By Tharun Kumar Nallamothu – Healthcare organizations are investing heavily in artificial intelligence, but in practice, only a small number of systems make it past the initial rollout and actually get used at scale. The gap isn’t coming from a lack of innovation. If anything, the pace of model improvement has been impressive.
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By Michael Ford – A growing share of the problem hospitals face today doesn’t come from denials at all. It comes from claims that sit unresolved without ever receiving a formal denial. Why do these claims fall through the cracks?
By April Miller – Healthcare organizations face financial pressure as operating costs and administrative demands increase. At the same time, patients expect clear pricing and greater transparency throughout the billing process.
By Cheryl Cruver – For the better part of the last three years, healthcare has been fascinated by artificial intelligence (AI). Conferences, board meetings, and strategic plans have all focused on AI’s capacity to reshape everything from clinical documentation to revenue cycle management. At this year’s HFMA Annual Conference, however, something interesting happened.
By Sanjeev Menon – Automation was always the floor, not the ceiling. The next phase of patient access depends on two capabilities the first generation was never built to deliver.
By Nathan Elliott – When NCQA validated its first ten clinical data streams in late 2021, Data Aggregator Validation looked like an experiment. Four years on, it looks like infrastructure.
By Tharun Kumar Nallamothu – Lessons from Real Clinical Environments – Artificial intelligence in healthcare has reached a point where technical capabilities are no longer the primary limitation. Models can transcribe clinical conversations, extract insights, and support decision-making with increasing accuracy.
By Zac Amos – Artificial intelligence (AI) is helping healthcare organizations automate administrative tasks, improve workflows and support better decision-making. However, implementing AI without careful planning can create new challenges for employees, leading to AI burnout instead of greater efficiency.
By Sujay Jadhav – In the complex, high-risk business of pharmaceutical research and development, sponsors face a challenge of picking target products with high therapeutic potential and acceptable safety profile. Then they must efficiently manage costs and timelines.