The platform developed by GMV records the data of patients with neck- or low-back-pain, harmonizes it and offers clinicians conclusions for designing personalized treatment and monitoring the trend of the illness.

Several European Studies have flagged up neck- or low-back pain as the world’s prime cause of incapacity. Seventy percent of adults report neck- or low-back pain at some moment of their lives, making this illness the commonest cause of daily incapacity. Healthcare professionals, on the other side of the equation, need better monitoring information to be able to predict with any certainty a treatment result.

This situation has prompted the European Union to include and drive within its recent programs the Back-UP project (Personalised Prognostic Models to Improve Well-being and Return to Work After Neck and Low Back Pain) with the aim of developing a more efficient monitoring procedure of neck- and low-back-pain patients. Back-UP, coordinated in Spain by the Valencia Biomechanics Institute (Instituto de Biomecánica de Valencia), involves a total of 11 organizations, featuring GMV as industrial leader and technology partner.

GMV has just deployed its telemedicine platform Antari Home Care, which integrates predictive models that allow clinicians to assess patients’ risk of back-pain onset in the next 2 to 6 months, forecasting their degree of functional incapacity and the probability of sick leave in this six-month period. GMV’s Antari Home Care works with predictive models developed by project partners and based on a digital representation of multidimensional clinical information, including personal data, physical and mental health data plus behavioral and socioeconomic factors that might impinge on neck- or low-back pain. It also includes the patient’s physiological parameters plus risk factors in the workplace and lifestyle in general to help extract clinical evidence.

It draws on machine-learning-based artificial intelligence to create prognosis models and in-silico assessments of possible interventions (simulations, modeling, experiments or analyses carried out with and simulation algorithms). The overall aim is to glean data-based evidence from clinical information of a varied nature arising from different sources.

Back-UP is expected to maximize treatment benefits while reducing overtreatment and associated damage of low-risk patients. Back-UP will also reduce neck- or low-back pain healthcare costs and, equally notably, increase worker productivity with the consequent knock-on benefits of efficiency and competitiveness.

* Back-UP has been funded by the European Union’s Horizon 2020 research and innovation funding program under grant agreement 777090