How patient organizations are driving rare disease drug development

October 15th, 2026 by

Patient organizations have stopped asking pharma for a seat at the table. In rare disease, a growing number are building their own table, and increasingly they are the ones sponsoring the trials. That was the throughline of a Clinical Leader conversation between Craig Lipset, co-founder and co-chair of the Decentralized Trials & Research Alliance and founder of Clinical Innovation Partners, and Dr. Derk Arts, founder and CEO of Castor, that aired on 17 September 2026. The math explains why it is happening now. In a strong year the FDA’s drug center approves roughly 50 new medicines. In that same year, researchers characterize somewhere between 150 and 250 new rare diseases. With more than 10,000 rare diseases on the books, and industry able to pursue only the commercially returnable ones, someone has to move the rest. The first hands up have been patient-led nonprofits.

What the session covered

For fifteen years the industry line was “include patients in design.” Lipset’s argument is that the ground has already shifted past that. These groups are no longer advisory voices.

“This isn’t partnering with patients. This is the patients who are driving the car.”Craig Lipset

Some patient-led nonprofits now sponsor molecules in Phase 1 and beyond with no pharma partner at all. The FOXG1 Research Foundation and the TESS Research Foundation are both advancing gene therapies that the FDA cleared to begin trials in the past year. When these groups invite industry to their table, Lipset said, they often do not get the same turnout they were promised for years, and his challenge to industry was blunt: answer the call.

The hard part is evidence, and it has a specific shape. Lipset relayed a challenge from a workshop led by Janet Woodcock, the former FDA principal deputy commissioner and longtime director of its drug center: when a population is too small for a randomized trial, the answer is not to run an underpowered one. Underpowered trials waste patients’ time and can bury good medicines as failures. The law, Arts noted, calls for substantial evidence from adequate and well-controlled studies, not specifically a randomized trial, which leaves room for self-controlled designs, natural history and real-world data, and model-informed external controls.

Arts pushed hardest on who should control the data these studies run on. He described helping an ultra-rare group stand up its own natural history study, with families pulling together their own medical records (a patient-mediated, direct-to-patient route) that then feed AI-assisted data extraction with human review in Castor Catalyst, work his team has taken on pro bono so that parents stay in charge of the data rather than handing it off.

“It’s quite elegant to have patients actually govern their own data sets and then decide who gets access and for what it gets used.”Derk Arts

Lipset’s analogy was the credit report. You can see who pulled your financial data, lock it, and audit the trail, so why not your health data? “Data sharing for research shouldn’t happen in the dark,” he said, pointing to a bipartisan run of US data-access policy and the European Health Data Space as parallel signs the direction is set, if slow.

What’s in the full recording

The recording goes further than a recap can. Lipset walks through three ways a patient group can actually recover money from a derisked asset, including a pediatric priority review voucher that has sold on the open market for roughly $100 to $200 million (19:54). He and Arts get specific on how to cluster related rare conditions, such as FOXG1 and CACNA1A, into a single master protocol modeled on oncology’s Lung-MAP and I-SPY 2 (40:56). There is a candid exchange on why fifteen years of data-portability policy still has not given patients a usable “credit report” for their health records (31:16). And Lipset closes on obsolescence, with a pair of subway-token cufflinks, in a way that reframes what AI should mean for everyone working in trials (49:05).

Watch the full conversation

It is built for clinical-development leaders deciding how to work with the patient organizations already running ahead of them, and for patient-organization leaders mapping their own data and evidence strategy.

Watch on demand

Then go deeper: the insight brief on patient-led drug development in rare disease is the practical companion to this conversation.

That brief traces the path from a registry to a trial, and shows how patient-reported outcomes captured through ePRO and a lean, patient-owned clinical data management approach make that path runnable for a small group.

Frequently asked questions

What is patient-led drug development?

Patient-led drug development is when a patient organization, usually a nonprofit founded by affected families, takes the sponsor role in developing a therapy rather than only advising a pharma company. In rare disease these groups now fund natural history studies, select targets, and sponsor early-phase trials themselves, often because no commercial sponsor will take on a population that small.

Why are patient organizations sponsoring their own clinical trials?

The number of newly characterized rare diseases each year far outpaces the number of new medicines the industry approves, and most rare conditions are too small to be commercially attractive. As Lipset put it in the session, industry will chip away at the commercially returnable diseases first, so patient-led nonprofits are stepping in to move the rest, in several cases as the sole sponsor of a molecule in early clinical development.

How can a small population generate evidence without a large randomized trial?

FDA approval requires substantial evidence from adequate and well-controlled studies, not specifically a randomized controlled trial. For very small populations that opens the door to self-controlled study designs, natural history and real-world data, and model-informed approaches such as external control arms and digital twins. The goal discussed in the session is a shared taxonomy so regulators can set a proportionate level of evidence for each type of rare indication.

Editorial note

During the live session, the scarcity of new approvals was described as roughly 60 to 80 new medicines a year. For accuracy: the FDA’s Center for Drug Evaluation and Research (CDER) approved 50 novel drugs in 2024 and 55 in 2023, averaging about 50 over the past decade (per FDA Novel Drug Approvals). The contrast with the pace of new rare disease discovery holds, and this post uses the verified figure. On the priority review voucher: the pediatric program lapsed at the end of 2024 and was reauthorized in 2026 through 30 September 2029. A move to make it permanent has been proposed but is not yet law.

Sources

  1. FDA, Novel Drug Approvals 2024 and 2023, Center for Drug Evaluation and Research. Available at: https://www.fda.gov/drugs/novel-drug-approvals-fda
  2. Consolidated Appropriations Act, 2026 (P.L. 119-75), Rare Pediatric Disease Priority Review Voucher reauthorization through 30 September 2029. Available at: https://www.congress.gov

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Inside the Curavit and Lindus deal: what unified US and European trial delivery really takes

August 28th, 2026 by

For a sponsor running a trial across the US and Europe, most of the delay lives in the seams between vendors, sites, and regions. That was the thread running through a recent Castor LinkedIn Live, hosted by Derk Arts, Founder and CEO of Castor, with Joel Morse, CEO and Co-Founder of Curavit Clinical Research, and Joel White, Owner of Marketcap Consulting and an independent analyst who follows the clinical research industry closely. The occasion was Curavit’s acquisition of Lindus Health’s CRO assets, which brings US and European trial delivery under one roof while Lindus turns its focus to drug development. What followed was a direct conversation about where cross-Atlantic trials lose time, and where technology and AI realistically fit.

What the conversation covered

The headline is consolidation, but the substance was about removing the seams that slow cross-Atlantic trials. Joel M. walked through how the two teams merged, roughly forty to fifty people across the UK, Europe, and the US, in about two months, and why he structured it as a stock-for-assets deal rather than the all-cash structure more common in recent deals. Aligning incentives, he explained, keeps everyone invested in the same outcome.

The most useful thread for anyone choosing vendors was about technology. Several CROs have tried to become full-service and own their entire tech stack at the same time, a pattern Joel W. has followed closely. Building software that sponsors actually adopt is genuinely hard, especially when the market already runs strong, embedded tools.

“If you’re asking a sponsor to use you as a CRO and require them to use your software, that’s tough in this market with so much embedded great technology.”Joel White, Owner, Marketcap Consulting

Curavit’s answer was to build for what is uniquely theirs and connect to the rest.

“…build a platform that we call Stratus… and then we would API into what I call standard clinical technology, like the great technology that Castor creates.”Joel Morse, CEO and Co-Founder, Curavit Clinical Research

The other throughline was speed and certainty. Joel M. described a decentralized trial where Curavit enrolled half the participants in four months, while ten physical sites needed another full year to enroll the rest. Sponsors want endpoint quality and safety, and they also want assurance on timing and budget. Controlling recruitment is how you promise it.

On AI, all three landed in the same place. The change is real and it arrives steadily, moving out from document and safety workflows toward data extraction and, over time, monitoring, which is where our own work on Castor Catalyst is focused. The gating factor is not capability. It is trust, and trust has to be earned.

“With AI comes an even greater… distrust. We have… five years of education and trust building ahead of us.”Derk Arts, Founder and CEO, Castor

There is a subtlety in how the technology fits together that is worth drawing out. When a CRO builds its own tools and connects them to the platform underneath, that platform is not the interchangeable part. It is the layer everything else has to trust and depend on, which is a harder thing to be, not an easier one. The teams that get this right own what is genuinely theirs and connect the rest to a core they can rely on. That core is not a commodity. It is the part that has to be right.

What’s in the full recording

The conversation went well beyond this summary. A few moments worth your time:

One practical takeaway for your own program. When you weigh a CRO or a platform, separate what each side should genuinely own from what should simply connect, and pressure-test your recruitment timeline early, because that is where speed and certainty are won or lost.

Watch the full conversation on the LinkedIn Live event page. This swaps to the castoredc.com on-demand page once it is live. It is worth an hour for anyone weighing how to run cleaner trials across the US and Europe, and where technology and AI realistically fit.

FAQ

What did Curavit acquire from Lindus Health?

Curavit acquired Lindus Health’s CRO assets in an asset purchase, bringing US and European trial delivery under one roof. Lindus is now focusing on drug development as Lindus Therapeutics. Roughly forty to fifty people moved across, and Joel M. said the deal closed in about two months.

Does running a trial through one CRO across the US and Europe remove cross-Atlantic friction?

That was the central question of the session. The panel’s view was that unifying delivery helps, but the real gain comes from controlling recruitment timelines and budget through the delivery model. Sponsors still want endpoint quality and safety, and they also want assurance on timing and cost.

Where does AI fit in clinical research delivery today?

The consensus was evolution rather than revolution. The frontier is moving out from document and safety workflows toward data extraction and, over time, monitoring. Castor Catalyst is focused on that extraction and review work. AI here is AI-driven work with a human in the loop, and the gating factor is trust, which has to be earned.

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Does real-world evidence still need the physician? A live debate at the RWE crossroad

August 21st, 2026 by

Anthropic’s CEO, Dario Amodei, recently claimed that AI could cure most human disease within five to ten years. Set aside whether that is right. The harder question underneath it is the one that decides how fast clinical research can actually change: what happens to the humans in the study, the patients and the physicians, when the machine gets this good?

That is the question Derk Arts, Founder and CEO of Castor, and Matthew Gordon, Vice President of Real-World Evidence at Aptitude Health, took to a live, no-slides debate. Derk argued the deliberately provocative side, that real-world evidence may no longer need the physician at all. Matthew argued that the physician is still where the real value is. Neither fully believed his own corner, and that was the point.

What the debate actually surfaced

Derk opened with the strong version of the case. Real-world evidence is about standard of care, patients increasingly can access and direct their own data, and modern tools can pull it directly. So why keep the physician in the loop at all? Matthew’s answer became the spine of the hour.

Data can tell us what is happening. It doesn’t always tell us why.
Matthew Gordon, Vice President of Real-World Evidence, Aptitude Health

His example was in oncology. The record may show what treatments a patient received from first-line to second-line therapy, or that a genetic test was ordered. It rarely shows why, or why a physician chose to deviate from guidelines for a particular patient. Derk pushed back: shouldn’t the why live in the notes? Often it does not, which is exactly Matthew’s point, and an audience point from Jen Wheeler sharpened it. If the reasoning is not in the record, it exists nowhere in written form, stranded on a single physician’s memory, a knowledge island you then have to reach. With ten-minute visits and AI scribes now standing between the clinician and their own recall, retrieving it after the fact is close to impossible at scale.

Where they converged is the practical takeaway. Technology does not remove the physician, it changes the job. Pulling data straight from the medical record, through Castor Catalyst, Castor’s AI extraction pipeline with auditable, human-review steps, takes the data entry burden off the clinician. As Derk described it, that frees the physician to contribute the context and adjudication only they can, and it lowers the monitoring and budget load enough to bring more community physicians into studies rather than fewer. Matthew’s own model puts the clinician at the front door.

Let’s use the physician as a gateway to the patient and provide the invitation to participate to the patient through the physician.
Matthew Gordon, Vice President of Real-World Evidence, Aptitude Health

The resolution both landed on: there is no one-size-fits-all study. Start from the strategic objective. Some questions are answered fastest by pulling the data direct to patient. Others need the clinician in the room. Most need both.

What is in the full conversation

A few exchanges are worth watching in full. Derk makes the case that a new category of study is arriving, physician-light and patient-led, built for the GLP-1 generation of people who do not see themselves as sick but want to be healthier, where patient-experience and wearable data carry the weight. The two then trade ten-year predictions, and they do not match: Derk expects genuinely physician-less trials pulling wearable, patient-reported, and multi-institution record data, while Matthew holds that no robot replaces the clinician because our prehistoric brains still want the human in the room. There is also a candid stretch on why the care setting will adopt this faster than clinical trials will, and Matthew’s unscripted account of what a single unified data platform changed for the community physicians he works with.

Watch the full debate on demand. No slides, two people who build this work arguing the future of real-world evidence. Best for real-world evidence, clinical operations, and HEOR leaders weighing how far to push direct-to-patient and AI-driven data capture.

Watch The RWE Crossroad on demand

FAQ

What is direct-to-patient real-world evidence?

