Doctor and patient talking and happy

From theory to saving lives




User research

User testing


Custom software


Baysient had developed through an international team of academics, clinicians and specialists a method of personalised drug dosing that was proving highly effective. However, how can this complex algorithm be used effectively by clinicians globally?


Rather than following the generic prescriptions, Clinicians are using leading edge personalised medicine through the simple to use web app, and making huge strides in improving patient outcomes.


The standard dose of drugs is based on the best results from historical trials. It is an amount that works for the majority of the population. However, no two patients are the same and so can have very different responses to the drug. Clinicians may use their experience to adjust doses as they observe the patient but this is not an exact science and can take too long to refine.

The outcome is that most patients are not responding as well as they could to their treatment; resulting in ill health and a shorten life. Baysient found a new model that could be used to dose the 'MAb' drugs that would allow personalised dosing to be created quickly using standard tests and their algorithm.

Getting doses of drugs right for a specific patient is complex and difficult to perfect

However, the algorithm was inaccessible to anyone other than those that had been given specific training. This was not suitable for rolling out to the global audience. This held back the potentially life saving treatment from patients.

What we did

The heart of this challenge was the need to understand the range of users this platform would need to interact with. The complex information the algorithm created needed to have inputs from nurses and administrators, while Doctors and patients needed to review and interact with the outputs.

Often in clinical settings there is complex range of users with a variety of needs, knowledge and expectations.

The different users had different expectations:

  1. Nurses - had to take tests and input results into the system amongst a variety of other systems, tools and jobs.
  2. Administrators - had to manage the system, ensure data remained secure and were able to manage their clinical staff and patients.
  3. Clinicians -  The Doctors had to interpret and understand what they were being told by the algorithm so that they could make an informed recommendation that was ultimately theirs to make.
  4. Patients - battling a condition that could have a significant (and even life threatening) impact on their lives; they needed to understand the complex data so that their condition could be effectively managed.

iDose by Basysient

To build iDose we undertook a series of prototypes and test activities to quickly learn what would work for the users. We used rapid prototyping tools to quickly build and test our ideas with real users and experts. This gave us quick learning loops that fed back into the development of the product.

Rapid prototyping and test cycles gave us so much useful information that were invaluable to the development of an effective solution.

To make the development process more complicated we had to negotiate a complex regulatory environment. We built the system so that it would adhere to FDA regulation and international data protection rules that had to be carefully controlled due to the sensitivity of the information we were processing.

A data entry tool allowed nurses to quickly and efficiently input test results into the system and minimise the scary risk of incorrect data entry.

An administration area allowed the clinicians, nurses and patients to be managed so that only those who needed access to particular information could access it, increasing security of patient records.

The Clinicians and patients had easy to interpret visualisations of their personalised prescription and access to controls that would allow them to explore alternatives that may better fit their specific lifestyle.

It's about doing what is best for the individual patient while remembering that the clinician and their team are busy and do not have time to battle a complex system.

Working alongside the academic team, clinicians and the specialists in a multi-national (we were all away at the same time for about 1 hours a day!) project was highly rewarding. We are very proud of the impact the solution is having on patients right now.

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