Beyond Drug Discovery: The Hidden Friction Slowing Clinical Trials

Anthropic's presentation at JPM Healthcare on its strategy in life sciences felt strangely polarizing. On one hand, hearing Dario discuss the velocity of change and the promise of AI in life sciences was exciting—but on the other hand, it felt like the same critical blockers that have held back efforts in the past decade were largely unaddressed.

Dario said, "where AI is going, maybe we could have a much faster rate of these drug discoveries. And then everything comes downstream from that. That leads to more discoveries of fundamental biology. That leads to more drug candidates. That leads to drugs that have larger effect sizes and so can go faster through clinical trials which leads to better care for patients."

While drug discovery is a powerful and very real use case for AI, it doesn't automatically translate into faster clinical trials or better care for patients. Once a drug leaves the lab and enters the clinic, progress is shaped by the operational reality of how trials are executed day to day.

The Unglamorous Friction Points

Everyone talks about accelerating trials. Few want to discuss the unglamorous friction points slowing them down. One of the clearest examples lives far downstream from drug discovery, in a workflow most sponsors rarely see: how patient data moves from clinical systems into trial databases.

Clinical Research Coordinators spend hours every week doing something that sounds almost unbelievable to anyone outside the industry: entering the same exact patient data into multiple systems multiple times.

A patient's blood pressure is 128/82. The nurse writes it in the chart during the visit. The coordinator records it on a tracking worksheet. Later, they log into the EDC and type the same numbers into separate fields. If it's a safety endpoint, they enter it again in another module. Same data, four entries. Coordinators we've talked to describe this as "death by a thousand clicks."

This is the norm and it's burning out the very people trials depend on. So why does this still happen? The answer lies in how clinical trials evolved, and what got locked in place when the industry went digital.

How We Got Here

For decades, clinical trials ran entirely on paper. Sponsors provided Protocols, or blueprints describing how a trial should run. Sites used Case Report Forms (CRFs), or structured documents derived from the protocol to record specific data at each patient visit. Source documents like medical records and lab reports held the raw clinical data.

Data lived in one physical place and moved physically between site and sponsor. There was one version of the truth, even if it took weeks to reach the sponsor's desk.

When Electronic Data Capture (EDC) systems emerged in the late 1990s and early 2000s, they digitized the sponsor's side of the equation. Faster database lock. Remote monitoring. Audit trails. Sponsors could finally see data in near real-time instead of waiting for paper forms to arrive by courier.

But EDCs didn't change how data originates at the site. Patient visits still happen in hospital systems, outpatient clinics, and research facilities using platforms designed for patient care, not trial compliance. Sites kept working in Electronic Medical Records (EMRs), lab systems, and paper worksheets because that's what patient care required.

70% of sites report that more than 50% of their EDC data is re-entered from existing EHR data (Clinical Leader).

The System Divide

Clinical systems and trial systems evolved with different goals. EMRs are designed for clinical care and EDC systems are designed for regulatory compliance with sponsor oversight and FDA inspections.

Neither system was built to talk to the other. Even when they technically could, they rarely do. The gap between them became a permanent feature of the landscape. So coordinators fill it manually. Every visit. Every patient. Every data point.

The Real Cost

A typical Phase 2 trial might require 30-40 data points per patient visit. A coordinator running multiple trials might manage 20-30 active patients at a time. If even half of those data points require manual transcription from one system to another, that's hundreds of redundant entries per week.

Every re-entry takes time. Every re-entry creates risk of typos, transposition errors, copy-paste mistakes. Those errors trigger queries from monitors, which require coordinators to pull source documents, write explanations, and re-verify data. More time. More clicking. Coordinators burn out and leave the industry entirely. And sponsors pay for all of it through longer timelines, higher monitoring costs, and preventable amendments.

31.2% of clinical research coordinators (CRCs) said they experienced stress because of contracts with sponsors. CRCs mentioned being overwhelmed by protocol changes, data entry requirements, and sending, updating, and resending documents (Florence Healthcare).

Widening the Lens

This is where the optimism around AI in life sciences needs to widen its lens. Advances in drug discovery are real and meaningful, but once a molecule enters the clinic, outcomes are shaped by operational reality. Until the friction embedded in everyday trial execution is addressed, faster discovery alone won't deliver faster trials or better care.

Fixing this type of friction is what we're focused on at Rightview. In our next post, we'll examine another workflow that has an outsized impact on timelines, sites, and sponsors alike.

If you are interested in learning more about how we're unifying unstructured data in clinical trials, reach out to us at research@rightview.ai.