Direct-to-patient real-world evidence collects data with the patient at the center rather than routing everything through a site. Patients consent electronically, retrieve their own medical records (in the US via TEFCA and FHIR or a HIPAA release), and contribute patient-reported data, with wearable device data connected via API. It suits questions about lived experience, adherence, and outcomes, and it is well matched to people who want to optimize their health rather than treat an illness.

How does AI extract data from medical records without losing clinical context?

The safeguard is human review, not full automation. Derk described Castor Catalyst as an AI-driven extraction pipeline with auditable steps and human review built in, so human reviewers stay in control, with risk-based review before data enters the record. Both speakers agreed that letting an AI infer missing clinical reasoning on its own is dangerous, because it invites hallucination. The role of AI is to surface and structure what is in the record, with a human keeping control.

How do you keep AI-assisted real-world data trustworthy?

By keeping the provenance clear. Both speakers agreed the evidence stays trustworthy only when it is obvious what came from the patient, what came from the clinician, and what was inferred by the model. Castor Catalyst attaches a confidence score and source evidence to every extracted value, and human reviewers stay in control, with risk-based review before data enters the record. The model is not allowed to fill missing clinical reasoning on its own, which is where trust would break down.

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What nine years of MDR experience is changing about PMCF planning, and where AI fits in

June 3rd, 2026 by
Nine years after EU MDR came into force, the question has shifted. Early conversations centered on how to collect post-market clinical data under the new framework. Now a growing number of manufacturers are asking something more fundamental: whether active data collection is actually required for their specific device, and what “collection” means in an era of AI-driven extraction from clinical records.That tension sat at the center of a recent live session between Derk Arts, CEO of Castor, and Dr. Bassil Akra, EU MDR Expert, Founder and CEO of AKRA TEAM. Together, they walked through how the regulatory climate has evolved since MDR’s launch, where the distinction between Post-Market Surveillance (PMS) and Post-Market Clinical Follow-up (PMCF) matters in practice, and what AI tools are actually changing about the data collection calculation for device manufacturers.

Three themes from the session

1. The MDR arc: from overcorrection to recalibration

When MDR first took effect, manufacturers faced a documentation and clinical evidence burden that many had not anticipated. Nine years into that arc, Dr. Akra described a shift in how regulators and legislators themselves are reading the history of the regulation. A proposed MDR 2.0 revision is already in discussion, driven by recognition that uniform application of the original requirements creates real risks for patient access.

“Today, expectations are becoming more balanced. We are already discussing a future revision of the regulation that will scale back some of these requirements, because the European legislature has recognized that nine years of experience with MDR have not been ideal. If we continue on the current path, many devices may disappear from the market, and patients may lose access to treatments they have relied on for years.”

Dr. Bassil Akra, EU MDR Expert, Founder and CEO, AKRA TEAM (approx. 4:13)

The proposed revision is not settled law, but the signal it sends is meaningful. The regulatory community is beginning to separate data collection that serves patient safety from data collection that serves administrative compliance.

One current runs the other way. Transparency is ramping up. Under Commission Decision (EU) 2025/2371, the first EUDAMED modules became mandatory on 28 May 2026, with legacy devices following by 28 November 2026, so what a manufacturer can show about its post-market activity is more visible than it was.

The wider direction on data governance is moving too. ICH E6(R3) Annex 2 was finalized in June 2026, though for device investigations the governing good clinical practice standard remains ISO 14155, not ICH E6.

2. Not every device needs active PMCF data collection

One of the most practically useful points of the session was a clarification many manufacturers have missed. Post-Market SurveillanceThe systematic process of continuously collecting and evaluating data on a medical device after it is placed on the market, as required by EU MDR Article 83. is a universal obligation under MDR. Post-Market Clinical Follow-upA subset of PMS that involves active clinical data collection, such as registries or studies, required only when deemed necessary to address residual clinical concerns (MDR Annex XIV, Part B). is not.

“Not every device is required to carry out active PMCF, whether that takes the form of a clinical investigation, a registry, or a dedicated post-market study. That obligation only arises when it is deemed necessary for that specific device.”

Dr. Bassil Akra, EU MDR Expert, Founder and CEO, AKRA TEAM (approx. 9:02)

For manufacturers of well-established devices, Dr. Akra recommended auditing current PMCF commitments to separate what is genuinely required from what was added out of caution or a misread of the guidance. For devices that have been on the market for 30 or 40 years with a well-understood safety profile, active data collection that does not address a specific residual risk adds administrative burden without adding patient safety value.

3. AI changes the ROI calculation for data collection

The third theme was how AI tools are changing what is practically achievable in real-world evidence generation for device manufacturers. Derk Arts described how Castor Catalyst supports AI-driven extraction from electronic health records along two routes. In a site-based upload path, teams run source documents such as discharge summaries and lab PDFs through Catalyst. In a direct-to-patient path in the US, a patient authorizes retrieval of their records through FHIR, HIPAA release, and the TEFCA framework. Each extracted value carries a confidence score and is reviewed and validated by a person before it enters the study registry, reducing the burden on clinical sites while a human stays in control of the record.

Castor has reported production numbers behind this approach in device data collection. In a live paper-based Site Upload study, where sites upload source documents such as lab reports and charts for AI-driven extraction, a human reviewer changed only 0.8% of the AI extractions (99.2% were accepted on the first pass), with 99.8% submission success across the study’s data points.

For manufacturers where data collection does add clinical value, tools like this lower the practical threshold for running a targeted PMCF program. The decision on whether to collect data at all, however, remains a regulatory and clinical judgment. Dr. Akra framed the role of AI in this space as a tool for amplifying human expertise, not replacing it.

“AI is not replacing human beings. It is empowering us as individuals and as experts. And with that empowerment comes the responsibility to demonstrate how we intend to use it.”

Dr. Bassil Akra, EU MDR Expert, Founder and CEO, AKRA TEAM (approx. 31:23)

Five sections to watch in the full recording

The session goes deeper on several questions that came up repeatedly in the live Q&A:

  • 9:02 How to determine when PMCF is actually required under MDR, and how to read “deemed necessary” in practice when auditing existing commitments
  • 14:46 What notified bodies are accepting as real-world evidence in EU PMCF submissions today, and how those criteria have shifted since MDR’s launch
  • 23:03 How the direct-to-patient data retrieval workflow operates end to end, including how FHIR-extracted data is processed and ingested into the study registry
  • 39:00 A discussion of which manufacturers benefit most from AI-driven data collection and where legacy established devices sit on that spectrum
  • 43:06 Dr. Akra’s recommended steps for engaging your notified body today, including how to prepare for a structured dialogue about PMCF obligations vs. self-imposed commitments

Watch both speakers work through specific manufacturer scenarios and respond to audience questions in real time.

Watch the recording

Castor supports medical device manufacturers with decentralized data collection and clinical trial solutions designed for real-world evidence generation. Learn more about Castor’s PMCF offering here.

Frequently asked questions

Is PMCF required for all medical devices under EU MDR?

No. Post-Market Surveillance (PMS) is a universal obligation for all medical devices under EU MDR (Regulation EU 2017/745). PMCF, the active clinical data collection component, is required only when it is deemed necessary to address specific residual risks, device safety questions, or to confirm the benefit-risk profile of the device. Manufacturers of well-established, lower-risk devices may be able to demonstrate continued compliance through literature surveillance and complaint analysis without running a dedicated PMCF study or registry. The key criterion is whether active clinical data collection adds value for patient safety or risk management, not whether it satisfies an administrative checklist.

What is the difference between PMS and PMCF under EU MDR?

Post-Market Surveillance (PMS) is the systematic process of continuously collecting and evaluating data on a device once it is on the market. It covers complaint handling, vigilance reporting, trend analysis, and literature review. Post-Market Clinical Follow-up (PMCF) is a specific subset of PMS that involves active clinical data collection, such as running a registry, conducting a post-market clinical study, or gathering structured real-world evidence from clinical settings. PMS is mandatory for all devices. PMCF activities are required only when existing clinical evidence does not sufficiently address remaining clinical concerns or when the device benefit-risk profile has not been fully characterised through pre-market data.

Can AI-driven chart review be used in PMCF submissions under EU MDR?

AI-driven chart review with human oversight is increasingly being explored as part of real-world evidence generation for post-market submissions. Notified bodies are evaluating AI-driven approaches case by case, with data quality, traceability, and documented methodology as the key criteria. Submissions that use AI tools for data extraction need to demonstrate that a qualified reviewer has validated the extracted data and that the process is auditable. Manufacturers planning to use AI-driven approaches in a PMCF methodology should discuss the approach with their notified body before committing to a study design, as regulatory expectations for AI-generated evidence are still developing.

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Under pressure: what it takes to validate a cuffless blood pressure device

May 1st, 2024 by

When a company sets out to bring a cuffless, continuous blood pressure monitor to market, the wristband is the easy part. The hard part is proving that a cloud algorithm can read blood pressure accurately from an optical signal, across every kind of person who might wear it. That was the throughline of “Under Pressure,” Castor’s 2024 conversation with Jay Shah, Chief Medical Officer at Aktiia, and Derk Arts, CEO of Castor.

It matters because the problem is enormous. The World Health Organization estimates that about 1.28 billion adults aged 30 to 79 have hypertension, and only about one in five have it under control.[1] A device that tracks blood pressure continuously, rather than once or twice a year in a clinic, is a genuinely different tool against that gap.

What the session covered

Jay’s most useful point for anyone building a device is that the regulator does not separate the hardware from the software. He explained that Aktiia’s band uses off-the-shelf components, optical sensors and an accelerometer, and that the defensible technology is a cloud algorithm his team refined over two decades at the Swiss research institute CSEM. In his telling, the whole system is assessed as a single submission, which raises the evidence bar in a way most teams underestimate:

“You have to prove accuracy across different skin tones, from Fitzpatrick scale one to six, follicle density, hair density, BMI, obesity, gender. All those things you have to prove accuracy in every single cohort of users. If you thought you’re gonna rush a PPG based device to the market, think again.”
Jay Shah, Aktiia

Continuous measurement also changes what blood pressure is. Jay said the device takes roughly 200 readings a week, which turns a single annual threshold into a pattern a clinician can act on. He used salt as the example of why that matters:

“Only fifty percent of people with hypertension are salt sensitive. So for fully half the people we’re making that recommendation to, it’s not gonna help. And we have no idea which fifty percent that person is.”
Jay Shah, Aktiia

He was candid about the discipline that takes. Jay described a deliberate choice to withhold a reading rather than publish a bad one, so the accelerometer suppresses measurement during wrist motion, when the optical signal is too noisy to trust. Castor’s part in the story was the data layer. Derk described Castor as an API-first platform that lets a device company bring sensor data together with clinical and patient-reported data in one place, and Jay pointed to the platform’s flexibility and reliability as what made it fit the work.

What’s in the full recording

The conversation gets more concrete than a summary can:

Watch the full conversation

Jay and Derk get specific about the evidence a cuffless device has to generate and how the data pipeline supports it. Watch the session on demand. Since the 2024 recording, Aktiia has rebranded as Hilo, and in 2025 its G0 Blood Pressure Monitoring System, the Hilo Band, became the first cuffless blood pressure monitor cleared by the FDA for over-the-counter use in the United States, with US availability following in 2026 and the device already available across Europe, the UK, and Australia.[2] If you are generating the evidence base for a medical device, see how Castor supports medical device clinical trials.

Frequently asked questions

Why is a cuffless blood pressure device a software challenge, not a hardware one?

The sensors in a cuffless monitor are largely off-the-shelf. The defensible technology is the algorithm that converts an optical signal into an accurate blood pressure reading, and regulators assess the hardware and that software together as a single submission. So the evidence burden sits on the algorithm’s accuracy, not the wristband.

What does it take to validate a cuffless blood pressure device?

According to Jay Shah of Aktiia, a device has to prove accuracy across the full clinical range of blood pressure and across every user subgroup, including Fitzpatrick skin tones one to six, hair and follicle density, BMI, and gender. That subgroup-by-subgroup bar is what makes bringing a PPG-based device to market slow and demanding.

What was Castor’s role for Aktiia?

Castor was the data platform. Derk Arts described it as an API-first system that lets a device company bring sensor data together with clinical and patient-reported data in one place, and Jay Shah pointed to its flexibility and reliability as the reason it fit Aktiia’s work.

References

  1. World Health Organization, Hypertension fact sheet: an estimated 1.28 billion adults aged 30-79 have hypertension, and around one in five (21%) have it under control. https://www.who.int/news-room/fact-sheets/detail/hypertension
  2. MassDevice: FDA clears Aktiia’s over-the-counter G0 cuffless blood pressure monitor (the Hilo Band), a 510(k) clearance and the first cuffless BP monitor indicated for OTC use in the US (2025). https://www.massdevice.com/fda-clears-aktiia-over-the-counter-g0-cuffless-blood-pressure-monitor/

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From hype to health: the three jobs Sword made its clinical evidence do

June 26th, 2025 by

Most digital health companies collect clinical evidence once, to get a foot in the door. Sword Health treated it as a strategy that changed as the company grew. Over ten years it put clinical research to three different uses in sequence: first to overcome clinician skepticism, then to establish digital care as a new standard, and now to prove cost-effectiveness so payers will reimburse it.

