Platform for Efficient Switching between Multiple Devices in the Intensive Care Unit

F. De Backere; T. Vanhove; E. Dejonghe; M. Feys; T. Herinckx; J. Vankelecom; J. Decruyenaere; F. De Turck

doi:10.3414/ME13-02-0021

Methods of Information in Medicine, Table of Contents

Methods Inf Med 2015; 54(01): 05-15
DOI: 10.3414/ME13-02-0021

Focus Theme – Original Articles

Schattauer GmbH

Platform for Efficient Switching between Multiple Devices in the Intensive Care Unit

F. De Backere

¹Information Technology Department (INTEC), Ghent University – iMinds, Gent, Belgium

,

T. Vanhove

¹Information Technology Department (INTEC), Ghent University – iMinds, Gent, Belgium

,

E. Dejonghe

¹Information Technology Department (INTEC), Ghent University – iMinds, Gent, Belgium

,

M. Feys

¹Information Technology Department (INTEC), Ghent University – iMinds, Gent, Belgium

,

T. Herinckx

¹Information Technology Department (INTEC), Ghent University – iMinds, Gent, Belgium

,

J. Vankelecom

²Department of Intensive Care, Ghent University Hospital, Gent, Belgium

,

J. Decruyenaere

²Department of Intensive Care, Ghent University Hospital, Gent, Belgium

,

F. De Turck

¹Information Technology Department (INTEC), Ghent University – iMinds, Gent, Belgium

› Author Affiliations

Abstract

Summary

Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Managing Interoperability and Complexity in Health Systems”.

Objectives: Handheld computers, such as tablets and smartphones, are becoming more and more accessible in the clinical care setting and in Intensive Care Units (ICUs). By making the most useful and appropriate data available on multiple devices and facilitate the switching between those devices, staff members can efficiently integrate them in their workflow, allowing for faster and more accurate decisions. This paper addresses the design of a platform for the efficient switching between multiple devices in the ICU. The key functionalities of the platform are the integration of the platform into the workflow of the medical staff and providing tailored and dynamic information at the point of care.

Methods: The platform is designed based on a 3-tier architecture with a focus on extensibility, scalability and an optimal user experience. After identification to a device using Near Field Communication (NFC), the appropriate medical information will be shown on the selected device. The visualization of the data is adapted to the type of the device. A web-centric approach was used to enable extensibility and portability.

Results: A prototype of the platform was thoroughly evaluated. The scalability, performance and user experience were evaluated. Performance tests show that the response time of the system scales linearly with the amount of data. Measurements with up to 20 devices have shown no performance loss due to the concurrent use of multiple devices.

Conclusions: The platform provides a scalable and responsive solution to enable the efficient switching between multiple devices. Due to the web-centric approach new devices can easily be integrated. The performance and scalability of the platform have been evaluated and it was shown that the response time and scalability of the platform was within an acceptable range.

Keywords

Computer-assisted decision making - decision support systems - clinical/organization and administration - intensive care units - information systems - User-computer interface

