OP315 An Artificial Intelligence Approach To Improve Medical Diagnosis Of Ischemic Cardiopathy In Patients With Non-Traumatic Chest Pain

Abstract

Introduction

Current clinical practice is based on guidelines and local protocols that are informed by clinical evidence. This means that clinical variability is reduced, but can lead to inefficient clinical decision-making and can increase medical errors, decreasing patient's safety. The aim of the EXCON project is to investigate the innovative concept of Intelligent Clinical History (ICH), and to develop functional prototypes of high added-value in healthcare services.

Methods

The innovative EXCON project will take advantage of recent advances in technologies for coding, structuring and semantizing medical information. Thanks to this new structuring, the EXCON platform will be developed. The final users will be health professionals and other decision-makers. Doctors, nurses, epidemiologists and information specialists will be involved in the development and subsequent validation of the platform.

Results

The EXCON platform identifies profiles of patients with a high probability of ischemic heart disease. In the sample analyzed (n = 4,700), 17 percent of patients were admitted to a cardiology unit with suspected coronary heart disease. Of the patients admitted, 53.7 percent did not have ischemic heart disease at discharge. If we apply the algorithm developed by the EXCON project, 24.8 percent of patients would not have been admitted and did not have ischemic heart disease.

Conclusions

In coming decades, patient management will be impacted by the application of new advanced data analytics tools. This will allow for safer and more efficient clinical management, decrease variability in clinical practice, and improve equity. That is why the development and assessment of these technologies is necessary.