That progression was the throughline of “From Hype to Health,” a Castor session with Fernando Correia, Chief Medical Officer at Sword Health and part of its founding team, and Derk Arts, CEO of Castor. The stakes are large: musculoskeletal conditions affect roughly one in two US adults,[1] and low back pain is the leading cause of disability worldwide.[2] It lands now because the funding correction that hit digital health after 2021 raised the bar: the companies that survived have to show clinical outcomes and cost savings, not one or the other.

What the session covered

Fernando laid out the three waves plainly:

“The first thing that we used our clinical trials for was to reduce friction towards adoption. The second wave of clinical trials, the ones where we used Castor, were geared towards defining our solution as a new standard of care. And finally, the third goal is to prove cost effectiveness in order to unblock financial reimbursement.”
Fernando Correia, Sword Health

Underneath that strategy is a deliberate mix of real-world evidence at scale and focused randomized trials. From the start, Sword decided which clinical data to collect prospectively on its members, so small early samples would compound into a large real-world dataset. Sword’s own low back pain cohort now exceeds 200,000 people, which Fernando described as one of the largest real-world samples in digital care. His point was that the dataset is not a byproduct, it is built on purpose:

“This takes years to build. Early on, nothing happens… then all of a sudden you have really, really big samples. But you only have that data if you consciously build that machine.”
Fernando Correia, Sword Health

Building that machine is an organizational problem as much as a data one. Fernando described his job as brokering a standing tension between clinicians who want to measure everything, product teams worried about survey fatigue and dropouts, and a commercial team that needs data to win clients. The resolution he keeps landing on is to start from what a buyer needs to see, then pick the shortest validated scale that still captures it.

The randomized trials came later. Sword’s earliest studies in 2016 ran on pen and paper. Its two pandemic-era RCTs comparing digital and in-person care ran on Castor, and one of them, a chronic low back pain trial with Emory University, was published in npj Digital Medicine in 2023 and reported outcomes comparable to in-person physical therapy.[3] Derk summed up where Castor fits in one line: a platform to generate clinical evidence and run a clinical trial. The 200,000-patient real-world dataset was Sword’s own. The controlled trials behind the published evidence are where Castor sat.

What’s in the full recording

The conversation goes further than the strategy:

Watch the full conversation

Fernando and Derk get specific about how to sequence evidence and design trials that hold up in the real world. Watch the session on demand. If you are building the evidence base for a digital health or device program, see how Castor supports clinical trials, real-world evidence, and medical device studies.

Frequently asked questions

What did Sword Health do differently to prove its digital care works?

Sword used clinical evidence as a sequenced strategy rather than a one-time study: early trials to overcome clinician skepticism, randomized trials to establish digital care as a standard, and cost-effectiveness studies to unblock payer reimbursement. It ran this alongside a large real-world evidence dataset built from prospectively collected member data.

How does real-world evidence work alongside randomized trials in digital health?

Real-world evidence compounds over years from data collected prospectively on every member, which is how Sword built a low back pain cohort of more than 200,000 people. Randomized trials run in parallel to test digital care against in-person care directly. Sword ran both, using the real-world data for scale and the trials for controlled comparison.

What was Castor’s role in Sword Health’s clinical evidence?

Castor was the clinical data platform for Sword’s two pandemic-era randomized trials comparing digital and in-person care, one of which was published in npj Digital Medicine in 2023. Sword’s 200,000-patient real-world dataset was collected on Sword’s own systems, not on Castor.

References

  1. United States Bone and Joint Initiative (BMUS): musculoskeletal conditions affect roughly one in two US adults. https://www.sciencedaily.com/releases/2016/03/160301114116.htm
  2. Global Burden of Disease 2021 / Lancet Rheumatology (2023): low back pain is the leading cause of years lived with disability worldwide. https://www.healthdata.org/news-events/newsroom/news-releases/lancet-new-study-shows-low-back-pain-leading-cause-disability
  3. Correia FD, et al. Digital versus conventional rehabilitation for chronic low back pain: randomized controlled trial. npj Digital Medicine 2023;6:121. https://swordhealth.com/articles/nature-study

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How device and diagnostic teams run compliant studies without enterprise EDC overhead

July 24th, 2026 by

If you run clinical studies at a medical device, diagnostic, or IVD company, you know the trade-off. Enterprise EDC systems carry cost, complexity, and build time that a low-complexity study never needs. Spreadsheets are cheaper, but they hand an auditor nothing usable. Teams running early or low-complexity studies get stuck between the two.

In a recent 30-minute product spotlight, Lisa Charlton, Chief Product Officer at Castor, and Angie Martinez, Senior Solutions Consultant, showed a third path and built a compliant study live, with no code, on the Castor Essentials program. The value was not the feature tour. It was seeing how a device team gets from protocol to audit-ready data capture in weeks, and where that entry point leads as a program grows toward post-market evidence.

The dilemma, and the middle path

Lisa opened on what she called the med tech data dilemma. Systems built for large pharma programs bring complexity and long build times that a simpler device study does not need, which makes them a poor fit for the work. Castor Essentials is built for that gap: a unified electronic data capture system and ePRO platform with a native randomization module, self-service, and 21 CFR Part 11 compliant from the start. Lisa noted that low-complexity self-service studies average three to four weeks to build.

Angie then built a study live and framed the approach up front:

Everything you’ll see here today is designed to be self-service, low to no code, and 21 CFR Part 11 compliant right out of the box.

In the build, Angie showed data entry saving in real time, edit checks that flag a coordinator before they leave a field, and an immutable audit trail behind every action. Randomization was the standout. Rather than a separate contract, it runs inside the EDC:

Historically, if you wanted randomization, you might have to contract with an expensive third-party system and spend weeks building custom APIs. We have a native randomization module inside our EDC.

From there she moved through monitoring and risk-based source data verification, an ePRO survey completed on a phone, and on-demand data exports, all from the same platform.

What Castor Essentials includes

  • Unified EDC, web-based ePRO, and a native randomization module
  • No-code build, with Castor Academy training and support
  • Three to four weeks to build for low-complexity studies
  • GDPR compliant, ISO/IEC 27001 certified, and 21 CFR Part 11 compliant
  • Data hosting across the US, EU, UK, and Australia for data residency
  • Over 800 medical device studies supported to date

What the recording covers

The full session goes deeper than a summary can. Angie walks through the native randomization setup, including stratification by site and severity and variable block sizes, and how blinding is handled (11:32). She puts on a monitor’s hat to show the centralized query command center and how risk-based verification is configured with a single click (12:50). Later she demonstrates Form Sync, which creates a linked, non-live copy of a study so a team can test changes and merge them into production with no downtime (35:05). And the Q&A gets specific: moving an active trial from another EDC mid-study (28:40), and how eConsent, part of the fuller Castor platform, can create the EDC record while keeping patient identifiers separate and encrypted (43:35).

From entry point to evidence program

The session did not stop at the build. Lisa pointed to where an Essentials study goes next. As a device program moves toward post-market obligations, Castor’s data management team can take on the build and query management, and Castor Catalyst adds AI-assisted extraction of source records, with human review, for PMCF and direct-to-patient evidence. Essentials is the fast, compliant way in, and the same platform carries the program through to the evidence a post-market plan demands.

Watch the build, then map your path

Watch the full 30-minute live build on demand, then see where an Essentials study can go as your evidence needs grow.

Watch the recording
Book a demo

Questions from the session

Can I migrate an active trial from another EDC to Castor?

Yes. The team explained that migrating an in-flight study mainly depends on the file type your current EDC exports (for example CDISC ODM, CSV, or Excel) and how many data points are involved, which drives the effort. Castor can convert and import that data in parallel while you build the study and prepare to go live.

If I upload a CSV, does the data map to existing fields or create new ones?

The form has to be built in Castor first. Not every column needs to match, as long as the system can match the participant ID and your variables are labeled correctly, it maps the columns automatically regardless of their order. If a participant record does not yet exist, the import creates it with the fields captured in the file.

How much of the build can the API do?

Castor is API-first, with publicly available APIs. You can push data, and pull data through them, and integrate with wearables, digital health devices, EHRs, and RTSM or IRT systems. Study structures can also be imported in XML and CDISC ODM formats.

Can Castor integrate with our CTMS?

Yes. The team confirmed CTMS integration is done regularly, using Castor’s APIs. The platform is built to be interoperable, whether you are exporting data or connecting to other systems.

Can I outsource EDC and data management together?

Yes. Castor has an in-house data management team that supports study build, query management, eCRF generation, data validation plans, and post-go-live activities, so you can outsource EDC and data management as one engagement.

What if we must host data only on EU-based servers?

That is supported. You choose the server region where your study is built and your data is stored. Castor offers data hosting across the US, EU, UK, and Australia to meet data residency requirements.

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Beyond the EHR: meeting the FDA’s new real-world evidence standards

April 26th, 2026 by

For years, the use of real-world evidence (RWE) in regulatory submissions has required managing complex privacy constraints, fragmented data standards, and ambiguous agency expectations. A series of recent regulatory updates has fundamentally shifted this landscape.

Between December 2025 and March 2026, the FDA issued two distinct but highly complementary pieces of guidance regarding Real-World Data (RWD). While one targeted medical devices and the other focused on drugs and biologics, together they signal a unified regulatory philosophy: the FDA is lowering the privacy barrier for RWD while significantly raising the requirements for data structure and provenance.

For clinical operations and data strategy leaders, understanding the nuances of both pathways is critical for adapting to this shift.

A side-by-side assessment: devices vs. drugs

December 2025

Medical devices: lowering the privacy barrier

The FDA created a conditional pathway for de-identified RWD in medical device submissions, significantly reducing the requirement for individually identifiable source data. This lowers the operational and legal friction involved in using large-scale post-market registries for regulatory decision-making. Regulatory attorneys across the industry view it as a leading indicator that similar flexibility for drug and biologic submissions may follow.

March 2026

Drugs and biologics: raising the quality requirements

The FDA adopted ICH M14, establishing explicit standards for how sponsors must design, analyze, and report non-interventional pharmacoepidemiological studies used for post-approval safety assessment. An EHR export is no longer sufficient on its own. Study design, data provenance, and statistical approach must all be pre-specified and documented to the ICH M14 standard before submission.

Medical devices: lowering the privacy barrier (December 2025)

In December 2025, the FDA finalized guidance that sent a strong signal to the life sciences industry by relaxing the requirement for individually identifiable source data, and creating a conditional pathway for de-identified RWD when using RWE in medical device submissions.[1]

Drugs and biologics: raising the quality requirements (March 2026)

In March 2026, the FDA adopted ICH M14: General Principles on Planning, Designing, Analyzing, and Reporting of Non-Interventional Studies That Utilize Real-World Data for Safety Assessment of Medicines, a harmonized international guidance developed in collaboration with EMA and PMDA.[2] While the device guidance created flexibility around identifiability, the drug guidance raised the methodological bar.

Note: The FDA’s separate draft guidance covering non-interventional studies for both safety and effectiveness (“Considerations Regarding Non-Interventional Studies for Drug and Biological Products”) was issued in March 2024 and remains in draft as of this publication.[3]

The shared destination: enriching RWD with patient perspectives

Because the FDA is increasingly focused on the patient perspective alongside clinical endpoints, relying solely on administrative claims or EHR data is often insufficient. To meet regulatory standards, sponsors are increasingly combining retrospective clinical data with prospective, direct-to-patient data collection via electronic patient-reported outcomes (ePRO).

We see this exact shift in our own platform data. Across the Castor platform today, over 360 observational and registry studies are actively enriching their clinical datasets by capturing more than 16.4 million patient-reported data points. Sponsors recognize that pairing clinical data with quality-of-life and patient-reported outcomes is a defensible way to satisfy regulatory scrutiny.

The technology imperative

If sponsors are going to submit pseudonymized RWE to support a regulatory decision, the agency still needs to trust the origin and integrity of the data.

The important distinction is between de-identification and pseudonymization. At the study conduct level, data must be pseudonymized: direct identifiers are stripped for the sponsor and regulator, while a secure coded link is maintained at the investigator level for safety reporting and source data verification. True de-identification, which severs that link entirely, is generally inappropriate during an active study because it makes source data verification and adverse event follow-up impossible. The FDA’s December 2025 device guidance accepts de-identified data at the point of regulatory submission, where the audit trail has already been captured during the study itself.

This requires infrastructure built on modern data standards: CDISC for regulatory output (required for FDA submissions) and FHIR for EHR data ingestion (an FDA-encouraged standard currently under active evaluation for RWD submissions).[4]

At Castor, our clinical trial solutions are powering 1,077 observational studies and registries encompassing over 627,000 patients globally.

1,077 observational studies · 627,000+ patients

active on the Castor platform

Across that volume, we see a distinct trend: sponsors are building reusable, interoperable data assets rather than single-submission registries. This requires infrastructure capable of ingesting EHR data via FHIR, integrating prospective ePRO, and producing CDISC-compliant output with full audit trails. Castor’s Catalyst platform is built to this specification. By maintaining full audit trails without exposing Protected Health Information, this infrastructure provides the exact defensibility the FDA requires.

