Periodic Reporting for period 1 - INSULYNC (INSULYNC: Changing the way diabetes is treated)
Reporting period: 2018-02-01 to 2018-05-31
Diabetes is a growing concern globally, with the number of diabetics being forecast to rise from 422 million in 2016 to 642 million in 2040. As a consequence, the total diabetes-related health expenditure is also set to rise at a rate of 19.2%, reaching € 685 billion by 2040. In Europe, diabetes affects 58 million people and costs € 145 billion per year. There is no cure for diabetes but a proper management of the disease can prevent complications and improve diabetes patients’ quality of life.
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.
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 activities conducted during the feasibility study aimed at assessing the viability of our project in the following three dimensions: technical, commercial and financial. In terms of technical feasibility, we performed a thorough analysis of the functional requirements of our software and determined the necessary amount of work, time and person-months to complete its development and reach the commercialisation stage by the end of the Phase 2 project. We concluded that a total of 102 person-months will be required to complete the planned work in 24 months, which means that we will need to add three developers to our technical team with expertise in backend, frontend and machine learning development. We also performed an analysis of risks and possible mitigation measures.
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.
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.
Medilync aims to provide a comprehensive solution to improve diabetes management that benefits patients, providers and healthcare systems. Globally, the healthcare systems are failing to respond to the unprecedented growth of diabetes. At the same time, important advances are being made in other areas that will likely transform the current paradigm of care. Among them, big data, machine learning and cloud computing hold great promise to improve the quality of care while saving resources and reducing costs. These technologies are at the core of Cloudlync and Insulync, our suite of solutions that will help healthcare systems tackle the following challenges: (i) lack of interoperability among eHealth systems (devices, apps, EHRs, etc.); (ii) patient’s lack of engagement with self-care; and (iii) incapacity of providers (due to time and workload constraints) of working together with patients to deal with the high complexity of diabetes management. Being an integrated solution prescribed and used by doctors to assist patients, Insulync app will achieve higher acceptance rates among patients and contribute to reduce the complications derived from poor management of the disease.