The key to AI cardiology analytics is human action
Artificial intelligence is making it easier than ever for cardiovascular programs to access and analyze vast amounts of clinical and operational data. But according to former American College of Cardiology (ACC) president Cathie Biga, MSM, MACC, and president/CEO of Cardiovascular Management of Illinois, data dashboards and AI-powered analytics alone will not improve patient care.
Biga spoke on this topic in a session at the ACC 2026 annual meeting and emphasized that the true value of analytics lies in how healthcare teams use the information to drive measurable change. She spoke to Cardiovascular Business in the above video interview.
"The point I made was what are we doing with this data? If we're not impacting outcomes, if we're just collecting data for the sake of data, we're not doing anybody any favor," Biga explained.
Her presentation, titled "Dashboards Don't Save Lives, Teams Do: Turning Cardiovascular Data Into Care," was delivered as part of a session honoring James Dove, an early advocate for data-driven medicine. Biga said Dove foresaw nearly two decades ago that healthcare would increasingly rely on data to guide clinical decision-making.
Today, the challenge is no longer obtaining data, especially with the help from artificial intelligence (AI). Rather, it is translating insights into action.
"We have a lot of data, but if we're going to collect data, we have to use the data," she said.
AI can identify problems, but not solve them
Modern cardiovascular information systems (CVIS) increasingly incorporate advanced analytics and AI tools that can quickly aggregate performance metrics, identify trends and generate sophisticated dashboards. This technology is highlighted in demos on the ACC expo floor by cardiology IT vendors as a way to greatly increase efficiency and visibility of the data. These systems have replaced the labor-intensive reporting processes that many healthcare organizations relied on in previous decades.
However, Biga cautioned that AI can only show organizations areas that need attention.
"AI can help point us, but if I have to sit through one more meeting with graphs of arrows up and down and arrows sideways, and the next month I have the same arrows, but we're not affecting any change, even AI is not going to help us," she said.
Using a lighthearted analogy, Biga added that "Tinkerbell is not going to come and give you an action plan." Instead, healthcare organizations must use humans to develop concrete strategies and execute them through multidisciplinary teams.
Cardiology management and care teams drive improvement
Biga stressed that physicians cannot shoulder the responsibility for quality improvement alone. Successful implementation requires collaboration among nurses, pharmacists, social workers, advanced practice providers and community resource specialists.
For example, if data reveal patient access issues, organizations can redesign workflows by creating parallel clinics or expanding the role of advanced practice nurses to reduce physician burden and improve appointment availability.
Similarly, heart failure programs with elevated readmission rates may benefit from greater involvement of dietitians, pharmacists and social workers to address factors that contribute to repeat hospitalizations.
"We need our teams," Biga said. "Our physicians can't possibly do it alone. Our nurses can't do it alone."
The ultimate goal is to implement interventions and then revisit the data months or years later to determine whether outcomes have improved.
"That's what's been missing in some of our data analysis," she said.
Set realistic goals and measure progress
A key component of turning analytics into results is establishing achievable performance thresholds rather than pursuing unrealistic targets.
Biga cited patient access as an example. If new patient appointments are currently scheduled 60 days out, organizations should focus first on reducing wait times to 30 days rather than attempting an immediate shift to 48-hour access.
The same approach can be applied to reducing emergency department observation stays, lowering readmission rates, controlling costs or improving other cardiovascular quality metrics. By implementing targeted changes and monitoring results over time, healthcare organizations can determine whether interventions are producing the desired effect.
As AI becomes increasingly embedded in healthcare operations, Biga said organizations must avoid the temptation to equate sophisticated analytics with meaningful improvement. "AI can generate these amazing dashboards. They cannot mandate change," she said.
For cardiovascular programs, the next frontier is not collecting more data, but transforming data into action. According to Biga, the organizations that succeed will be those that combine powerful analytics with engaged multidisciplinary teams capable of turning insights into better patient outcomes
Dave Fornell has covered healthcare for more than 17 years, with a focus in cardiology and radiology. Fornell is a 5-time winner of a Jesse H. Neal Award, the most prestigious editorial honors in the field of specialized journalism. The wins included best technical content, best use of social media and best COVID-19 coverage. Fornell was also a three-time Neal finalist for best range of work by a single author. He produces more than 100 editorial videos each year, most of them interviews with key opinion leaders in medicine. He also writes technical articles, covers key trends, conducts video hospital site visits, and is very involved with social media. E-mail: [email protected]