Clinical Research & Clinical Trial Management | Project Owner

How Many Software Systems Does One Clinical Trial Actually Need? Ask someone outside the industry to guess how many software systems a single clinical trial runs on, and they'll probably say two or three: something to collect patient data, something to manage the study. Ask someone who actually staffs a site, and the answer is closer to twenty. This isn't exaggeration. It's documented, repeatedly, across multiple independent industry surveys. And once you start counting, the financial and human cost of that sprawl becomes hard to ignore. The inventory: what actually runs a trial A realistic multi-site trial typically touches systems across at least six layers: Core eClinical systems EDC (Electronic Data Capture) for patient-level data collection CTMS (Clinical Trial Management System) for enrollment, site performance, milestones, budgets RTSM/IRT for randomization and drug supply management eTMF for trial master file / regulatory document repository eConsen...

A short dream about the future of clinical trials Sometimes it feels like clinical trials are very modern and very digital. We have electronic systems, dashboards, remote monitoring, and cloud platforms. But if we look closely at how clinical data actually moves, the story is more interesting. It is not really a story about paper becoming electronic. It is a story about manual transcription slowly disappearing . You could describe the evolution of clinical trials data capture in four stages: PDC → MDC → EDC → IDF And we are probably somewhere between stage 2 and stage 3. Stage 1. PDC (Paper Data Capture) In the beginning, everything was paper. The investigator wrote data in the medical record. Then the site copied the data into a paper CRF. Then someone at the sponsor or CRO entered the paper CRF into a database. So the data was written three times : Source document Paper CRF Database Paper was not the problem. Transcription was the problem. Stage 2....

Is cross-system clinical trial automation failing not because workflows cannot be executed, but because protocol terminology cannot be interpreted consistently across systems and clinical studies? Discussions about automating clinical trials often focus on advanced technologies such as workflow engines, interoperability standards, and AI/ML. In practice, cross-system automation most often breaks much earlier, at a far more basic level: inconsistent naming conventions .  Clinical protocols are written in natural language, where the same term can legitimately carry different meanings depending on study design, therapeutic area, or regulatory intent. Software systems, by contrast, assume that identifiers are stable, explicit, and unambiguous. This mismatch creates friction long before questions of execution logic or governance arise. Several academic and industry initiatives have proposed encoding protocol elements, such as eligibility criteria, visits, milestones, consen...