In practice: the Kaleido Registry

To understand how this shift plays out operationally, we can look at large-scale commercial deployments on the Castor platform. The Kaleido Registry, a prospective commercial registry, successfully enrolled nearly 34,000 participants across 8 sites. Rather than relying solely on administrative site data, the sponsor deployed a high-volume ePRO strategy to capture over 117,000 patient-reported data points directly from the participants. This approach reflects the direction both guidance documents point: large-scale observational datasets that integrate patient-reported data at the point of collection, rather than appending it retrospectively.

Conclusion: building for the long term

The 2025 and 2026 RWD guidance changes provide a clear framework for the future of clinical development. The FDA has outlined a pathway for faster, more pragmatic evidence generation across both devices and drugs, provided sponsors can meet the heightened expectations for data provenance.

For mid-size biotechs and MedTech organizations, the immediate action item is evaluating the foundational architecture of their observational studies. A registry designed only to satisfy a post-market surveillance requirement captures limited downstream value. A registry built on interoperable, pseudonymized infrastructure, with prospective ePRO built in from the start, can support subsequent label expansions and serve as a reusable data asset across the product lifecycle.

Organizations that build interoperable, audit-ready data infrastructure now will be better positioned to meet current FDA standards and to repurpose registry data for lifecycle management as their product pipelines mature.

Diagnostic: assessing your RWE readiness

Use the questions below to evaluate your organization’s current position relative to the 2025 and 2026 FDA guidance requirements.

For MedTech and device leaders

For pharma and biotech leaders

References

  1. FDA. “Use of Real-World Evidence to Support Regulatory Decision-Making for Medical Devices.” Finalized December 18, 2025. fda.gov/regulatory-information/…
  2. FDA. “M14 General Principles on Planning, Designing, Analyzing, and Reporting of Non-Interventional Studies That Utilize Real-World Data for Safety Assessment of Medicines.” Federal Register, March 4, 2026. federalregister.gov/…
  3. FDA. “Real-World Evidence: Considerations Regarding Non-Interventional Studies for Drug and Biological Products.” Draft guidance, March 2024. [In draft as of April 2026.] fda.gov/regulatory-information/…
  4. FDA. “Exploration of HL7 FHIR for Use in Study Data Created from Real-World Data Sources for Submission to the FDA.” Federal Register, April 2025. federalregister.gov/…

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What AI replaces in Phase 2/3 data management, and the edit check question your team hasn’t asked yet

April 9th, 2026 by

Event summary
April 8, 2026
5 min read

There’s a version of the AI-in-clinical-trials conversation that consists mostly of noise. Bold claims, proof-of-concept demos, vendors who say their system handles everything. Then someone asks what the FDA would say, how it fits existing SOPs, or who takes accountability when something goes wrong, and the conversation gets much shorter.

Derk Arts, CEO at Castor, and Alison Bishop, a data management specialist with close to thirty years of experience across small and large clinical research organizations, set out to have a different kind of conversation. One that was, in Derk’s framing, “very specific and very much grounded in reality.” The session ran for about forty-five minutes on March 31 and covered the full data management lifecycle: what AI handles well today, what it is approaching, and where the honest answer is still “not yet.”

The conversation opened where most teams are already starting: document generation. Medical writing, clinical data management plans, database validation protocols. Both Alison and Derk confirmed this is where AI has made the most visible inroads. The technology generates text, so people gravitated toward having it generate text. Alison noted that drafts of the DMP, the SAP, and supporting documents are natural starting points, though all require human review before anything goes anywhere near a sponsor. Both agreed this territory is important and getting better, but it’s not where the session was going to spend its time.

That time went to edit checks.

Derk framed the provocation directly: what if organizations moved away from writing programmed edit checks entirely, and instead deployed an intelligent system to flag problematic data in real time? Hard-coded rules replaced by contextual judgment. Alison’s answer was measured and specific:

“I think there are a number of things that we need to make sure that we work through in order to get there. And obviously sponsors are going to want evidence. They’re going to want proof that what they get at the end is as good as if not better than what they would get with a traditional model.”

Alison Bishop, data management specialist

She outlined what that evidence would look like: run both approaches on a study with known edit check history, compare what each system flags, and build a retroactive benchmark. Derk described how Castor applies this with Castor Catalyst, using real source data and patient journeys to validate AI performance against the historical record. The question most Phase 2/3 teams haven’t asked is not whether AI can flag data issues. It can. The question is whether you can prove it, to a sponsor, in a way that would hold up on inspection.

Query management sits directly downstream of edit checks, and the session moved there next. The same system that identifies a data issue can generate the query. But the more interesting design question is whether it should, and under what conditions. Alison and Derk worked through a risk-stratification model: adverse events and primary endpoints always route through mandatory human review; lower-risk data can move at a different pace once confidence is established. Derk described how confidence scoring in Catalyst serves as the configurable decision point for each query type. Alison put the broader principle in terms that will resonate with anyone who has watched data review backlogs grow:

“AI is taking away the burden of the volume of review… focusing us at the things that are going to have the biggest value.”

Alison Bishop

The governance question ran through all of it. Regulators will require accountability, system validation, and the ability to explain every AI-driven decision. Alison laid out what a human oversight model would actually need: review gates for the first patients, defined processes for model drift, deviation handling that feeds into existing quality management. An AI validation plan sitting alongside the standard data management plan, defining upfront which data the system handles autonomously, which routes require human sign-off, and how confidence thresholds were set, is the direction the session pointed to. Both agreed this is where things are heading.

The recording covers material this post doesn’t have room for.

Derk walks through Castor Catalyst‘s visual audit trail in detail (37:17): a replay capability that shows what the AI agent did and why, step by step, in terms a human reviewer can follow. He makes a counterintuitive point that’s worth the watch on its own. An AI creates a more complete audit record than a human doing the same work, because a CRA doing source data verification doesn’t log every step in their reasoning. The AI logs all of it.

The Q&A includes a direct exchange on automation bias (43:11): how you stop human review from becoming a rubber stamp when the AI is right most of the time. It gets into ensemble validation approaches and why the problem isn’t actually new to this industry.

And Alison closes with a use case worth pursuing (42:10): using AI to track clean patients and flag what still needs doing before an interim deliverable. Straightforward in theory. Harder than it sounds when your systems don’t talk to each other.

Watch the full session on demand

Forty-five minutes on where AI is actually landing in clinical trial data management, built for anyone making technology decisions in the space.

Watch now

Frequently asked questions

Can AI replace programmed edit checks in clinical trials?

Possibly, but not without evidence first. Alison’s position in the session was that sponsors will want proof that an AI-driven approach produces outcomes at least as good as traditional edit checks before they adopt it. The practical path is to run both models in parallel on a study with known edit check history, compare what each system flags, and build a retroactive benchmark. Derk described how Castor applies this with Castor Catalyst, using real source data to validate AI performance against the historical record before asking any sponsor to trust it.

How do you prevent human review of AI output from becoming a rubber stamp?

Automation bias (accepting high-quality AI suggestions without scrutiny) was raised in the Q&A. Derk described one approach Castor uses internally: running a separate AI validation step using a different model or model family to check the output of the first. This creates a consensus check rather than relying on a human reviewer to spot errors in a mostly-correct stream. Risk stratification is the other mechanism: routing the highest-stakes data (adverse events, primary endpoints) through mandatory human review, while lower-risk data moves at a different pace. Derk also made the point that this problem isn’t unique to AI. It’s already present when a CRA does source data verification, and at least with AI you have an audit trail of every decision.

Where should clinical data management teams start with AI adoption?

The session framed it as a progression tied to what you can validate and demonstrate to a sponsor or regulator. Document generation (drafting data management plans, SAPs, validation protocols) is where most teams start, with full human review. Edit checks and query management are the next steps, but both require evidence of equivalence or improvement before replacing the traditional approach. The key point Alison made: process redesign matters more than tool selection. Overlaying AI on an existing workflow rarely produces the efficiency gains clinical data management teams are looking for. The gains come from thinking carefully about where human involvement actually needs to be.

References

  1. Castor LinkedIn Live session: “What AI replaces in Phase 2/3 data management — and what it doesn’t.” Recorded March 31, 2026. Featuring Alison Bishop (data management specialist) and Derk Arts (CEO, Castor).

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Early-stage biotech’s AI advantage is a window that closes. Sol Babani and Derk Arts on how to use it.

April 21st, 2026 by

At the AWS Life Sciences Symposium the previous week, Solomon Babani, Founder and CEO of Symbiosis Advisors, sat through presentation after presentation on AI in clinical development. His takeaway: “There’s a lot of retrofit going on.” Companies were taking existing processes, existing workflows, and asking how to bolt AI onto them. He left mostly disappointed.

That framing opened a live conversation on April 15 between Sol and Derk Arts, MD, PhD, CEO and founder of Castor, on what AI strategy actually looks like for early-stage biotech clinical trials teams. An hour, no slides, no scripted pitch. The session covered why most AI guidance misses the point, where the FDA’s guidance framework actually helps, and what separates the teams that will get this right from the ones that won’t.

The distinction is not budget, team size, or therapeutic area. It’s whether you’re building or retrofitting. Early-stage biotech can build. Most of pharma can’t.

The greenfield window is real

Big pharma is stuck. Studies designed years ago, processes embedded in SOPs that cost more to change than to work around, and organizational immune systems that turn almost any innovation into a retrofit. Early-stage biotech teams are in a completely different position.

“I think it would be almost foolish not to entertain how to modernize your approach. You have the opportunity, you don’t have to worry about undoing legacy systems, legacies, processes, procedures, and all of that, and look for a more efficient way.”Sol Babani, Founder and CEO of Symbiosis Advisors (14:29)

That window closes the moment a team builds a first study on manual processes. The longer you wait, the more you are retrofitting too. Derk noted that Castor itself is working to remove manual data entry from the process entirely, describing how Castor Catalyst handles source document review and data capture with human oversight on the output rather than at the data entry stage. The goal, he said, is to create a digital trail that makes monitoring a remote and efficient process rather than a site visit.

FDA guidance: useful signal, limited map

Derk’s read on the current FDA AI guidance was specific: positive direction, weak on practical detail. His estimate was that about ninety-eight percent of the innovation actually happening in this space involves teams using existing models, not building their own. The guidance focuses heavily on model development. That maps to a fraction of what most teams are actually doing.

Sol’s answer: stop waiting for detailed guidance and start with a structured decision framework instead. Identify the business problem. Define the specific AI use case. Work through validation requirements and data quality. Estimate cost and timeline. Define what success looks like before you start. That sequence matters. Skipping any step is where implementations go wrong.

Build versus buy: the compliance variable

For data collection and electronic data capture infrastructure, Sol’s position was unambiguous: in a 21 CFR Part 11 environment, building your own compliance layer introduces validation risk that established vendors have already absorbed. The cost gap between buying sophisticated tools and building from scratch has collapsed. Most biotech teams don’t need to build. They need to choose well.

Human oversight is a methodology, not a checkbox

Sol was clear that human-in-the-loop oversight is not just a regulatory compliance requirement. It is an active design choice that shapes how a team builds its AI workflows from day one. Derk put the practical risk plainly:

“It just produces believable bullshit if you’re not careful. When you do it properly, it doesn’t do that. But if you don’t do it properly, it probably will.”Derk Arts, MD, PhD, CEO and founder of Castor (39:58)

The right setup requires a well-structured data source, a paid model with appropriate data protection agreements, and a documented review workflow. The shortcut through free public models is not a starting point. It is a risk that looks like one.

Derk also framed the financial stakes of getting AI right across the industry, noting that failed oncology studies represent billions of dollars in lost investment annually, as context for why modernizing clinical trial infrastructure matters beyond individual efficiency gains.

What’s in the full recording

The session covered more ground than the highlights above capture. Specific moments worth finding:

Sol walks through exactly why CRO economics are a structural obstacle to faster AI adoption in monitoring. A large part of CRO revenue is tied to monitoring visits, and he explains where the conflict actually sits and what biotechs can do to address it in negotiations. (26:00)

Derk breaks down what he calls the four-stakeholder problem in clinical AI: the vendor, the CRO, the sponsor, and the site, each with different definitions of what “saving money” means and different incentives to move toward or resist change. (29:04)

A detailed exchange on the buy versus build decision, including Derk’s specific reasoning for why building 21 CFR Part 11 compliant infrastructure against an existing EDC system would be “quite risky” for most biotech teams, and where Sol draws the line between cases that justify building versus buying. (22:13)

Sol’s five-step decision framework, presented in full with specifics on validation approach, training data requirements, timeline and cost estimation, and how to define what a successful implementation looks like at the end. (37:34)

The full conversation is available on demand. If you’re building your first clinical program or thinking through your AI strategy for an upcoming study, this is 60 minutes worth watching.

Watch the full recording

Frequently asked questions

How should early-stage biotech companies start with AI in clinical operations?

Start by identifying the specific business problem before choosing a tool. Sol Babani recommends a five-step approach: identify the business problem, define the specific AI use case, think through validation and data requirements, estimate cost and timeline, and define what success looks like before you start. Avoid applying AI broadly to all processes at once. The most common mistake is treating AI adoption as a technology project rather than an operational one.

