Segmentation of Chronic Patients

Chronic diseases have become increasingly significant in our country, partly due to a longer life expectancy and to the fact that we maintain a healthy lifestyle, common to developed countries. Indirect costs, associated with disability or dependence, are currently concentrated in the working population.

Besides, chronic patients who also suffer from co-morbidity (disorders co-occurring with a primary disease) or a more serious state need to attend Specialised Services or to be admitted at hospitals more frequently.

Likewise, many chronic patients who only need routine follow-ups in Primary Care centres turn constantly to Specialised Services, which makes it more expensive and not necessarily better for the patient’s condition.

Therefore, developing a national strategy to improve healthcare services for chronic patients has become a priority for the Health Care System. Two cornerstones of this strategy would be stratifying and dividing population into groups according to risk levels, and applying predictive models to optimize the management of high-risk patients and anticipate a hospital readmission, reducing costs related to it.

What is the Segmentation of Chronic Patients?

IIC wide experience applying own techniques and algorithms allows analysing data stored in several heterogeneous systems (Primary Care, Specialised Services, Pharmaceuticals, etc.) and detecting a series of relevant variables in order to segment chronic patients.

Stratifying population is not a fixed process. Patients characteristics evolve over time and their risk level must be adjusted regularly. Once population has been grouped into risk levels, there is a possibility of performing a detailed analysis of a set of conditions that represent a priority to the system, because they are severe, or due to their economic impact.

At IIC we have developed predictive models to address particular issues and predict behaviour patterns. We could therefore detect common characteristics and reasons for readmissions at hospitals, for example, or determine the probability of readmissions for a particular period.

Segmenting chronic patients permits predicting their evolution and treatment in terms of quality, use of resources and costs.

Why invest in Segmenting Population Services?

Analysing the information provided by data from medical histories is fundamental to anticipate the needs from patients and medical centres and optimize healthcare resources. Segmenting population according to chronic illnesses and setting different levels among chronic patients contribute to implement prevention programmes for population groups showing a higher risk.

These prevention programmes are aimed at improving quality of life, cutting healthcare costs by, mainly, reductions of emergency readmissions of those patients with insufficiently controlled chronic conditions, which might have been avoided calling these patients with a high readmission probability to periodical examinations.

We develop predictive models to address particular issues and predict behaviour patterns.