Beat the Clock: Finish Clinical Trials Faster

·29 min·2 clips

Clinical trials are largely powered by Excel, even as phase two and phase three studies add EDC, IRT, central labs, and CROs.

1. cc: Life Science Podcast frames 'Beat the Clock: Finish Clinical Trials Faster' around clinical-trial operations and automated insights. 2. Chris hosts Jin Kim, the CEO and founder of Miracle, and Jin matters because he built the product after working in enterprise sales with Biogen, BMS, and GSK. 3. The episode asks how biotech teams can finish studies on time before runway and money run out. 4. Jin says Miracle integrates clinical trial systems into one automated real-time dashboard and surfaces insights to streamline operations. 5. He gives the example that some phase two and phase three customers have finished studies three to four months ahead of schedule. 6. He links that speed to lean biotech companies that cannot afford delays. 7. Jin says clinical trial operations is still 'largely powered by Excel.' 8. He explains that the problem grows when EDC, IRT, central labs, recruitment vendors, and CROs all produce separate data silos. 9. He uses the naming mismatch of 'date of randomization' and 'randomized date' to show why systems fail to recognize the same information. 10. He says teams must export CSVs from multiple systems and normalize them into a single view. 11. Jin describes Miracle as using existing APIs, data transports, and data exports to consolidate study data into one platform. 12. He says that process automates work that previously took hours and might only have been done once or twice a week. 13. He notes that more frequent updates can surface issues on Thursday or Friday instead of waiting until the following Wednesday. 14. At AWS Life Sciences in New York, he says the event was mostly mid-to-large pharma and big tech with AI as the dominant topic. 15. He argues that AI is only as good as the data behind it, especially when one database is clean and another requires more cleaning. 16. He says disconnected systems prevent AI from giving a comprehensive picture, so companies need the data infrastructure ready first. 17. The interview stays practical and conversational, with Chris pressing on spreadsheets, nomenclature, recruitment metrics, and AI skepticism. 18. Jin answers in a direct, implementation-focused style and repeatedly returns to workflows, roles, and metrics instead of abstract AI claims. 19. People working in biotech clinical operations, data management, or trial leadership would likely find this useful. 20. People looking for abstract AI commentary without operational detail may skip it.

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