What does human-in-the-loop mean for AI in regulated clinical environments?

Human-in-the-loop is not just a compliance checkbox. It is an active design methodology where a qualified person reviews and approves AI-generated outputs before they enter the clinical record. In a GCP and 21 CFR Part 11 context, traceable human oversight is required. The practical implication: early implementations should build full human review into the workflow, track the outcomes of that review, and use that data to justify a more risk-based approach over time. Teams that skip this step are also skipping the evidence base they will need to reduce review requirements in future studies.

Should early-stage biotech companies build or buy AI tools for clinical development?

For most clinical trial solutions use cases, buy. Building your own 21 CFR Part 11 compliant infrastructure introduces validation risk that established vendors have already absorbed. The cost gap between buying sophisticated AI tools and building from scratch has collapsed significantly. Building makes sense only when the use case is highly proprietary, the data cannot leave the organization, or no commercial solution covers the specific problem. For source document review, data capture, and monitoring support, commercial solutions with validated compliance frameworks are almost always the better choice for an early-stage biotech team.

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When the surgeon never touched the EDC: what the SAGES x Castor webinar covered

May 19th, 2026 by

The problem Sangrag Ganguli, MD, put on the table wasn’t abstract. “Anywhere from ten to forty five minutes of chart review per patient entry,” he said. “And this is a pretty big time commitment.” As General Surgery Resident and inaugural AIS fellow through the SAGES HPB solid organ committee at the University of Chicago Medicine, Sangrag oversees two national minimally invasive surgery registries, aMILES and aMIPS, spanning 21 centers across North America. At any given time, his team runs around 10 active research projects, most requiring someone to open an EMR, trace through operative reports and discharge summaries, identify the relevant variables, and enter them by hand.

The May 6 SAGES x Castor Catalyst webinar, hosted by Derk Arts, MD, PhD, CEO of Castor, was about what changes when that work looks different.

What the session covered

The hepatobiliary database Sangrag’s team maintains is granular. Preoperative data includes patient demographics, frailty index, and tumor imaging. Intraoperative fields capture surgical approach (laparoscopic, robotic, open), conversion status, blood loss, and pathology margins. Postoperative data requires prospective follow-up to record complications, readmissions, and cancer recurrence. Each entry means cross-referencing the operative report, clinic notes, discharge summary, and imaging records, then translating all of it into structured CRF variables. “It’s very careful, detail-oriented work,” Sangrag said, “but it’s also extremely time consuming.”

Variability compounds the problem. Different reviewers interpret the same documentation differently. The same reviewer introduces inconsistencies across a long patient list, especially when balancing chart review against clinical responsibilities. This is a data quality problem as much as an efficiency one.

Derk walked through Castor Catalyst in a live interface. The workflow: upload a surgical source document, Catalyst auto-de-identifies PHI, extracts structured data from free text, and presents each result with a source citation pointing back to its exact location in the document. The reviewer accepts, overrides, or adjusts, then submits into the electronic data capture system. “You basically go from manually entering it one by one to reviewing it in bulk,” Derk said, “but always being able to see where it found the information.”

Sangrag’s reaction was immediate. He walked through the variables Catalyst would handle in his own registries: liver segments resected, surgical approach, conversion status, blood loss, anesthesia type, and positioning.

“I think that’s where this platform really shines… not only giving you the data, but also showing you where that data was gotten from.”

— Sangrag Ganguli, MD, SAGES x Castor Catalyst webinar, May 6, 2026

The more substantial point came at the close. It was addressed directly to surgical trainees watching the session.

“I think the important point is that this automation really doesn’t replace you. [I] think this really elevates the role of the trainee that frees up your time to do more high value work like study design, learn how to do your own stats or refine your manuscript writing skills and so on. And those are the skills that really define academic development and your impact within research, not how well you can extract patient information from the EMR.”

— Sangrag Ganguli, MD

What’s in the full recording

The 35-minute recording includes several exchanges not covered here:

  • Sangrag walks through the complete variable set the hepatobiliary database collects, from preoperative labs to long-term cancer recurrence follow-up, giving a precise picture of what 10 to 45 minutes per chart actually means in practice 09:33
  • A direct exchange about trust in AI: Sangrag rates himself as a beginner in AI adoption and walks through exactly which research tasks he would and wouldn’t rely on AI for today 13:14
  • The live de-identification step: Catalyst draws redaction boxes on a surgical PDF in real time before extraction begins, and the interaction between the document view and the review interface shows what accepting or overriding a specific extracted value actually looks like 23:26
  • How a new extraction pipeline is calibrated from five example files, a subject matter expert voice-over, and synthetic training data, with performance benchmarked against a gold-standard reference set 27:19
  • The custom domain architecture: how a hepatobiliary surgical report domain built for aMILES can be copied across future studies without rebuilding the pipeline from scratch 34:05

The recording is 35 minutes. It’s built for PIs managing multi-center surgical registries, research residents, and anyone responsible for the data quality of a study that still runs on manual chart review.

Watch on demand

Questions about this session

Answers drawn from the session transcript. Production performance data sourced from Castor internal results — confirm with Derk’s team before publishing.

How does Castor Catalyst extract data from surgical source documents?

Castor Catalyst processes uploaded surgical source documents (operative notes, lab PDFs, discharge summaries) in three steps: PHI de-identification, AI extraction of structured data fields, and a human review interface that shows exactly where in the document each extracted value was found. The reviewer accepts, overrides, or adjusts each result before submitting to the EDC. The pipeline is calibrated using five example files from the study team, with a subject matter expert voice-over explaining what to look for in each document type.

Does Castor Catalyst replace the chart reviewer in surgical research?

No. Every extracted result requires a reviewer to accept or override before it enters the electronic data capture system. The role shifts from manual transcription to data quality supervision. Sangrag Ganguli addressed this directly to trainees during the session: the time freed from chart entry goes toward study design, statistical analysis, and manuscript development, and those are the skills that define a researcher’s impact within academic surgery.

Can a Castor Catalyst extraction pipeline be reused across multiple SAGES studies?

Yes. Standard domains (adverse events, demographics, labs, vital signs) work out of the box. A custom domain built for hepatobiliary surgical reports, such as the SAGES aMILES registry, can be copied across aMIPS and any future study using the same document types, without rebuilding the extraction pipeline from scratch. Derk Arts walked through this architecture during the session. For registries supporting real-world evidence generation across multiple centers, this reusability means one configuration pass serves the full research program.

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We asked a skeptical site owner to test our AI live. Here’s how it held up

July 2nd, 2026 by

Can AI actually help clinical trial sites? Short answer: yes, but only for the unglamorous work. It reliably pulls source-document data into the EDC without manual re-entry, with every value human-reviewed. It is not ready to run patient-facing outreach. That was the verdict when a skeptical site owner stress-tested it live, on air.

Most AI pitched at clinical research sites falls apart the moment someone who actually runs a site touches it. So we skipped the polished demo and did the opposite: we handed the controls to a skeptic, live, and asked him to test it however he wanted. On our recent LinkedIn Live, Brad Hightower, CEO of Hightower Clinical and a Castor advisor who runs sites across the Oklahoma City metro and helped start Save Our Sites (SOS), joined Castor CEO and founder Derk Arts to stress-test Castor Catalyst with his own modified files, on air. Brad’s opening position was blunt. He had tried AI voice for patient outreach and it “went terribly,” and he was in the middle of moving his sites back from electronic source to paper. If AI was going to earn a place in his workflow, it had to survive contact with real site paperwork first.

What the session covered

The honest state of AI at sites came first. Brad has tried the shiny tools and mostly walked away. His AI voice experiment for patient outreach collapsed the moment patients caught on:

Even in Oklahoma, people would catch on pretty quick that they were talking to AI. As soon as people figured it out, they were like, nope, I’m not talking to you. And even then, it would tell them things that simply weren’t true.

The AI he actually uses is practical: running his own recruitment ad campaigns instead of paying an agency, summarizing sponsor email threads, breaking down protocols for operational issues. Derk’s read on why the flashy use cases fail is that direct-to-patient AI is one of the first things people try and should be one of the last, because the guardrails, the latency, and the burden of supervising it are brutal.

Then the use case that actually pays off: getting data out of source and into the EDC without anyone re-typing it. That is what Catalyst does. It reads paper, site uploads, or patient-retrieved records (through a HIPAA release or a FHIR-based retrieval standard like TEFCA), structures the data, and hands it to a person to confirm before it enters the record. Derk on where the money leaks today:

There is a ton of money being spent just to make sure the patient has the diagnosis. It is ultimately five data points you need from the EMR. Are you going to pay a physician to pull that record and wait a couple of weeks, or do you automate that?

The third thread was the one that matters most: the hard part is not proving an AI can find something in a document. It is the workflow around it. Full traceability, an audit trail, automatic checks on patient ID, timepoint and study, and PII redaction handled by a local, offline module that never ships data to an AI vendor. That is where the real engineering, and the compliance, lives.

What only happens in the recording

The demo is the part worth watching. Brad came ready to test it properly, uploading files he had modified in advance to probe how it handled edge cases, then narrating what he was checking (around 36:00 to 44:00). A few moments to jump to:

The whole test ran on synthetic files, so nothing you see is real patient data.

Watch the full test

Brad’s verdict after testing it: “this experiment passed.” Watch the full live session, including the files he modified in advance and Derk’s candid take on what is and isn’t ready today, on demand here

FAQ

What is Castor Catalyst?

Castor Catalyst is AI-assisted source data extraction. It reads paper, site uploads, and patient-retrieved records, structures the data, and routes every value to a human to confirm before it enters the EDC. It removes manual data re-entry, not human oversight.

Does the AI replace the site coordinator?

No. Every extracted value is presented for a person to review, accept, or override before it is committed, with a full audit trail and a link back to the original source. The data point is still submitted in the coordinator’s name, flagged as coming from Catalyst.

How does Catalyst handle patient identifiers and privacy?

Uploaded files pass through a local, offline redaction module that identifies and removes PII before anything is processed. Redaction does not send data to an AI vendor, which is designed to fit site privacy policies.

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The Catch-22 of eConsent: The Missed Opportunity for Improved Clinical Trials

March 13th, 2025 by

eConsent technology is often praised for its potential to revolutionize clinical trials, yet it remains underutilized. The promise is clear: better comprehension leads to better satisfaction, retention, and protocol adherence. But the reality is much bleaker. Many implementations fail to deliver on this promise because they treat eConsent as a digital version of paper forms rather than as a transformational tool. This article explores the barriers holding eConsent back, backed by evidence from scientific literature, and proposes actionable solutions.

Why eConsent Matters: The Evidence

Multiple studies have demonstrated that interactive eConsent tools enhance patient comprehension compared to traditional methods. Taylor et al. (2021) showed that video-based consent interventions significantly improved participants’ understanding of trial concepts compared to standard consent forms. Their randomized trial across six clinical studies found:

“Participants exposed to the video had better understanding scores compared to those exposed to the standard consent form process.” (p = 0.020).

Moreover, Glaser et al. (2020) highlight that 85% of interactive, teach-back-based digital interventions successfully improved comprehension outcomes. They concluded:

“Interactive interventions, particularly with test/feedback or teach-back components, appear superior.”

Retention is another critical metric tied to eConsent. Skea et al. (2019) emphasize that participants drop out when they don’t feel adequately informed or when trial demands are misaligned with their expectations:

“Initial decisions to participate may not have been fully informed, leading to mismatched expectations and eventual attrition.”

These findings align with broader educational research demonstrating that interactivity and multimedia boost knowledge retention. For example, multimedia learning theory shows that combining visuals, text, and interactivity engages multiple cognitive pathways, enhancing understanding.

The Catch-22: Barriers to eConsent Adoption

Despite these proven benefits, eConsent remains stuck in a vicious cycle:

“The term ‘eConsent’ is often misunderstood, with many platforms offering only weak ‘design’ functions, which do not fulfill the full promise of digital tools.”

“Current practices often fail to define adequate patient comprehension and to ensure its assessment as part of the informed consent process, leading to frustration among sites.”

Breaking the Cycle: Unlocking eConsent’s Full Potential

  1. Example 1: Incorporating Interactive Features
     Taylor et al. (2021) demonstrated that video-based eConsent significantly improved satisfaction and understanding. If we give patients a short a video explaining key trial concepts, followed by an interactive quiz, we have already made real progress. This approach not only improves comprehension but also creates an audit trail for compliance.
  2. Example 2: Leveraging Educational Theory
     Studies on digital learning suggest that chunking information into bite-sized pieces and using visual aids enhances retention. A trial sponsor could use animated videos to explain randomization or potential side effects, coupled with glossary tools to define medical terms. Such tools could reduce dropout rates and enhance patient confidence.
  3. Example 3: Quantifying ROI
     Consider a large, global trial where dropout rates are reduced by just 5% through better eConsent. This could save millions in recruitment and operational costs. To encourage adoption, sponsors should calculate and share these savings, framing the initial investment as a long-term cost-saving measure.

