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CLIENT CASE STUDY


CASE STUDY:
PREDICTIVE HEALTH
ANALYTICS

OVERVIEW

By developing effective predictive analytics capability, Accenture helped Valencia La Fe improve prediction of unplanned preventable chronic episodes by 70 percent compared to traditional models. According to estimates by the department, this should enable savings of up to 50 million euros per year.

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OPPORTUNITY

Some 60 percent of healthcare costs are consumed by just 5 percent of patients, in whom a large number of chronic diseases are concentrated for developed countries. Life expectancy is increasing, along with the prevalence of chronic diseases, which means health systems need to help improve and manage the quality of life of multi-chronic patients while using limited budgets sparingly.

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SOLUTION

Accenture’s predictive analytics team worked with La Fe doctors to create a predictive analytics solution that identified chronic patients with a heightened risk of avoidable hospitalisations twelve months in advance. The selection of appropriate patients, combined with optimised support and monitoring, significantly increased the chances of producing better health outcomes.

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RESULTS

The predictive model delivered:

  • Improved prediction accuracy–70 percent

  • Possible savings of up to 50 million Euros per year

  • Improvement of quality of life related to health of up to 22 percent (measured by the EQ5D)

  • Initial health consumption analysis suggests a drastic reduction of up to 79 percent of hospitalisations and emergency room visits.

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VIDEO INSIGHTS

Valencia La Fe: Predictive health analytics models and case management

In developed countries, approximately 60 percent of healthcare costs are consumed by 5 percent of patients in whom a large number of chronic diseases are concentrated.

Watch how Valencia La Fe is using health analytics to improve care quality and healthcare efficiency.

La Fe de Valencia: Effective chronic disease management through health analytics

La Fe de Valencia faced the challenge of responding to rising incidence of chronic diseases, responding proactively to patients and improving their quality of life. At the same time, they wanted to release pressure on hospital and primary care capacity. They achieved this by leveraging predictive analytics and efficiently implementing interventions that which reduce the length of hospital stays. The results were confirmed through a clinical trial with some 500 patients.