HC PSI - Intelligent Services Platform

HC PSI – Intelligent Services Platform


Research and develop a tele-health platform equipped with the latest advances in Decision Support Systems that will allow those involved in the process to make a more informed decision regarding the patient’s referral, increasing the entities’ efficiency.


This platform is intended for health entities to have a technological system capable of:

  • Detecting in advance and with high precision the exacerbations of Chronic Obstructive Pulmonary Disease;
  • Communicating to the patient the degree of risk present;
  • Advise, if necessary, the most suitable hospital unit in the imminence of an urgent admission.

Platform overview

The main objective of this project was to develop a clinical decision support system, enabling healthcare professionals to make more informed decisions regarding the management of patients with Chronic Obstructive Pulmonary Disease (COPD) and significantly improve organisational efficiency. In a pioneering effort to improve the healthcare landscape, the INOV team has successfully developed a telehealth platform designed to revolutionise COPD management. The system stands out for its ability to detect COPD exacerbations at an earlier stage and with greater precision.

Main components

  • Vital signs prediction: This component predicts prognostic vital signs data, providing a proactive tool for healthcare professionals to anticipate changes in a patient’s state of health. By taking advantage of advanced predictive analysis, the INOV platform allows medical teams to intervene quickly and make timely adjustments to treatment plans. This module uses various machine learning algorithms to make the predictions.
  • Early Warning score calculation: Centred on assessing the risk of deterioration, this module calculates early warning scores, allowing healthcare providers to accurately assess the urgency of intervention. The system issues alerts whenever measurements of biometric signs deviate from the permitted range or when significant changes in baseline values occur, ensuring timely responses to potential emergencies.
  • Measurement Error Detection: The aim of the module is to identify measurement errors and abnormal variations detected in the patient’s historical data, whether due to sensor or equipment failures or human error. This module checks whether the measurements fall within a pattern that can be observed by the patient, taking into account their history of vital signs measurements. The component promptly alerts both the patient himself and the nurse in charge of treatment to the invalidity or questionable nature of the information entered. By diligently identifying such anomalies, this module serves as a critical mechanism for ensuring the accuracy and reliability of the data in the system.
  • Basal Value Adjustment: The main function is to continuously and intelligently monitor and adjust the patient’s baseline values. This adjustment is based on the historical records of vital sign values measured by the patient and documented on the HCAlert platform. The aim of the module is to increase the accuracy and effectiveness of the monitoring system by dynamically adapting the reference values according to the patient’s specific health history.

Patient-centred benefits

The platform’s ability to detect exacerbations early allows healthcare providers to promptly communicate the degree of risk to patients. By issuing timely warnings and recommendations, the system enables individuals to take proactive measures and, when necessary, allows identification of the need for urgent hospitalisation in order to avoid potential crises.


Project name: HC-PSI – Plataforma de Serviços Inteligentes
Code: 70275

Main objective: Strengthening research, technological development and innovation

Region of Intervention: Centro

Promoter: Hope Care, SA.

Co-promoters: INOV – Instituto de Engenharia de Sistemas e Computadores Inovação; Universidade da Beira Interior

Approval date: 07-01-2021
Start date: 04-11-2020
End date: 30-06-2023

Total eligible cost: 861.083,12 €

EU financial support: 608.931,40 € (FEDER)


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