Finally: just doing the right thing

Beyond the financial and operational arguments, eConsent represents an ethical obligation. As Pietrzykowski and Smilowska (2021) emphasize:

“The informed consent process actually leads to patients’ full comprehension of what they are consenting to. Unless this assumption is demonstrably true, the ethical viability of current medical experimentation practice is seriously flawed.”

This is also where I feel strongly. We are submitting patients to incomprehensibly long consent forms, knowing that comprehension will be low, but at least we’ve covered our legal bases.

Using multimedia and personalized tools isn’t just about better trials—it’s about respecting participants’ autonomy. When patients fully understand what they’re agreeing to, every stakeholder reaps the rewards.

To summarize, we must:

It’s time to move beyond treating eConsent as a checkbox and realize its full potential. The technology is ready—now the industry must commit to using it to create better, more ethical, and more successful clinical trials. Let’s break the cycle.

References

  1. Taylor et al. (2021). Randomized comparison of two interventions to enhance understanding during the informed consent process for research. Clinical Trials, 18(4), 466–476. doi:10.1177/17407745211009529
  2. Glaser et al. (2020). Interventions to improve patient comprehension in informed consent for medical and surgical procedures: An updated systematic review. Medical Decision Making, 40(2), 119–143. doi:10.1177/0272989X19896348
  3. Skea et al. (2019). Exploring non-retention in clinical trials: A meta-ethnographic synthesis of studies reporting participant reasons for drop out. BMJ Open, 9(6), e021959. doi:10.1136/bmjopen-2018-021959
  4. Vanaken et al. (2024). Effective eConsent strategies for every study: Utilizing the eConsent fit-for-purpose study framework. Applied Clinical Trials. Available online.
  5. Pietrzykowski & Smilowska (2021). The reality of informed consent: Empirical studies on patient comprehension—Systematic review. Trials, 22(1), 57. doi:10.1186/s13063-020-04969-w

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ePRO, eCOA 101: Everything You Need to Know About ePRO and eCOA

July 31st, 2025 by

What is eCOA?

Electronic Clinical Outcome Assessment (eCOA) represents the digital transformation of patient outcome measurement in clinical trials. Rather than relying on traditional paper forms, eCOA technology encompasses all electronically captured clinical outcomes data, fundamentally changing how trials collect and manage patient-reported information.

The shift from paper to digital has revolutionized clinical trial data collection, creating new opportunities for real-time monitoring and improved data quality. Modern electronic data capture systems integrate with eCOA platforms to create comprehensive clinical data ecosystems supporting both regulatory submissions and real-world evidence generation.

Understanding ePRO Within the eCOA Framework

Electronic Patient Reported Outcomes (ePRO) represents the patient-centric component within the broader eCOA framework. While these terms are often used interchangeably, ePRO is actually one of four distinct assessment types within the eCOA ecosystem:

  • ePRO (electronic Patient Reported Outcomes): Patient-reported symptoms, quality of life measures, and treatment experiences
  • eClinRO (electronic Clinician Reported Outcomes): Healthcare provider assessments and clinical observations
  • eObsRO (electronic Observer Reported Outcomes): Caregiver or family member observations, particularly important in pediatric or cognitive studies
  • ePerfO (electronic Performance Outcomes): Objective measurements captured through digital tools and devices

ePRO has gained particular prominence in patient journey optimization due to its direct connection to patient experiences and regulatory emphasis on patient-centered drug development. Decentralized clinical trials increasingly rely on ePRO data to capture patient experiences outside traditional clinic settings.

The Implementation Reality: Beyond Technology

eCOA implementation involves significantly more complexity than deploying a simple electronic survey. The process requires careful coordination of regulatory compliance, intellectual property management, and operational planning across multiple stakeholders.

Regulatory Framework: Regulatory agencies continue emphasizing electronic data collection approaches in clinical trials, with updated guidance supporting eCOA implementation. The FDA defines Clinical Outcome Assessments as measures that describe or reflect “how a patient feels, functions, or survives,” encompassing the four eCOA categories [FDA 2024][1]. The industry has responded with remarkable growth – the global eCOA solutions market reached $1.94 billion in 2024 and is projected to grow at 16.1% annually to $4.78 billion by 2030 [MarketsandMarkets 2024][2].

Intellectual Property Management: Many clinical trials utilize copyrighted assessment instruments that require specific licensing agreements. These instruments often carry usage restrictions and approval processes that can significantly impact implementation timelines and platform selection decisions.

System Integration Requirements: eCOA platforms must integrate with existing clinical trial infrastructure including EDC systems, clinical trial management systems, and regulatory submission processes. This integration requires sophisticated technical architectures and security protocols that meet clinical research standards.

Data Integrity and Compliance: Clinical trials require comprehensive audit trails, electronic signatures, and data lineage capabilities that support regulatory inspections. Organizations must ensure their eCOA platforms integrate with electronic consent processes and maintain data integrity throughout the study lifecycle.

BYOD vs. Provisioned Devices: Strategic Considerations

The choice between Bring Your Own Device (BYOD) and sponsor-provisioned device strategies represents a critical implementation decision with implications for patient experience, data quality, and operational complexity.

Provisioned Device Approach

Sponsor-supplied devices offer controlled environments with standardized hardware, operating systems, and security configurations. This approach provides consistency across all participants but introduces logistical challenges including device distribution, technical support, and patient training requirements.

BYOD Strategy Considerations

BYOD approaches leverage patients’ existing smartphones and tablets, potentially improving engagement through familiar interfaces and integration into daily routines. However, BYOD implementation requires careful attention to device variability, security protocols, and data equivalency validation across different platforms and operating systems.

BYOD adoption is accelerating rapidly, driven by cost-effectiveness and patient familiarity with personal devices. Research demonstrates the importance of patient-centered design and user experience for successful eCOA implementation, with electronic methods showing superior compliance compared to traditional paper-based approaches [Clinical Leader 2024][3]. Organizations considering BYOD strategies should evaluate current regulatory expectations and validation requirements for their specific study contexts.

Licensing and Validation Considerations

Copyright and licensing management represents one of the most complex aspects of commercial eCOA implementations, particularly for studies utilizing established clinical assessment instruments.

Licensing Requirements: Many clinical outcome assessments are copyrighted materials requiring study-specific licenses for electronic deployment. These licensing processes often involve multiple stakeholders and can require technical documentation, translation reviews, and approval workflows that extend implementation timelines.

Validation Processes: Electronic implementations of established instruments may require validation studies to demonstrate equivalence with paper-based versions. These studies ensure that electronic formats maintain the measurement properties and clinical validity of the original instruments.

Multi-Language Considerations: Global clinical trials require linguistic validation processes that extend beyond simple translation. These processes involve cultural adaptation, cognitive testing, and formal validation studies to ensure conceptual equivalence across different populations and languages.

Implementation Planning and Timeline Management

Successful eCOA implementation requires realistic planning that accounts for the various regulatory, technical, and operational requirements involved in electronic data collection deployment.

Timeline Variables: Implementation schedules depend on multiple factors including study complexity, regulatory requirements, licensing needs, translation requirements, and organizational readiness. Early identification of these variables helps establish realistic project timelines and resource allocation.

Change Management: Modifications to electronic systems after initial deployment often require coordination across multiple study components including visit schedules, data transfer processes, site training materials, and regulatory documentation. Planning for potential changes during the implementation process helps minimize disruption to ongoing studies.

Optimization Opportunities: Organizations with established eCOA capabilities may achieve implementation efficiencies through standardized processes, pre-validated instrument libraries, and streamlined approval workflows. These approaches can provide time savings when appropriate for specific study requirements.

Patient Engagement and Data Quality Benefits

The transformation from paper-based assessments to electronic systems addresses fundamental challenges in clinical data collection, particularly patient compliance and data accuracy.

Compliance and Completion Rates: Research has demonstrated that patients are significantly less compliant with paper diaries than previously assumed, with studies showing higher completion rates and more accurate data capture with electronic methods [Stone et al. 2002][4]. Electronic systems provide real-time data validation, immediate feedback to patients, and automated reminders that improve protocol adherence.

Patient-Centered Design: Modern eCOA platforms prioritize intuitive user interfaces and patient-facing technology improvements that accommodate diverse patient populations, including considerations for age, technical literacy, and accessibility requirements. This patient-centric approach recognizes that patients are experts in their own experience and should be empowered to provide accurate, meaningful data.

Real-Time Data Quality: Electronic capture enables immediate data validation, range checks, and consistency monitoring that identifies potential issues before they impact study integrity. This real-time capability supports both patient safety monitoring and regulatory submission requirements.

Implementation Best Practices and Workflow

Successful eCOA implementation requires systematic planning that addresses both technical and operational considerations throughout the study lifecycle.

Pre-Implementation Planning: Effective eCOA deployment begins with comprehensive assessment of patient populations, study requirements, and operational capabilities. This includes evaluating patient technology access, site infrastructure, and regulatory requirements across all study regions.

User Experience Validation: Testing eCOA interfaces with representative patient populations ensures usability across diverse demographics and technology comfort levels. This validation process should include cognitive interviews and usability testing to identify potential barriers to completion.

Training and Support Infrastructure: Comprehensive training programs for both sites and patients, supported by accessible technical support, are critical for successful adoption. This includes developing multilingual support materials and establishing clear escalation procedures for technical issues.

Data Integration and Monitoring: eCOA platforms must integrate seamlessly with electronic data capture systems and provide real-time monitoring capabilities that support both operational oversight and patient safety monitoring.

Technology Evolution and Future Considerations

Emerging Technologies: The eCOA field continues evolving with advances in mobile technology, wearable devices, and digital therapeutics integration. Organizations are increasingly exploring how these technologies can enhance patient engagement while maintaining regulatory compliance and data integrity.

Regulatory Adaptation: As regulatory agencies gain experience with electronic data collection approaches, guidance continues evolving to address new technologies and implementation approaches. Staying current with regulatory expectations remains essential for successful eCOA deployment.

Industry Standardization: Professional organizations and industry consortiums continue developing best practices and standardized approaches that can improve implementation efficiency and reduce regulatory review timelines across the industry.

 

Looking to implement eCOA solutions for your clinical trials? Discover how Castor’s integrated eCOA platform combines regulatory compliance with patient-centered design to accelerate your clinical research objectives.

Frequently Asked Questions

What is the difference between eCOA and ePRO?

eCOA (Electronic Clinical Outcome Assessment) serves as the umbrella term for all electronically captured clinical outcomes data. ePRO (Electronic Patient Reported Outcomes) represents the patient-reported component within the eCOA framework, alongside eClinRO (clinician-reported), eObsRO (observer-reported), and ePerfO (performance-based) outcomes.

How long does eCOA implementation typically take?

Implementation timelines vary significantly based on study complexity, regulatory requirements, licensing needs, translation requirements, and organizational capabilities. Early identification of these factors and realistic project planning help establish appropriate timelines for specific study contexts.

What are the key considerations for BYOD vs. provisioned devices?

The choice depends on study requirements, patient populations, regulatory considerations, and operational capabilities. BYOD approaches may improve patient engagement through familiar interfaces, while provisioned devices offer controlled environments. Both strategies require careful attention to data quality, security, and regulatory compliance.

How do licensing requirements affect eCOA implementation?

Many clinical assessment instruments are copyrighted materials requiring study-specific licenses for electronic deployment. These licensing processes can involve multiple stakeholders and approval workflows, making early planning and stakeholder engagement important for realistic timeline management.

 

References

  1. U.S. Food and Drug Administration. (2024). Clinical Outcome Assessment (COA) Frequently Asked Questions. Available at: https://www.fda.gov/about-fda/clinical-outcome-assessment-coa-frequently-asked-questions
  2. MarketsandMarkets. (2024). Electronic Clinical Outcome Assessment Solutions Market Growth, Drivers, and Opportunities. Available at: https://www.marketsandmarkets.com/Market-Reports/ecoa-solutions-market-87857774.html
  3. Clinical Leader. (2024). The Rise of Electronic Clinical Outcome Assessments (eCOAs) in the Age of Patient Centricity. Available at: https://www.clinicalleader.com/doc/the-rise-of-electronic-clinical-outcome-assessments-ecoas-in-the-age-of-patient-centricity-0001
  4. Stone, A.A., et al. (2002). Patient non-compliance with paper diaries. BMJ, 324(7347), 1193-1194. Available at: https://www.bmj.com/content/324/7347/1193

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Essential Guide to Electronic Data Capture (EDC) in Clinical Trials

May 2nd, 2019 by

Electronic data capture (EDC) systems have become important tools for academic, medical device, biotech and pharmaceutical research. If you are a medical researcher but aren’t yet familiar with the concept of electronic data capture, here is a guide to EDC, its use in clinical trials, and how it can greatly benefit your research.

Electronic Data Capture Clinical Trials Guide Screen

Let’s begin with a quick overview of EDC.

What is Electronic Data Capture?

In a nutshell, electronic data capture (EDC) is a system of capturing and managing clinical trial data on a digital platform to replace traditional paper-based data capture. Using such a system not only lets you capture data securely but also expedites the research process and ensures data reusability. Most EDC systems nowadays are cloud-based, ensuring secure access from anywhere.