Full Text

References

References
1 Lapinsky S. Mobile computing in critical care. Journal of Critical Care 2007; 22 (01) 41-44.
2 Berger E. The iPad: Gadget or medical godsend?. Annals of Emergency Medicine 2010; 56 (01) A21-A22.
3 Kubben P. Neurosurgical apps for iPhone, iPod touch, iPad and Android. Surgical Neurology International 2010; 1 (01) 89 +.
4 Lapinsky P, Weshler J, Mehta S, Varkul M, Hallett D, Stewart T. Handheld computers in Critical Care. Journal of Critical Care 2001; 5: 227+.
5 Colpaert K, Vanbelleghem S, Danneels C, Benoit D, Steurbaut K, Van Hoecke S. et al. Has information technology finally been adopted in Flemish Intensive Care Units?. BMC Medical Informatics and Decision Making 2010; 10: 62.
6 Osheroff J, Teich J, Middleton B, Steen E, Wright A, Detmer D. A roadmap for National Action on Clinical Decision Support. JAMIA 2007; 14 (02) 141-145.
7 Garg A, Adhikari N, McDonald H, Rosas-Arellano P, Devereaux P, Beyene J. et al. Effects of Computerized Clinical Decision Support Systems on Practitioner Performance and Patient Outcomes. JAMA 2005; 293 (10) 1223-1238.
8 Moxey A, Robertson J, Newby D, Hains I, Williamson M, Pearson S. Computerized Clinical Decision Support for Prescribing: Provision does not guarantee Uptake. JAMIA 2010; 17 (01) 25-33.
9 Orwat C, Graefe A, Faulwasser T. Towards Pervasive Computing in Health Care - A Literature Review. Social Science Research Network Working Paper Series 2008; 8: 26.
10 Kawamoto K, Lobach D. Clinical Decision Support provided within Physician Order Entry Systems: A Systematic Review of Features Effective for changing Clinician Behavior. In. AMIA Annl Sym Proc. 2003: 361-365.
11 Decruyenaere J, DeTurck F, Vanhastel S, Vandermeulen F, Demeester P, De Moor G. On the design of a generic and scalable multilayer software architecture for data flow management in the Intensive Care Unit. Methods of information in medicine 2003; 42 (01) 79-88.
12 Kawamoto K, Houlihan C, Balas A, Lobach D. Improving Clinical Practice using Clinical Decision Support Systems: A Systematic Review of Trials to identify Features Critical to Success. BMJ (Clinical research ed) 2005; 330 07494 765 +.
13 Van Hoecke S, Decruyenaere J, Danneels C, Taveirne K, Colpaert K, Hoste E. et al. Service-oriented subscription management of medical decision data in the Intensive Care Unit. Methods Inf Med 2008; 47 (04) 364-380.
14 Herasevich V, Pickering B, Dong Y, Peters S, Gajic O. Informatics Infrastructure for Syndrome Surveillance, Decision Support, Reporting, and Modeling of Critical Illness. Mayo Clinic Proc 2010; 85 (03) 247-254.
15 Sittig D, Wright A, Osheroff J, Middleton B, Teich J, Ash J. et al. Grand Challenges in Clinical Decision Support. J Biomed Inform 2008; 41 (02) 387-392.
16 Bardram J. Pervasive Healthcare as a Scientific Discipline. Methods Inf Med 2008; 47 (03) 178-185.
17 Triantafyllidis A, Koutkias K, Chouvarda I, Maglaveras N. An Open and Reconfigurable Wireless Sensor Network for Pervasive Health Monitoring. Methods Inf Med 2008; 47 (03) 229-234.
18 Blobel B. Architectural Approach to eHealth for Enabling Paradigm Changes in Health. Methods Inf Med 2010; 49 (02) 123-134.
19 Mitchell J, Gerdin U, Lindberg D, Lovis C, Martin-Sanchez F, Miller R. et al. 50 years of informatics research on decision support: What’s next. Methods Inf Med 2011; 50 (06) 525.
20 Peek N, Swift S. Intelligent data analysis for knowledge discovery, patient monitoring and quality assessment. Methods Inf Med 2012; 51 (04) 318.
21 Surján G. et al. How to use health informatics to manage the information overflow created by itself?. Methods Inf Med 2013; 52: 97-98.
22 Tentori M, Favela J. Activity-aware computing for healthcare. IEEE Pervasive Computing 2008; 7 (02) 51-57.
23 Tentori M, Hayes GR, Reddy M. Pervasive computing for hospital, chronic, and preventive care. Foundations and Trends in Human-Computer Interaction 2012; 5 (01) 1-95.
24 Bardram JE, Bossen C. Mobility work: The spatial dimension of collaboration at a hospital. Computer Supported Cooperative Work (CSCW) 2005; 14 (02) 131-160.
25 Bardram E. The trouble with login: on usability and computer security in ubiquitous computing. Personal and Ubiquitous Computing 2005; 9 (06) 357-367.
26 Koch SH, Weir C, Westenskow D, Gondan M, Agutter J, Haar M. et al. Evaluation of the effect of information integration in displays for icu nurses on situation awareness and task completion time: A prospective randomized controlled study. Int J Med Inform 2013; 82 (08) 665-675.
27 Effken JA, Loeb RG, Kang Y, Lin ZC. Clinical information displays to improve ICU outcomes. Int J Med Inform 2008; 77 (11) 765-777.
28 Falke O, Rukzio E, Dietz U, Holleis P, Schmidt A. Mobile Services for Near Field Communication. Tech. Rep. LMU-MI-20071.; Vodafone Group Research and Development, Munich, Embedded Interaction Research Group, University of Munic, Computing Department, Lancaster University, UK, Fraunhofer IAIS, Sankt Augustin and b-it, University of Bonn. 2007
29 Ok K, Coskun V, Aydin MN, Ozdenizci B. Current benefits and future directions of NFC services. In: Proc of the International Conference on Education and Management Technology (ICEMT), 2010. IEEE. 2010: 334-338.
30 NFC-Forum. Available. http://www.nfc-forum.org.
31 Csapodi M, Nagy A. New applications for nfc devices. In: Mobile and Wireless Communications Summit, 2007. 16th IST. IEEE. 2007: 1-5.
32 Gamma E, Helm R, Johnson R, Vlissides J. Design Patterns: Elements of Reusable Object-Oriented Software. Addison-Wesley. 1994
33 Paulson L. Building Rich Web Applications with AJAX. Computer 2005; 38 (10) 14-17.
34 Wang G. Improving Data Transmission in Web Applications via the Translation between XML and JSON. In: 2011 Third International Conference on Communications and Mobile Computing. IEEE. 2011: 182-185.
35 Roland M. Software card emulation in NFC-enabled mobile phones: great advantage or security nightmare. Fourth International Workshop on Security and Privacy in Spontaneous Interaction and Mobile Phone Use. 2012
36 Madlmayr G, Langer J, Kantner C, Scharinger J. NFC devices: Security and privacy. In: Proc of the Third International Conference on Availability, Reliability and Security, 2008. ARES 08. IEEE. 2008: 642-647.
37 Ok K, Coskun V, Aydin MN, Ozdenizci B. Current benefits and future directions of NFC services. In: Proc of the International Conference on Education and Management Technology (ICEMT), 2010. IEEE. 2010: 334-338.
38 Want R. Near Field Communication. IEEE Pervasive Computing 2011; 10 (03) 4-7.
39 Farion K, Michalowski W, Wilk S, OSullivan D, Rubin S, Weiss D. Clinical decision support system for point of care use. Methods Inf Med 2009; 48: 381-390.