Cievert was founded in 2011 by a former therapeutic radiographer working for the NHS. They are based in the United Kingdom and have developed several solutions for addressing referral management, improving the delivery of consultations and revolutionising patient follow-up. Cievert decided to explore their market potential in the Netherlands. They teamed up with MedScaler to validate their value proposition through an Market Validation.


Cievert developed a web-based platform, Penguin, to monitor patients remotely and determine whether they would benefit from a follow-up appointment. Penguin automatically assesses patients and asks them questions based on their diagnosis and course of treatment. The patient then verifies their date of birth and post code. If a patient answers a question that falls outside of the guidelines, this is flagged, and the clinician is notified in real-time. The platform uses logical algorithms to determine if a patient needs a follow-up appointment.

What is your founding story? How did you come up with this innovation?

Cievert was founded by a clinician in the NHS, Chris. He had seen his fair share of outdated processes and backward technology, so he decided to do something about it. He founded Cievert in 2011. After initial success at his local NHS Trust, he expanded his efforts to cover more and more areas of the UK. We now manage, for example, 25% of the UK’s radiotherapy patients.

How is your innovation revolutionizing healthcare?

We are completely transforming the existing model of outpatient follow-up. Instead of having your doctor say, “See you in 6 weeks’ time for your follow-up appointment”, our solution monitors patients remotely in order to determine when is best for them to be followed up. That way, we can reduce unnecessary appointments and detect any potential problems earlier.

Why are you interested in entering/exploring the Dutch market?

The outdated model of outpatient care is in dire need of transformation, particularly when healthcare systems are under ever-increasing pressure. We are therefore freeing doctors’ time from patients who are fine and helping them focus on the patients that would benefit most from support. This can have a massive impact on any clinical area. We are helping transform oncology, gastroenterology, renal care, endoscopy, rheumatology, dermatology…

What is the next milestone for your company?

We have become well-established in the NHS, having managed over 150,000 patients to date. This year, we have started to work with hospitals abroad, which is incredibly exciting. We look forward to continuing our international expansion, with the help of the experts over at MedScaler.

What is the most valuable tip about innovating healthcare that every entrepreneur could learn from?

Work as closely with clinicians as you can. They will be the end user, they will act as the patient’s voice, they will be your champion – there’s no point doing anything without making sure that it’s what the clinician wants. Make them happy and they will fight your corner at every chance they get.

What research, pilot or trials do you currently have ongoing to become an evidence based intervention?

We are currently carrying out evaluations of Penguin’s impact in:

  • Gastroenterology (through an NIHR-badged national clinical trial)
  • Renal care (in partnership with London South Bank University)
  • Rheumatology¬†(in partnership with Novartis)
  • Dermatology (in partnership with Novartis)
  • Endoscopy (in partnership with Newcastle University)

In your opinion, why are MedTech and Digital Health so important?

The world of medicine has some cutting-edge technology: robotic surgeons, AI-enabled diagnostics, personalized drugs… And yet, hospital processes can be so backward in so many ways. The UK’s NHS, for example, still relies heavily on fax machines – in 2020! This means that even the most basic technology, if developed well, can have a huge positive impact on patients’ lives.

Where do you see your innovation in 5 years?

We are doing some very exciting AI work in partnership with Durham University. Our solution captures plenty of rich, structured patient data, which we are anonymizing for the purpose of applying machine learning techniques. The aim is to personalize treatment in this way. In 5 years’ time, it would be great to see hospitals around the world using this approach to making decisions about a patient’s treatment.