When researchers conduct a clinical study, they collect information about their patients using Case Report Forms (CRFs). For example, a diabetes study with a demographics form and a lab result form.

CRFs are traditionally paper-based, and when the study is completed, the CRF documents are gathered and sent to the sponsor, who then transfers the data to a statistical analysis tool. Not only is this a lot of manual work, but also a LOT of paper, and in some cases this paper must be stored for 15 years!

However, by using an electronic data capture system, these Case Report Forms are filled out on a web-based platform, and the data is stored directly in a central database. This saves the hassle and risks of storing potentially sensitive data on paper, Excel, and other less secure and non-reusable methods. An electronic data capture system helps ensure compliance with medical data privacy, security, and Good Clinical Practice regulations. Furthermore, most professional EDC systems today provide automatic identification of data errors. This helps the Monitors to ensure the highest data quality. This process significantly improves the quality of the data and decreases the time needed for data review and corrections.

For example, Castor’s cloud-based EDC system enables researchers to easily capture high quality, reusable data. It is tailored specifically for use in academic, medical device, biotech, and pharmaceutical research, and compliant with patient data privacy laws and GCP.

So what features should you look for in an EDC and how does this work in practice? Read on to learn how 5 organizations benefited from using Castor EDC for their medical research.

What features should you look for in an EDC?

  1. A user-friendly interface, designed to meet the specific needs of medical researchers
  2. A user-access controlled platform that facilitates collaboration among multiple sites and researchers around the world
  3. The ability to set data limits for all captured data to reduce human error
  4. Assured compliance with privacy and data protection policies, ensuring that mandatory regulations are met and that research data is collected and maintained
  5. Seamless integration with existing tools, wearables, legacy systems, and other technology
  6. The ability to reuse research data for the initial research team, the statistical analysis team, and any future users of the data
  7. Access to proactive support and technical advisors to help set up your clinical trials

What are the benefits of using Electronic Data Capture?

Here’s how 5 organizations benefited from using an electronic data capture system for their medical research.

EDC benefit #1: Ease of set up

Setting up a complete clinical study in an EDC system can be relatively easy, depending on the system you choose. For example, Castor EDC’s user interface is designed to be intuitive and researcher-friendly. Castor’s online video training, monthly webinars, and prompt customer service make setting up your study very simple. For more complex studies, one of our clinical study experts can provide assistance.

Why Iantech Inc chose Castor to conduct their medical device randomized controlled trial.

Iantech Inc is a medical device company that performed a clinical study on a minimally invasive technique for the removal of cataracts. The study included 150 subjects and was concluded in only two surgery days. The Iantech team was impressed with how comprehensive and complete Castor EDC was for their company. Within a few days after the study protocol was finalized, they were able to use Castor and enable a fully operational research base and data set. All this at only a fraction of the cost of other systems offering similar functions.

Case Study Graphic - Iantech

EDC benefit #2: Easy & secure access

One of the main benefits of using a cloud-based EDC system is that it makes collaborating with researchers from across the world easy and more importantly, secure. It allows researchers to set up a study online and simply invite other collaborators via a web link. Collaborators can access the study with a single click and without any paper. In addition, the principal investigator (PI) can set different permissions depending on the collaborator’s role.

Why Ghent University Hospital used Castor to capture data from 150 centers across the world.

The DIANA Study is an international study on antibiotic use, initiated by Ghent University Hospital in Belgium. It includes a thorough 436 page CRF, incorporating data from 150 different centers across Europe, Asia, Australia, and the Americas. Without a cloud-based solution, such a global study would have been impossible.

“Without Castor EDC, we would never have been able to collect such a large amount of data across all centers with the limited amount of local funding they received. And a paper CRF of this length would certainly not have been manageable.”
– Dr. De Waele, Ghent University Hospital in Belgium

EDC benefit #3: Capture high-quality data

A major benefit of using an EDC system is that it allows researchers to add dependencies and validations to the research forms prior to data collection, significantly improving data quality. This ensures that the data collected is accurate, flagging errors or results falling out of predetermined parameters. For example, when collecting data on subjects’ blood pressure, the PI can set parameters ensuring that only realistic data can be added, significantly reducing the possibility of human error in data entry.

Why ClinFlows used a single electronic data capture system for all researchers to capture high-quality data.

INTRAGO II is a two-year global investigator-initiated study on the use of radiotherapy during brain tumor surgery. The study included data from centers in Germany, Australia, Canada, Peru, Spain, and the United States. The study needed to be accessed by all global participants and there was a limited budget for monitoring. For this reason, clinical services company ClinFlows carefully built multivariate validations and dependencies across their forms using Castor. This guaranteed data quality and significantly reduced the cost of data corrections after the study.

EDC benefit #4: Reduction in time and cost

Why PrevER Trial used Castor to collect and store large datasets.

The PrevER trial investigated the prevalence of Hepatitis C in visitors to the emergency department at Ziekenhuis Oost Limburg. Given a large number of inclusions (3000) and sensitive patient data, initiator Dr. Rob Bielen remarked –

“Had we used Microsoft Excel and Access, it not only would have been impossible to comply with all the data protection requirements, but such a large dataset would have taken days to prepare for analysis.”
– Dr. Rob Bielen, Hasselt University/Ziekenhuis Oost Limburg Genk

EDC benefit #5: Data can be reused for future research

After publication of the study results, most datasets end up in a drawer and forgotten. When using an EDC system such as Castor, all data is stored in a central database in a standardized format. This allows researchers and their colleagues to easily reuse the data for replication and meta-analyses, to combine datasets, and to test completely new hypotheses.

An electronic data capture platform enables researchers to collect all the study data in one place, whether related to primary or secondary outcomes, patient-reported outcomes, or safety-related data. This includes electronic Case Report Forms (eCRF), electronic Patient Reported Outcomes (ePRO), electronic Clinical Outcome Assessments (eCOA), and Serious Adverse Events (SAE). Castor EDC also helps researchers to easily import legacy data or connect it to other systems (CMS, QMS, device software) through an API.

Additionally, when performing multiple post-market studies with similar end-points, using a single EDC makes it easy to reuse study setups and eCRFs.

EDC benefit #6 Compliance

One of the principal reasons for researchers to switch to an EDC system is compliance. Clinical research is highly regulated, and data, identity, and privacy regulations are becoming increasingly strict.

When capturing data using paper, Microsoft Excel, Microsoft Access, Google Forms or SPSS, it is virtually impossible to comply with all mandatory regulations which exist to protect sensitive medical data. Using a single platform for data management is critical to ensure ICH-E6 – Good Clinical Practice (GCP) and General Data Protection Regulation (GDPR) compliance. Audit trails, user access rights, and data encryption are almost impossible to control using paper copies, Excel, or other legacy systems. For example, GCP requires researchers to keep an audit trail, tracking changes in the data.

In addition, patient data needs to remain unidentifiable and secure. Papers need to be transported and Excel files can be copied and stored on a personal computer, posing major security risks. It is advisable to invest in an EDC system that allows for on-demand data access and reporting for different uses while offering meticulous data security and automatic backups. Castor EDC offers this to its users, complies with GCP and GDPR and has all relevant ISO certifications.

How AMC used Castor to become a leader in data security and compliance.

The Department of Anesthesiology in the Academic Medical Center (AMC) in Amsterdam conducts several research studies. Many were still performed using Microsoft Excel, Google Sheets and Google Forms, a process that was error prone and carried the risk of data leakage. To address this, they secured a department-wide license with Castor EDC, helping to ensure Good Clinical Practice and compliance with patient data privacy laws. They now have a much better overview of the trials running in their department, and significantly reduced their liability risk.

Interested in learning more?

Electronic data capture systems have become essential tools for contract research organizations (CROs), academic researchers as well as medical device, biotech and pharma companies. Globalization and advances in worldwide collaboration in medicine mean that researchers lagging behind in usage of this technology might find it increasingly difficult to keep up.

Castor EDC offers a user-friendly, affordable, and compliant solution for academic researchers and commercial companies. Without having prior technical knowledge, researchers can build a study in just a few clicks using the intuitive Form Builder (simply define the data fields and start collecting high-quality data!). Plus, Castor’s world-class support team is available when you need them.

 

Frequently asked questions

What is electronic data capture (EDC)?

EDC is software that replaces paper case report forms, letting clinical research teams collect, manage, and store trial data electronically in a secure, auditable electronic data capture system.

What should you look for in an EDC system?

Look for fast study build and configuration, regulatory compliance (21 CFR Part 11 and GCP), strong data validation and query management, integrations, and responsive support.

What are the main benefits of EDC over paper?

EDC reduces transcription errors, speeds up data cleaning and query resolution, improves data quality and compliance, and gives teams real-time visibility into their study data.

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What to look for in an EDC system: a practical guide for clinical trial teams

November 16th, 2022 by

Every clinical trial generates data across dozens of forms, sites, and time points. How that data gets collected, cleaned, and transferred determines how much of your team’s time goes to administration versus science. The right electronic data capture (EDC) system removes friction at every stage. The wrong one creates it.

This guide covers what an electronic data capture system actually does, the five operational benefits that matter most for study teams, what to look for during evaluation, and how EDC fits into the broader architecture of modern clinical trial solutions.

What is electronic data capture?

What is electronic data capture?An electronic data capture (EDC) system is software used to collect, clean, transfer, process, and store data in clinical trials. EDCs are used by contract research organizations (CROs), sponsors, and sites to conduct both simple and complex clinical trials in all phases of research. By using electronic records to capture and manage data on a digital platform, trial sponsors eliminate the need for traditional paper-based data collection. They can capture data securely and expedite the research process while ensuring data reusability.

Most EDC systems today are cloud-based so users can get secure access from anywhere. These systems collect varying types of data depending on the therapeutic area related to the clinical trial. For example, the data forms used in an oncology study might include:

The benefits of EDCs in clinical trials

The benefits of EDC in clinical trialsToday, clinical trials are typically expensive and time-consuming. Worse yet, they often create a burden for their participants. Trial sponsors are challenged to optimize every aspect of the research and development process to accelerate time to market, reduce the risk of failure, and retain participants along the way—all while staying on budget.

With the help of EDCs, sponsors can build clinical trials that meet the needs of regulators, payers, and participants. By using a foundational data platform that is flexible and interoperable, researchers speed up their research while lowering costs. Here’s how.

1. Incorporate real-world data (RWD) collection

An EDC solution simplifies and streamlines data collection. Once the data collection phase is complete, statistical processing can happen quickly. An EDC supports real-world evidence collection by:

Real-life example: COVID-RED

Julius Clinical’s COVID-RED (Remote Early Detection) study used wearable sensor technology and a smartphone app to measure participant vitals and provide personalized advice to get tested for COVID-19, even before symptoms appeared. The trial sponsors knew recruiting and collecting data from their 20,000-participant target would be incredibly challenging under ordinary circumstances—it would require multiple sites, complicated administrative processes, and in-person participant visits. And with 7,000 participants from a high-risk group safety during the pandemic was an issue.

Julius Clinical partnered with Castor to build an eClinical platform to run their trial remotely from start to finish. Through Castor’s clinical trial platform, over 17,800 participants were engaged and retained remotely, while data was automatically collected from thousands of medical devices and pushed into Castor EDC. According to the team at Julius Clinical: “Castor’s infrastructure ensured we were able to effectively carry out this study while keeping administrative burden, costs, and losses in participant engagement to a minimum.”

Julius Clinical's COVID-RED (Remote Early Detection) study

2. Directly capture global data

Clinical trials can be a global affair, with participants and researchers spread throughout the world. An EDC supports accuracy in international clinical studies through features like legible entries and automatic calculations. EDCs support global clinical trials by:

Real-life example: WHO Solidarity Trial

To combat the largest pandemic in recent history and generate sufficient evidence of the efficacy and safety of potential treatments, the World Health Organization (WHO) needed to mount a massive clinical trial.

The WHO needed a user-friendly system that they could deploy globally with 24/7 support. It needed to be advanced enough to support adaptive medication allocation and real-time reporting. Finally, offline data capture was a requirement due to unreliable internet in remote locations. The WHO approached Castor to help solve these challenges and quickly selected Castor as its sole EDC provider for the trial.

Castor quickly implemented a CRF in six languages, developed an adaptive randomization algorithm fitting WHO specifications, developed an offline capture tool for areas with poor telecom infrastructure, and provided 24/7 support.

“Launching and executing this ongoing trial is a remarkable achievement. In 6 months, the trial team accumulated information on the collective experience of more than 11,000 hospitalized patients in settings with varied and evolving standards of care.” – New England Journal of Medicine

3. Automate for the next generation of trial designs

Gone are the days of tedious manual data entry. Automation within clinical trials supports faster research and can speed up the return on investment of a trial. Automation within EDCs can benefit researchers by:

Real-life example: AusculThing

AusculThing seeks to transform the way healthcare providers detect heart murmurs by enabling them with a far more precise tool than the human ear. To train the algorithm to work more effectively, they would need to collect an enormous amount of audio and diagnosis data across 5 different sites.

