PROPOSAL OF A DIGITAL MODEL FOR MEDICATION RECONCILIATION USING BLOCKCHAIN AND ARTIFICIAL INTELLIGENCE

• Main Author: Albuquerque, Ricardo
• Publication Date: 2023
• Other Authors: Seixas Sardinha, Luís Filipe, Paulo Albuquerque , Susana, Salazar Silva, Maria Romana, Leite, Eduardo Manuel, Fragoeiro, Isabel
• Format: Article
• Language: por
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: https://doi.org/10.29073/e3.v9i1.735
• topic: Reconciliação Terapêutica; Inteligência Artificial; Interoperabilidade; Blockchain; Inovação; Therapeutic Reconciliation; Artificial Intelligence; Interoperability; Blockchain; Innovation

The Electronic Health Record (EHR) is the result of a transformation in the current healthcare system, influenced by innovation, integration, and sharing of clinical data. The EHR should include a clinical summary of patients, electronic prescriptions, electronic medication dispensing records, laboratory results, medical images and/or reports, and hospital discharge notes. Prescription errors and therapeutic administration are among the most common avoidable errors in healthcare, ranking as the sixth leading cause of death in the United States and a priority for the World Health Organization since the launch of the “Medication Without Harm” initiative. The objectives of this study are: i) to present a digital therapeutic reconciliation model; ii) to propose an interoperable and legally compliant solution; iii) to integrate Blockchain and Artificial Intelligence technologies into the presented model. Through a descriptive and exploratory literature review, the use of HL7 FHIR and SNOMED-CT standards, a client-CA model for Blockchain, and machine learning and natural language processing models for Artificial Intelligence were proposed. Ensuring the interoperable and secure transmission of clinical data is complex but theoretically feasible. The advantages of therapeutic reconciliation will be measurable through the continuous reduction of morbidity and mortality associated with therapeutic errors.