificial intelligence.

A $1.2 million grant helps Montefiore build AI CDS tool for lung failure.

Source: Thinkstock

By Jessica Kent

- Montefiore Health System has received a $1.2 million grant from the Agency for Healthcare Research and Quality (AHRQ) to develop an artificial intelligence (AI) tool that will help clinicians identify a rare form of lung failure and offer clinical decision support (CDS) for patients. 

Acute respiratory distress syndrome (ARDS) affects approximately 200,000 people in the United States. Although it is a deadly condition, it is easy to miss, with providers failing to make an accurate diagnosis to 40 percent of the time.

“ARDS is underrecognized because these patients are often extremely ill and have other life-threatening conditions, such as shock, pneumonia, or trauma,” said Michelle Ng Gong, Chief of Research, Critical Care at Montefiore Health System and professor of medicine and of epidemiology & population health at Albert Einstein College of Medicine.

“Since diagnosis depends on the patient meeting a number of criteria, it is easy for one of the criteria to be attributed to another acute condition, rather than to ARDS. By using new technology, we hope to help clinicians identify ARDS as early as possible, when treatment may be most effective.”

The AHRQ grant will help Montefiore build an AI tool to screen patients and flag those at elevated risk of developing ARDS. After identifying high-risk individuals, clinicians will receive clinical decision support guidance, including best practices for treating ARDS patients..

To develop the tool, researchers will build off Montefiore’s existing Patient-centered Analytical Learning (PALM) platform.  The ARDS algorithm will use de-identified patient data to determine the critical data points for ARDS. The AI will then run in the background of the electronic health record (EHR) and flag any patients who match the profile for ARDS.

This new tool will add to past work from the Critical Care and health data teams at Montefiore Einstein, which demonstrated that AI can improve outcomes for patients with severe acute respiratory failure.

The new project will also enhance Montefiore’s commitment to fostering timely and proactive clinical decisions. For over three years, the health system has been developing an overarching AI framework to inform predictive analytics algorithms across every sector of care.

“Our strategy at Montefiore is to build a data-driven and evidence-based health system – essentially a learning healthcare system – that can understand its own population thoroughly, understand and improve its practices, and develop the highest quality of services for the people it serves,” Parsa Mirhaji, MD, PhD, Director of the Center for Health Data Innovations at Montefiore Einstein and Associate Professor, Systems and Computational Biology at Albert Einstein College of Medicine, told HealthITAnalytics.com.

“In order to accomplish that goal, we have created a system that harvests every piece of data that we can possibly find, from our own EMRs and devices to patient-generated data to socioeconomic data from the community. It’s extremely important to use anything we can find that can help us categorize our patients more accurately.” 

With the development of this new tool, Montefiore expects to continue to leverage AI and big data analytics to improve care for lung failure patients.

“We have seen the power of AI and predictive analytics to accurately pinpoint patients at risk for other critical conditions and believe AI can be effective in helping clinicians identify patients with ARDS too,” said Mirhaji.

“The ultimate goal is for AI to become a standard tool for clinicians, helping them provide the best care for our patients.”

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