Periodic Reporting for period 1 - INSULYNC (INSULYNC: Changing the way diabetes is treated)
Reporting period: 2018-02-01 to 2018-05-31
Medilync is developing software that employs Machine Learning to maximise the value of the data generated by diabetic patients and convert it into impactful information that helps improving glycemic control. The storage and processing of patients’ data takes place in our cloud backend – Cloudlync. The knowledge thus created is used to empower the patient’s care circle, which is patient-centred and includes caregivers, doctors and other healthcare professionals. Insulync (mobile and web apps) is the interface with users. Besides the basic logging features of commercially available diabetes management systems, we offer a convenient and easy way to upload information from any diabetes or wearable device, possibility for integration with electronic health records, and high quality content adjusted to the needs of the different users. Our objective is to deploy a comprehensive solution for the management of diabetes that increases patients’ engagement and adherence to treatment. We use streaming analytics to deliver real-time insights to the users’ dashboards, supporting patient’s short-term decision making (e.g. the need to compensate for an increased intensity of exercise or carbohydrate consumption). As support for the medium to long term decision making, for both patients and doctors, we developed machine learning models that will be trained and validated with real data from patients. Current approaches deal with limited amounts of information and are not able to account for interpatient variability. Our software will perform an automated case by case analysis of relevant parameters (glucose levels vs. medication, exercise, carbohydrates intake, etc.) to retrieve personalised information on how they interact, affecting glycemic control. It will also be able to predict future blood glucose levels and risk of hypo/hyperglycaemic events in a time frame that allows taking preventive action.
The market analysis conducted made us rethink our business model and better align our technical and commercial objectives. Thus, pilot tests will be performed with patients and doctors from our 18 target countries, who will help build commercial relationships with potential customers. We will perform a gradual roll-out of the software and adopt a B2B SaaS sales strategy with tiered pricing depending on the number of users. Our initial customer base will be mostly constituted by clinics and insurers, with the exception of Iceland and UK where, from the beginning, we will target the public healthcare systems. With the analysis of competitors, we concluded that most of them are either selling directly to patients or entering into partnerships with device manufacturers to gain access to their broader customer base. We also performed a freedom to operate analysis to ensure that neither our software nor any of its components will infringe any third party intellectual rights.
Finally, we have drafted a financial forecast that confirmed the potential for a rapid recovery of the invested money during the first commercialisation years, with revenues of € 21.6 million and a return on investment (ROI) of 4.59.