Health systems using Carta Healthcare have reduced abstraction time by up to 66 percent and lowered abstraction costs by 50 percent or more. They also consistently achieve 98 to 99 percent Inter-Rater Reliability, the highest standard for data quality.
“There has been a lot of focus on automating clinical workflows,” said Brent Dover, CEO of Carta Healthcare. “What we see in practice is that automation without clinical oversight does not hold up. This award reflects the success of a different approach, where clinicians remain at the helm and technology supports their work.”
More than a dozen healthcare executives offered their perspectives on the event, and how it showed health systems are changing their approach to digital tools.
The ViVE digital health conference drew about 7,000 healthcare leaders to Los Angeles a few weeks ago for discussions about AI, technology and changes in the industry.
More than a dozen healthcare leaders shared their insights from the conference with Chief Healthcare Executive®.
While the leaders offered a variety of perspectives, several underscored that hospitals and healthcare organizations are looking at AI differently.
At the ViVE Conference, Greg Miller, Vice President of Marketing and Business Development at Carta Healthcare, discusses the critical role of humans in the loop when it comes to healthcare AI. As one of the first technologies that requires continuous monitoring, AI in healthcare demands oversight, accountability, and collaboration between humans and machines to ensure accuracy and trust. Greg also highlights the power of clinical data in advancing women’s health—from improving prevention and diagnosis to enabling better treatments and even driving toward cures. The more high-quality data we have, the clearer the story becomes—not just about individuals, but entire populations. This conversation underscores a key truth: data, when used responsibly and paired with human insight, has the potential to transform the future of care.
It’s not well known but there’s a lot of people in hospitals who spend a lot of time creating patient registries for quality programs, CMS reporting, clinical trials and lots more. It requires extremely detailed abstraction of patient data from patient records and comparisons with registry demands. Wouldn’t it be clever if an AI system could read the chart and help the people doing that work (usually very expensive nurses) do it quicker? That’s the premise behind Carta Healthcare. Greg Miller and Jared Crapo from Carta demoed the system for me and told me about the market for it.
Carta Healthcare, the award-winning leader in enterprise clinical data management, underscored the role of hybrid intelligence in advancing responsible AI for healthcare as Anthropic unveiled Claude for Healthcare during the 44th Annual J.P. Morgan Healthcare Conference.
Claude for Healthcare Powers Carta Healthcare’s Hybrid Intelligence. Carta Healthcare, the award-winning leader in enterprise clinical data management, underscored the role of hybrid intelligence in advancing responsible AI for healthcare as Anthropic unveiled Claude for Healthcare during the 44th Annual J.P. Morgan Healthcare Conference.
The greatest hybrid intelligence opportunities lie in workflows that require both accuracy and throughput. Clinical documentation, diagnostic support, care coordination, and quality measurement all meet that description. But nowhere is the need more visible than in clinical data abstraction.
Clinical data abstraction in hospitals is the process of clinicians manually reviewing a patient’s electronic medical record to answer very specific questions for clinical registries. These registries are national, standardized databases that track patients with similar conditions or procedures, and they are essential for quality measurement, process improvement, and regulatory reporting. Specifically, they help hospitals track outcomes, assess treatments, refine care pathways, and demonstrate adherence to established standards.
I’ve spent more than 30 years in nursing, and in recent years my work has shifted to something most people never think about: clinical data abstraction. My job is to review the medical record and extract the pieces of information that clinical registries rely on for research, quality reporting, regulatory requirements, and everyday decision-making. It’s specialized work that depends on clinical judgment, a deep understanding of clinical documentation in the EHR, and a commitment to getting every detail right.
Clinical data abstraction-the meticulous process of reviewing medical records to extract key information for research, quality reporting, and regulatory compliance-is a complex undertaking. It demands clinical judgment, a deep understanding of electronic health records (ehrs), and unwavering attention to detail. Information is often scattered across multiple systems and documented inconsistently by different clinicians, making the task both challenging and time-consuming.
