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An inefficient clinical trial will result in wasted resources, delays in new treatment for patients in need and negatively impact population health even though we have the scientific understanding to do much better. The mission of Lindus Health is to accelerate clinical trials with a vision to fix the system. Working towards such a cause with a team of brilliant people who are as excited about this mission as I am makes working at Lindus Health an amazing opportunity.
Lindus Health places huge emphasis on R&D. Innovative research is a must to break free from the traditions anchored in this field and one of the company goals is to generate innovative solutions to long standing problems. As a machine learning engineer, I can see the potential of using machine learning to give meaning to past data and design the patient-centric, efficient clinical trials of the future. The clinical trials field features many large datasets (including publicly available ones like clinicaltrials.gov), which have been relatively under-explored. There are challenges associated with dealing with a mix of structured and unstructured data, but I’ve been able to overcome these and build predictive models for an improved trial design process. There is a lot of room for innovation in this field and it is a great professional motivation for me to work towards this at Lindus Health.
In addition to working as a machine learning engineer, I have a design background where I believe some of my most valuable skills originated from. At Lindus Health everyone’s skills and contributions are valued and appreciated which is one of the reasons that I could immediately see myself as a fit to this company. I can contribute to ongoing product challenges from a designer’s perspective as well as being highly focused on a machine learning project. In my opinion, this is one of the perks of working in a growing start-up environment with open-minded people where you can contribute to different business aspects instead of focusing only on one thing that you can do.
Ece Kavalci, Machine Learning Engineer
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Discussing DTx challenges and go-to-market in the UK with Charlotte Lee-Sinclair (ex-Big Health)
A fireside chat, this time by an actual fire! Our conversation with Dr Charlotte Lee-Sinclair is a must-read on DTx challenges and the road to go-to-market in the UK. Read here.
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Debunking the 3 biggest myths about decentralised trials
You've probably heard of decentralised clinical trials, but there are a lot of myths out there. Here we debunk the 3 biggest myths.
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Product Clinic #6: Releasing, Fast and Slow
Depending on your background, our 6 to 8 week product release cycle will evoke one of two reactions. If you work in the clinical space: Woah, what an incredibly fast release cycle! But if you work in a typical SaaS startup: How quaint, I release 3 times a day! Heard of Daniel Kahnemann's bestseller Thinking, Fast and Slow? Well, there is our inspiration!
