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Predictive Models

The advances in technology have allowed a boost in predictive medicine using the treating and analysis of big data, which presents a significant relevance in the daily life of a hospital, contributing to a large extent to an enhanced capacity of patients, health professionals and other stakeholders in the sector.

​Predictive Medicine based on evidence will allow the various stakeholders to predict the future and anticipate health problems, allowing patients to participate actively and knowledgeably in the process of managing their illness and in encouraging change in habits and behaviours. 

Predicting unscheduled hospital readmissions is a good example of the contribution of predictive models. In this field, the construction of predictive models about the risk of unscheduled hospital readmission allows identifying patients that are considered high risk upon discharge, and identify the associated factors, and, based on that information: to build the profile of the risk of readmission while simultaneously developing guidelines for the creation of programs for the prevention of hospital readmissions taking place.

In this sense, there are several recognized benefits linked to the development of predictive models and the evaluation of outcomes in health, including the ability to identify which protocols and guidelines to follow and what changes to make in order to continuously improve the provision of health care services.  

Glintt is today developing exploration projects in the field of clinical outcomes and predictive models in partnership with the world's largest stakeholders in the industry.