This collaborative initiative is coordinated by the WeMind Cluster with the participation of the Cluster Innovative Solutions for Independent Living (SIVI) and the companies ITA Clinic, ISALUS and Evidence-Based Behavior.
Together, we have embarked on the innovative development of a solution for relapse prevention and intervention in behavioural disorders and their improvement in care, because we believed it was necessary to provide tools to help professionals alert them of the risk of relapse or to help them to detect it early if it has occurred.
It is necessary to take advantage of all the information available automatically to help the clinical team detect the behaviour and its changes that precede these relapses, making use of both the information derived from the digital phenotype and the therapeutic evolution itself collected in the electronic medical record and the baseline risk factors for relapse collected in questionnaires and scales.
Exploring the frontiers of research:
In the initial phase we were involved in developing the methods of patient profiling, detection of behavioural changes and explanation of these changes for these pathologies, as well as the implementation of these methods in the eB2 MindCare platform and their integration with the electronic health record system. Finally, we will address the development of the solution and the creation of risk assessment procedures.
Novelty and relevance at every step:
To make this possible, it is necessary to have all the information available together or, in other words, to make the systems that collect this data, eB2 MindCare on the one hand, and the medical records on the other, interoperable. With these processes, we hope to minimise the frequency of unnecessary relapses and make it possible to address them in sufficient depth and early enough.
In the current situation of increasing demand for mental health care, the availability of procedures and tools that make the work of clinical teams more effective and efficient is a priority objective. The tension in mental health care systems requires improving the results of interventions in terms of average length of stay in treatment and the effectiveness of interventions.
A glimpse into the soul of CONTIGO CARE:
At its core, CONTIGO CARE aims to develop a technological solution that dynamically merges clinical, follow-up and patient interaction and passive monitoring information for the prediction and early detection of relapse. To this end, an artificial intelligence engine will be built to detect changes in the joint evolution of these sources of information that are potentially related to relapses in substance use.
A promising future:
ContigoCARE will provide a validated and easy-to-use solution that will help mental health professionals to detect relapses early and, at the same time, will contribute to improving the follow-up of mental health patients.
We will offer the market a specific solution, validated for these pathologies and open and interoperable with other ERP and electronic medical record systems, and we will facilitate, on the one hand, to start the procedure to obtain its certification as a Class II healthcare device from the current Class I, increase its market share in private healthcare, access public healthcare and start its international expansion.
In addition, we will have our own validated method that will allow us to expand this methodology to different centres and different pathologies, with the consequent direct impact on patients and improvement of processes.
With the application of the method and the tools proposed by ContigoCARE, it is possible to significantly reduce the time and effectiveness of the treatment.
This project has been funded by the AEI (Innovative Business Associations) line of aid from the Ministerio de Industria, Comercio y Turismo of the Spain Government.
