The path to scalable AI in healthcare will not be defined by a single breakthrough or technology. It will be shaped by the industry’s ability to address the foundational challenges that have existed for decades.
Healthcare is pouring money into AI, but poor data quality is quietly sabotaging results by scaling bias, errors, and mistrust instead of value. Until organizations fix historical data, set accuracy baselines, and keep humans in the loop, AI will multiply problems faster than it improves outcomes.
By reducing administrative burden and redesigning workflows around human needs, it creates space for what matters most: connection between clinicians and patients.
When you combine clean data with longitudinal patient records and transparent access — that's when you start seeing real transformation in care delivery.
Healthcare is a data-driven industry, but it's held together by incompatible, siloed data systems. Providers and payers need the ability to exchange data and update directories in real time; otherwise, they cannot ensure patients will access the high-quality care they deserve.
As organizations dive further into their datasets and explore how artificial intelligence (AI) and machine learning (ML) can help reveal new insights, one thing must remain at the forefront: data quality.
When developing or deploying new algorithms, hospitals and healthcare AI developers must pay close attention to the quality of training datasets, as well as take active steps to mitigate biases, said Divya Pathak, chief data officer at NYC Health + Hospitals, during a recent panel discussion.
Zelis highlights how employers, payers, and consumers are integrating digital tools to manage healthcare costs.
Atropos Health — a company that delivers clinical data to physicians at the point of care to help them treat complex patients — recently launched a clinical advisory board and new evidence network. Both announcements are meant to enhance the credibility and transparency of its physician consult platform, CEO Brigham Hyde said at ViVE in Nashville.
More than half of technology leaders at the country’s top 50 health systems by net patient revenue said they’re investing more money into interoperability initiatives in 2023 than they did last year, according to a new report. Participating in health information exchanges is a key way that health systems strengthen their interoperability strategy.
The key is to understand that bad data is the primary driver of disenrollment, and to create a plan to improve the quality of data.
The hope is that this NIH-funded data provenance project will help standardize and reconcile inconsistencies so clinicians have better data to bring to the bedside and run through clinical decision support systems.
Enterprise EHR boosts scalability, interoperability, and governance for large healthcare systems.