Curing AI’s Data-Indigestion Problem
Harnessing CLIMB to systematically improve AI performance and efficiency through strategic training data optimization.
Dr. Charlotte had always believed that AI would transform clinical care … not by replacing doctors, but by removing the paperwork that kept them from patients. As fictional director of clinical AI integration at PfarmAssist (a fictional health-tech startup), she was hired to bring that vision to life. The company had developed a promising digital assistant designed to transcribe and summarize patient encounters in real time—freeing physicians from the drudgery of typing notes late into the night.
Early signs were encouraging. The assistant could reliably convert spoken dialogue into readable notes, and the team had trained it on a massive trove of health-related web text—believing more data meant better results. Investors were impressed, and pilot hospitals signed on eagerly. But once deployed, the cracks began to show.
Clinicians complained that the summaries missed key clinical facts—mislabeling medications, skipping relevant symptoms, or hedging on diagnoses. A cardiologist reported that “palpitations” were being confused with “anxiety” in summaries. A general practitioner said the assistant glossed over social history entirely. Though the model was fast and fluent, it lacked depth (and worse, couldn’t be trusted). Usage began to fall. Several doctors reverted to manual charting.
The problem wasn’t immediately obvious. The model had been trained on billions of words from medical forums, wellness blogs, and health-related Wikipedia pages. But few of those sources resembled actual clinical documentation. Most were written for laypeople, not by doctors. And very little resembled the structured, high-stakes writing found in real patient notes.
Still, the team assumed that if the dataset was big enough, the model would “figure it out.” It didn’t. Instead, it began generalizing—using vague language, over-indexing on common phrases, and omitting detail. It was writing like a medical blogger, not a physician.
Mounting Pressure, Limited Options
At the same time, pressure was intensifying. Hospital partners wanted rapid improvements, especially in specialized fields like oncology and emergency medicine. Meanwhile, Cliffland Analytics and Yoke.ai (also fictional startups) were gaining traction—boasting models trained specifically on niche medical tasks.
Worse yet, internal discussions revealed a hard ceiling. The team couldn’t afford to scale the model much further. Their cloud training budget was fixed. The easy lever (“just train it on more data”) was off the table. The engineering team explored adding filters to the dataset or manually tagging better examples, but both were labor-intensive and unlikely to yield meaningful improvements fast enough.
The elephant in the room? No one really knew what kind of training data would produce better results. The team lacked a systematic way to measure whether oncology case studies were more helpful than radiology reports, or how much weight to give real clinical notes versus publicly available medical articles. Everything about their training data strategy was based on gut feel.
What Happens If You Stay the Course?
Dr. Charlotte understood what was at stake. If the product didn’t improve quickly (and dramatically), several pilot hospitals would walk away. More concerning, the credibility of the company’s core AI engine was beginning to erode. Product marketing had leaned heavily on its “smart summarization” capabilities. Now, sales reps were spending more time explaining what the assistant couldn’t do than what it could.
Beyond revenue loss, there were deeper risks. If the assistant continued to produce vague or inaccurate documentation, it could raise red flags with compliance teams (or worse, introduce liability for physicians relying on incorrect notes). The company’s leadership began questioning whether the current approach was salvageable or if they’d need to pivot to a less ambitious product.
And so, despite the sophistication of the technology and the commitment of her team, Dr. Charlotte faced a hard truth: the model wasn’t underpowered; it was underfed. It wasn’t that they needed more data. They needed better data. And they needed a strategy to figure out what “better” actually meant (not in theory, but in practice) on the clinical front lines.
Continue learning how Dr. Charlotte put a newly published AI research to work, changed the game with data strategy, and more.