The AusculThing team turned to Castor’s eSource platform, a system that would enable them to collect, capture, and process a significant amount of patient data from several sources, and feed it directly into Castor’s EDC. By the end of the trial, researchers had collected audio data recorded by a digital stethoscope and additional diagnosis info from over 1,700 patients – all without the need for manual source data verification. With data that’s healthy, integrated, and accessible through one interface, AusculThing can now train its AI algorithm much more efficiently and make considerable strides in creating an auscultation system that’s more accurate and efficient for healthcare systems worldwide.

Castor’s eSource was used to connect all data sources

4. Streamline collaboration workflows

Throughout studies, trial teams, monitors, and auditors need to capture, process, and access data from anywhere, at any time. EDCs allow data to be quickly shared with relevant personnel, enabling efficient exchange of information by:

Real-life example: RSP Systems—Using Monitoring to Track Study Progress

Over the past few years, RSP Systems has worked to bring their GlucoBeam monitor to market as a non-invasive and convenient glucose monitoring technology for diabetes patients. To do so, they’ve conducted multiple small clinical trials testing various settings on the device to improve protocols, estimate the signal-to-noise ratio, and increase the accuracy of the measurements. But the success of their studies depended on gathering highly accurate data ready for analysis. And with multiple trials running—some multicenter—they needed a bird’s eye view of all their studies with minimal manual effort.

Using Castor EDC, RSP Systems can now capture clinician, patient, device, and any other external data and get a complete overview of all the data linked to an individual patient. In particular, they’ve made great use of the monitoring feature in Castor EDC for a comprehensive overview of all the queries, data validations, and dropped verifications in each of their studies. They could find all the active validation fields in their study forms, reports, or surveys and easily filter by the type—Exclusion, Warning, and Message. From there, they could find out from which site the validation errors originated, compare the field values with the reference values defined during the validation setup, and jump to the step with the validation errors.

Woman testing glucose level with continuous glucose monitor

5. Manage mid-study changes with ease

EDC systems are flexible and adjustable, helping to meet the needs of the study as they change throughout the process. They support mid-study changes by:

Real-life example: AKRN Scientific Consulting

AKRN Scientific Consulting joined forces with a medical device manufacturer to innovate a way to keep a donor heart oxygenated in transport to its recipient. Together, they designed a clinical study to test their transportation solution. They then partnered with Castor to help design a clinical research ecosystem that would keep up with the evolving needs of the study.

As the randomized clinical trial expanded into several sites in different countries, study managers wanted to incorporate feedback from various study monitors and participants while still accommodating the varied regulatory and clinical needs. The protocol needed to be a living document that could evolve, reflecting the advice and parameters of each site. Fortunately, Castor’s eCRF made this possible, allowing changes to be quickly programmed and automatically uploaded to each site. As a result, each site always had the most up-to-date version of the eCRF.

What features should you look for in an EDC?

While choosing an EDC is a significant investment of both budget and time, the operational savings over the course of a study make it well worth the evaluation effort. Here’s what to prioritize:

How EDCs fit into the future of clinical research

Currently, clinical trials use isolated technology tools—such as eCRFs—to collect, manage, and analyze data. eCRFs are study forms built into EDCs that let researchers and clinical staff enter data directly into the system. These digital, typically web-based questionnaires ensure data compliance with privacy, security, and Good Clinical Practice regulations. Researchers can tailor eCRFs to fit each study, saving time and money.

While eCRFs represent a meaningful improvement over paper-based data entry, they remain a component within a broader, connected ecosystem rather than an end in themselves. The clinical trial industry is moving from siloed, cumbersome processes to decentralized clinical trials and fully connected, interoperable platforms for collecting and analyzing data. That shift involves pairing an EDC with the broader eClinical stack—eConsent, scheduling, televisits, and data from devices, consumer wearables, and patient apps.

An API architecture setup allows data collection from almost any source, and the goal is straightforward: a single system to collect, store, and process all clinical trial data. Therapy breakthroughs happen faster when researchers have access to affordable, user-friendly, and compliant data solutions. In just a few clicks, researchers can build a study with Castor’s clinical trials platform—without any prior technical knowledge—and grow alongside Castor as they move into fully integrated trials.

Frequently asked questions about EDC systems

What is the difference between an EDC system and a CDMS?

An EDC (electronic data capture) system is the tool used to collect and clean clinical trial data at the point of entry. A CDMS (clinical data management system) is the broader infrastructure that manages data across the full lifecycle of a study, including storage, processing, and export. In modern platforms, the two functions are typically combined: the EDC captures data from sites, devices, and patients, while the CDMS layer handles validation, query management, and data lock.

How does an EDC integrate with ePRO and eCOA tools?

Modern EDC platforms connect to ePRO and eCOA solutions via API, allowing patient-reported data to flow directly into the trial database without manual transfer. The EDC applies the same edit checks and data validation rules to patient-entered data as it does to site-entered data, maintaining a single audit trail. This is especially important in studies where patient-reported outcomes are part of the primary or secondary endpoint package.

What should I look for in an EDC for a decentralized or hybrid clinical trial?

For decentralized clinical trials, the critical EDC requirements are: remote participant access for direct data entry, device and wearable data ingestion via API, offline data capture capability for sites with unreliable connectivity, and native ePRO/eCOA integration. Additionally, look for built-in eConsent workflows and the ability to configure site-specific data collection rules without rebuilding the entire study structure.

What data standards should an EDC support for regulatory submissions?

For FDA and EMA submissions, CDISC standards are the primary requirement: CDASH for data collection forms and SDTM for data submission. Your EDC should either natively produce SDTM-compliant outputs or integrate with a data transformation layer that maps from the collection format to submission format. Systems that build CDASH compliance into the eCRF design stage reduce the transformation burden significantly at data lock.

How does an EDC reduce data errors and query burden in clinical trials?

EDC systems reduce errors at the source through built-in edit checks, plausibility constraints, and range validations that flag problems the moment data is entered—before it becomes a query. Auto-generated queries notify data managers and sites in real time, while medical coding standardizes terminology across sites and countries. The result is fewer discrepancies at database lock and less time spent on manual query resolution during the study.

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The New Standard in Participant-Centric Trials: What Castor’s 2025 Updates Reveal About the Future of Clinical Research

May 28th, 2025 by

“We’re merging consumer-grade digital expectations with the complexity of clinical trial compliance.” — Lisa Charlton, Chief Product Officer at Castor

The May 2025 Castor Product Spotlight wasn’t just a feature walkthrough—it was a directional statement. As the boundaries between real-world data (RWD), eCOA / ePRO, direct-to-patient (DTP) delivery, and digital-first engagement continue to blur, Castor has placed a strategic bet on the convergence of flexibility, compliance, and user experience.

In a 30-minute LinkedIn Live session, Lisa Charlton and Conal Nolan pulled back the curtain on the latest evolution of Castor’s platform: frictionless onboarding, end-to-end workflows, modular flexibility, and a renewed focus on reducing both site and participant burden.

Here’s what it all signals—for the future of trials, for technology buyers, and for participants themselves.

Designing With the Participant, Not Just the Protocol

Charlton opened by reframing a persistent challenge: participant-facing technology has historically lagged behind the user experiences people expect from banking, e-commerce, and health apps.

The core thesis?

“If I can bank with an OTP on my phone, I should be able to consent to a trial with the same ease.”

The Castor platform now offers just that—an OTP-first authentication experience that removes login friction without compromising on security. But more critically, it sets the tone for what follows: every subsequent step in the workflow is participant-centered and seamlessly integrated, from recruitment landing pages to eConsent and follow-up eCOA / ePRO tasks.

“You only get one chance to keep a participant engaged. We treat every first touchpoint with that in mind.” — Conal Nolan

From Static Consent to Adaptive Engagement

The traditional approach to informed consent is increasingly incompatible with remote and global studies. Castor’s eConsent experience is built with real-world comprehension in mind: embedded videos, study FAQs, and optional live scheduling links elevate participant understanding while reducing dropout due to confusion or mistrust.

The flexibility doesn’t stop at consent. Sponsors and CROs can control whether consent is self-serve or requires investigator oversight, with real-time notifications and audit trails keeping the process accountable. One-time password verification is integrated at critical steps like form signing—securing both the participant journey and regulatory compliance.

End-to-End DTP: One Platform, Modular by Design

Castor’s 2025 platform advancements reflect a strong commitment to an API-first, modular architecture, allowing study teams to build personalized workflows without starting from scratch.

This was brought to life in the live demo: a participant lands on a branded recruitment page, completes a pre-screener, and seamlessly progresses through eConsent and baseline forms—all on their own device, without app downloads or account creation.

Once enrolled, participants receive personalized SMS alerts prompting follow-up actions or reminders to open Castor Connect, the native mobile app supporting push notifications, offline use, and biometric login.

“Our aim is not to force patients into a rigid protocol workflow, but to adapt the workflow to their lives.” — Lisa Charlton

Real Results from Real Studies

The feature set isn’t theoretical—it’s validated in global real-world studies.

Case Study 1: Fully Remote, Infectious Disease RWE Trial

Case Study 2: Hybrid Model with Remote Capture

These examples demonstrate Castor’s ability to scale across therapeutic areas, geographies, and operational models—delivering both high-touch service and software under one roof.

Compliance, Resilience, and the Road Ahead

Key compliance features—like audit trails distinguishing participant-entered data, offline functionality in Castor Connect, and eConsent audit logs—reinforce the platform’s regulatory readiness. And in scenarios where internet connectivity drops mid-form, auto-saving ensures that no data is lost.

Looking ahead, Castor is developing on-site eCOA / ePRO workflows to further reduce friction in hybrid trials. The forthcoming model will enable data collection at sites with seamless handover to remote follow-up, ensuring no action or data point is missed—even in fluctuating trial environments.

“This is the direction the industry is heading—flexibility without compromise.” — Conal Nolan

Final Thoughts: From Tech-Driven to Participant-Led

Castor’s updates reflect a broader shift in clinical research: technology can no longer be an operational afterthought. It must serve the dual goals of scientific rigor and participant empathy.

The 2025 Product Spotlight makes it clear: Castor is building for a world where participants expect digital fluency, and study teams need tools that meet those expectations—while accelerating timelines, increasing retention, and improving data quality.

For RWE leaders, digital trial managers, and platform evaluators, this isn’t just a better experience. It’s a strategic imperative.

Explore how Castor’s participant-first technology can help you deliver more compliant, engaging, and efficient studies. Let’s build the next generation of trials—together.

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How a 3-Person Team Got to Phase 3 — Without the Overhead

June 26th, 2025 by

A Hands-On Approach to Global Trial Execution

Gameto is a 20-person biotech. Their clinical operations team? Just three people. Yet that trio has designed and run nine studies across Peru, Mexico, Australia, and the United States—on their own infrastructure, without a big-box CRO, and without hiring dedicated data managers. They’re now in Phase 3 in the U.S. and already have commercial access in several countries.

What makes this story worth your time is how little was outsourced. Gameto didn’t hand off trial setup to consultants. They trained embryologists during live egg retrievals, used existing site relationships for implementation, and configured studies directly in Castor. Fast, focused, and regulator-ready.

“You’d be surprised what you can accomplish when there’s no other choice.”

— Sabrina Piechota, Clinical Ops, Gameto

Study Build in Weeks, Not Months

One randomized trial in Peru went from protocol to go-live in three weeks—in Spanish. It covered patient demographics, hormone labs, embryology results, pregnancy data, adverse events, image uploads, and randomization. The build came from Castor’s documentation and helpdesk—no special training, no delays.

“We don’t have six months to set up a database. We have three weeks.”

— Christian Kramme, CSO, Gameto

Data That Passes FDA Review

A 38-patient dataset—30 Fertilo, 10 control—moved the product straight into FDA Phase 3. Clean audit trails and rapid CRF versioning kept regulators comfortable.

“We’re constantly learning, so the platform had to let us adjust on the fly without losing quality.”

— Christian Kramme

Localization Without the Bottleneck

Modular CRFs with multilingual support let the team reuse core forms while adding local fields. Most issues were spotted through direct CRF review and fixed in real time—no extra headcount required.

A Global Head-to-Head Study, Run Lean

Next, Gameto will compare Fertilo to conventional IVF in Europe, Australia, the Middle East, and Southeast Asia. Same three-person core team, same platform, bigger impact.

“We’re not trying to build a giant apparatus. We want to stay nimble, stay close to our data, and scale smart.”

— Christian Kramme

Long-Term Follow-Up Built In

Maternal safety and two-year infant development metrics are already embedded in the registry. Patients complete milestone questionnaires every six months, feeding directly into the Castor database.

Watch the Full Conversation

This article distills key points from the fireside chat between Derk Arts (Castor CEO), Christian Kramme (Gameto CSO) and Sabrina Piechota (Gameto, Embryology Department Manager). For the complete discussion—including audience Q&A—watch the on-demand webinar here:

How Gameto’s Team Built a Phase 3 Trial Engine from the Ground Up (On-Demand)

What Larger Teams Can Learn

Speed is only half the story; precision is the other. By keeping data close to the scientists and trusting a flexible EDC, Gameto shows that lean execution can satisfy regulators and accelerate timelines. For sponsors tired of bloated vendor chains, this is proof that smaller, smarter teams can win.

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