Step Towards Pervasive Technology Assessment in Intensive Medicine

This paper presents the evaluation of a Pervasive Intelligent Decision Support System in Intensive Medicine making use of Technology Acceptance Model 3 TAM3. Two rounds of questionnaires were distributed and compared. The work is based on a discursive evaluation of a method employed to assess a new and innovative technology INTCare using the four constructs of TAM3 and statistical metrics. The paper crosses the TAM3 constructs with INTCare features to produce a questionnaire to provide a better comprehension of the users' intentions. The final results are essential to validate the system and understand the user sensitivity. The paper validates a method to access technologies in critical environments and shows an example of how a questionnaire can be developed based on TAM3. It also proves the viability of using this method and advises that two rounds of questionnaires should be performed if we want to have better evidence on user satisfaction.

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

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

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

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

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

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

[7]  Thomas G. Dietterich Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.

[8]  Cynthia LeRouge,et al.  A nursing clinical decision support system and potential predictors of head-of-bed position for patients receiving mechanical ventilation. , 2010, American journal of critical care : an official publication, American Association of Critical-Care Nurses.

[9]  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 .

[10]  Filipe Portela,et al.  Real-Time Decision Support in Intensive Medicine: An intelligent approach for monitoring Data Quality , 2013 .

[11]  Upkar Varshney Pervasive healthcare computing , 2009 .

[12]  Filipe Portela,et al.  Data Mining Predictive Models for Pervasive Intelligent Decision Support in Intensive Care Medicine , 2012, KMIS.

[13]  Ø. Hammer PAST - PAlaeontological STatistics , 2001 .

[14]  Filipe Portela,et al.  Intelligent Data Acquisition and Scoring System for Intensive Medicine , 2012, ITBAM.

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

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

[17]  I. Hațieganu,et al.  PEARSON VERSUS SPEARMAN, KENDALL'S TAU CORRELATION ANALYSIS ON STRUCTURE-ACTIVITY RELATIONSHIPS OF BIOLOGIC ACTIVE COMPOUNDS , 2005 .

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

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