Implementing a Pervasive Real-Time Intelligent System for Tracking Critical Events with Intensive Care Patients

Nowadays, it is increasingly important to utilize intelligent systems to support the decision making process DMP in challenging areas such as Intensive Medicine. In Intensive Care Units ICU, some of the biggest challenges relate both to the number and the different types of available data sources. Even though in such a setting the values for some variables are easy to collect, data collection is still performed manually in particular instances. In order to improve the DMP in ICU, a Pervasive Intelligent Decision Support System, called INTCare was deployed in the ICU of Centro Hospitalar do Porto in Portugal. This system altered the way information is collected and presented. Moreover, the tracking system deployed as a specific module of INTCare-Electronic Nursing Record ENR is made accessible anywhere and anytime. The system allows for the calculation of the critical events regarding five variables that are typically monitored in an ICU. Specifically, the INTCare tracking system characterizes a grid that shows the events by type and duration, empowers a warning system to alert the doctors and promotes intuitive graphics that allow care providers to follow the patient care journey. User acceptance was measured through a questionnaire designed in accordance with the Technology Acceptance Model TAM and results of implementing the INTCare tracking system, and its interface are reported.

[1]  Manuel Filipe Santos,et al.  Rating organ failure via adverse events using data mining in the intensive care unit , 2008, Artif. Intell. Medicine.

[2]  Upkar Varshney,et al.  Pervasive Healthcare and Wireless Health Monitoring , 2007, Mob. Networks Appl..

[3]  J. Machado,et al.  Intelligent and Real Time Data Acquisition and Evaluation to Determine Critical Events in Intensive Medicine , 2012 .

[4]  Nicola T. Shaw,et al.  The impact of a Critical Care Information System (CCIS) on time spent charting and in direct patient care by staff in the ICU: A review of the literature , 2009, Int. J. Medical Informatics.

[5]  Fred D. Davis,et al.  A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies , 2000, Management Science.

[6]  Filipe Portela,et al.  Enabling a Pervasive Approach for Intelligent Decision Support in Critical Health Care , 2011, CENTERIS.

[7]  Upkar Varshney Pervasive healthcare computing , 2009 .

[8]  Viswanath Venkatesh,et al.  Technology Acceptance Model 3 and a Research Agenda on Interventions , 2008, Decis. Sci..

[9]  I. Ajzen,et al.  Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research , 1977 .

[10]  Filipe Portela,et al.  Pervasive Intelligent Decision Support System - Technology Acceptance in Intensive Care Units , 2013, WorldCIST.

[11]  Manuel Filipe Santos,et al.  INTCare: a Knowledge Discovery Based Intelligent Decision Support System for Intensive Care Medicine , 2005, J. Decis. Syst..

[12]  Mohandeep Kaur,et al.  Critical events in intensive care unit , 2008, Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine.

[13]  Upkar Varshney,et al.  Pervasive Healthcare Computing: EMR/EHR, Wireless and Health Monitoring , 2009 .

[14]  Rajshekhar Sunderraman,et al.  Health Level-7 compliant clinical patient records system , 2004, SAC '04.

[15]  Filipe Portela,et al.  Knowledge Discovery for Pervasive and Real-time Intelligent Decision Support in Intensive Care Medicine , 2011, KMIS.

[16]  Mahadev Satyanarayanan,et al.  Pervasive computing: vision and challenges , 2001, IEEE Wirel. Commun..

[17]  Filipe Portela,et al.  Enabling real-time intelligent decision support in intensive care , 2011 .

[18]  Manuel Filipe Santos,et al.  INTCare - Multi-agent Approach for Real-time Intelligent Decision Support in Intensive Medicine , 2010, ICAART.

[19]  M. Mikkonen,et al.  User and Concept Studies as Tools in Developing Mobile Communication Services for the Elderly , 2002, Personal and Ubiquitous Computing.

[20]  Manuel Filipe Santos,et al.  A Pervasive Approach to a Real-Time Intelligent Decision Support System in Intensive Medicine , 2010, IC3K.

[21]  M. Keegan,et al.  Severity of illness scoring systems in the intensive care unit , 2011, Critical care medicine.

[22]  Nathalie Bricon-Souf,et al.  Context awareness in health care: A review , 2007, Int. J. Medical Informatics.

[23]  Chun Che Fung,et al.  TECTAM: An Approach to Study Technology Acceptance Model (TAM) in Gaining Knowledge on the Adoption and Use of E-Commerce/E-Business Technology among Small and Medium Enterprises in Thailand , 2010 .

[24]  D. Bates,et al.  The Critical Care Safety Study: The incidence and nature of adverse events and serious medical errors in intensive care* , 2005, Critical care medicine.