PP164 Improving Medical Diagnosis Through Advanced Data Analytics Tools

• Published in: International journal of technology assessment in health care 2019, Vol.35 (S1), p.67-68
• Main Authors: González-Barcina, Imanol, de Vicuña-Meléndez, Aitor García, Santorcuato, Ana, Revuelta-Antizar, Ivan, Rodríguez-Tejedor, Santiago, López-Moreno, Borja, Arana-Arri, Eunate
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Clinical decision making; Clinical medicine; Data analysis; Decision making; Decision support systems; Electronic health records; Electronic medical records; Emergency medical services; Epidemiology; Medical diagnosis; Medical personnel; Natural language processing; Pain; Patients; Physicians; Prototypes; Resource management; Safety
• ISSN: 0266-4623; 1471-6348
• DOI: 10.1017/S0266462319002630

Copyright © Cambridge University Press 20192019Cambridge University PressIntroductionCurrent 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.MethodsThe 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. 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 platforms.ResultsTo develop the ICH platform clinical data on a highly prevalent symptom with high variability in clinical practice, such as non-traumatic chest pain in emergency services, has been collected from different electronic medical record databases. The extraction of clinical data to implement new techniques of artificial intelligence requires tasks that must be automated, which today is difficult and tedious (data is often not computerized). Through techniques applied in EXCON, such as natural language processing, relevant clinical data have been extracted and a Decision Support System has been developed and validated. This tool optimizes resources and improves clinical management, reducing errors and increasing patient's safety.ConclusionsIn 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.