As we try to combat healthcare burnout, improving operational efficiency is a big part of that. Like everything in healthcare, there are plenty of ways to approach an issue, so today we are going to narrow that down to back office health IT systems. We reached out to our incredible Healthcare IT Today Community to ask — how are back office health IT systems improving operational efficiency in areas such as finance, human resources, and supply chain management? The following are their answers.
The Stevie Awards for Technology Excellence are among the world’s premier business awards, honoring achievement across the technology sector, from artificial intelligence to biotechnology. The winning nomination, “Hybrid Intelligence for Trustworthy Clinical Data Abstraction,” recognizes how Carta Healthcare solves one of healthcare’s most persistent operational problems, the manual, costly, and error-prone work of clinical data abstraction.
92% of healthcare leaders say they won't trust AI at scale without deep clinical expertise. Read that twice if you're a clinician feeling like healthtech doesn't want you.
This stat comes from an April survey of US healthcare leaders, published on 21 July by Carta Healthcare.
The headline may be about AI, but the finding is really about people.
The findings reframe a debate that has focused on whether or not AI belongs in clinical settings. The data suggests that question has largely been answered, though adoption stalls for structural reasons. Organizations validate AI in a pilot, see results, and then fail to expand it.
Where does adoption stall?
When asked what slows AI adoption, respondents pointed to operational friction. Difficulty integrating with the electronic health record (EHR) was the top barrier at 44%, followed by lack of executive sponsorship or budget (37%) and competing organizational priorities (33%). Concerns like clinician trust, ongoing cost, and regulatory uncertainty tied far lower at 26% each.
According to a Carta Healthcare survey, 92% of healthcare leaders believe deep healthcare domain expertise is critical when evaluating an AI vendor.
Carta Healthcare, the leader in enterprise clinical data management, today released findings from a national survey of U.S. healthcare leaders showing that AI is proving its worth but failing to expand, and that leaders will not trust it at scale without deep clinical expertise. In the survey’s most lopsided result, 92% said deep healthcare domain expertise is critical when evaluating an AI vendor, with half of all respondents assigning it the maximum possible score. Even where AI has delivered measurable value, 71% of organizations are not expanding it at pace.
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.
