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The Hospital General Universitario Gregorio Marañón (HGUGM) is a public hospital in Madrid, Spain. It is the largest hospital in the Community of Madrid and serves more than 2 million people. It has 8,000 employees and more than 1,200 beds. It is a teaching hospital, attached to the Complutense University of Madrid.
In the year 2021, the data on healthcare activity were: Total discharges 42.624, the average length of stay 7,61, total admissions 42.531, emergency admissions 26.691 total emergencies 239.076 and percentage of emergency admissions 11,34%. The incidence of delirium in patients over 65 years old ranges between 11,7% and 18,5%, depending on the type of hospitalization unit [1–3].
The Secretariat for Justice Administration of the Ministry of Justice of the Government of Catalonia includes among its functions the modernization of the justice administration in Catalonia through the renovation of judicial infrastructures, information systems, and the organization of the judicial office to achieve a more open, agile, efficient and quality justice.
Since 2018, the Hospital has been equipped with an Electronic Health Record (EHR) that encompasses relevant information regarding patients’ acute clinical conditions, baseline medical history, functional and mental state, as well as socio-demographic and clinical variables.
The available Electronic Health Record (EHR) electronically stores patients’ medical information and care records. It enables healthcare professionals to access, review, and update patient information quickly and efficiently. With the EHR, it is possible to conduct a comprehensive review of a patient’s medical data, including previous diagnoses, treatments, test results, prescribed medications, healthcare provider notes, and other relevant data for patient care. Additionally, the EHR supports the registration of comorbidity using the Charlson index and the assessment of pain with the appropriate scale based on the patient’s cognitive level. Delirium is assessed upon admission and whenever there is a significant change in the patient’s condition using the Confusion Assessment Method (CAM).
The Confusion Assessment Method (CAM) is a simple tool that can be used by physicians and nurses to integrate their observations and identify when delirium is the most likely diagnosis. In medical and surgical settings, CAM demonstrates a sensitivity of 94% to 100% and a specificity of 90% to 95%.
Efforts should be made to improve the identification of patients at risk during admission to establish preventive interventions and avoid complications such as falls, prolonged hospital stays, and unintentional removal of devices. CAM has become a standard screening tool in clinical studies of delirium conducted across multiple settings, including emergency departments and long-term care facilities. It takes five minutes to administer and can be particularly useful when incorporated into routine bedside assessments. However, current workloads and nursing shortages make it challenging to perform assessments consistently on a shift basis.
Since the Hospital does not currently have an early detection system, incorporating a delirium algorithm into a third-party analytical solution owned by the Hospital, with a systematic approach, would provide an optimal solution to this problem.
Evidence-Based Behavior (eB2) is a technology start-up that develops Artificial Intelligence based solutions for behavior monitoring and evaluation. eB2 has a multidisciplinary team covering research, technical, clinical, business, organizational and management profiles that have developed the eB2-MindCare system for mental health and well-being.
eB2 EarlyDel is an automated system that manages patients’ delirium risk throughout their hospital stay. It uses machine learning algorithms to analyze data from EHRs, assessments by nurses and physicians, and a post-discharge app to improve clinical workflow, quality of care, and patient safety. The system provides real-time updates on delirium risk and generates various risk measures. It also offers a dashboard for data visualization and guidance for managing delirium. Secure data exchange protocols ensure patient data privacy. eB2 EarlyDel is an innovative tool for early delirium detection and improved patient care.
eB2 EarlyDel responds to Earyldel’s main challenge by developing and validating algorithms for predicting the risk of delirium, with the support of a machine learning expert team. The team is now integrating these algorithms into a product under development by the eB2 team for future use and commercialization.
The partnership with the challenger results in an efficient and effective solution being implemented at the HGU GM, paving the way for the market entry of eB2 EarlyDel as a valuable addition to the existing product portfolio.
This project has received funding from the European Union’s Horizon Europe research and innovation programme under the Grant Agreement 101071212.
Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Innovation Council and SMEs Executive Agency (EISMEA). Neither the European Union nor the granting authority can be held responsible for them.
