Design and Development of a Mobile System for Supporting Emergency Triage

Summary

Objectives: Our objective was to design and develop a mobile clinical decision support system for emergency triage of different acute pain presentations. The system should interact with existing hospital information systems, run on mobile computing devices (handheld computers) and be suitable for operation in weak-connectivity conditions (with unstable connections between mobile clients and a server).

Methods: The MET (Mobile Emergency Triage) system was designed following an extended client-server architecture. The client component, responsible for triage decision support, is built as a knowledge-based system, with domain ontology separated from generic problem solving methods and used for the automatic creation of a user interface.

Results: The MET system is well suited for operation in the Emergency Department of a hospital. The system’s external interactions are managed by the server, while the MET clients, running on handheld computers are used by clinicians for collecting clinical data and supporting triage at the bedside. The functionality of the MET client is distributed into specialized modules, responsible for triaging specific types of acute pain presentations. The modules are stored on the server, and on request they can be transferred and executed on the mobile clients. The modular design provides for easy extension of the system’s functionality. A clinical trial of the MET system validated the appropriateness of the system’s design, and proved the usefulness and acceptance of the system in clinical practice.

Conclusions: The MET system captures the necessary hospital data, allows for entry of patient information, and provides triage support. By operating on handheld computers, it fits into the regular emergency department workflow without introducing any hindrances or disruptions. It supports triage anytime and anywhere, directly at the point of care, and also can be used as an electronic patient chart, facilitating structured data collection.

Keywords

Clinical decision support systems - triage - handheld computers - hospital emergency service

• References

• 1 Musen MA, Shahar Y, Shortliffe EH. Clinical decision support systems. In. Shortliffe EH, Perreault LE, Wiederhold G, Fagan LM. editors. Medical Informatics. Computer Applications in Health Care and Biomedicine. 2nd ed. Springer- Verlag: 2001: 573-609.
• 2 Shortliffe EH. The adolescence of AI in medicine: Will the field come of age in the 90’s?. Artif Intell Med 1993; 5: 93-106.
• 3 Séroussi B, Bouaud J, Antoine ÉC. Oncodoc: a successful experiment of computer-supported guideline development and implementation in the treatment of breast cancer. Artif Intell Med 2001; 22: 43-64.
• 4 Hanson 3rd CW, Marshall BE. Artificial intelligence applications in the intensive care unit. Crit Care Med 2001; 29 (02) 427-35.
• 5 Anderson JG. Clearing the way for physicians’ use of clinical information systems. Commun ACM 1997; 40 (08) 83-90.
• 6 de Dombal FT, Leaper DJ, Staniland JR, McCann AP, Horrocks JC. Computer-aided diagnosis of acute abdominal pain. Br Med J 1972; 2 5804 9-13.
• 7 Hunt DL, Haynes RB, Hanna SE, Smith K. Effects of computer-based clinical decision support systems on physician performance and patient outcomes: A systematic review. JAMA 1998; 280 (15) 1339-46.
• 8 Kinney WC. Web-based clinical decision support system for triage of vestibular patients. Otolaryngol Head Neck Surg 2003; 128 (01) 48-53.
• 9 Gertner AS, Webber BL. Traumatiq: online decision support for trauma management. IEEE Intelligent Systems and Their Applications 1998; 13 (01) 32-9.
• 10 Daou Systems Inc. Going mobile: from ehealth to mhealth. A Daou Systems white paper. 2001. Available from. http://www.daou.com/emerging/pdf/mHealth_White_Paper_April_2001.PDF Accessed March 25 2004
• 11 Tachakra S, Wang XH, Istepanian RS, Song YH. Mobile e-health: the unwired evolution of telemedicine. Telemed J E Health 2003; 9 (03) 247-57.
• 12 Ammenwerth E, Buchauer A, Bludau B, Haux R. Mobile information and communication tools in the hospital. Int J Med Inf 2000; 57 (01) 21-40.
• 13 Fischer S, Stewart TE, Mehta S, Wax R, Lapinsky SE. Handheld computing in medicine. J Am Med Inform Assoc 2003; 10 (02) 139-49.
• 14 Roth AC, Leon MA, Milner SM, Herting Jr. RL, Hahn AW. A personal digital assistant for determination of fluid needs for burn patients. Biomed Sci Instrum 1997; 34: 186-90.
• 15 Zupan B, Porenta A, Vidmar G, Aoki N, Bratko I, Beck JR. Decisions at hand: a decision support system on handhelds. Medinfo 2001; 10: 566-70.
• 16 Michalowski W, Rubin S, Slowinski R, Wilk S. Mobile clinical support system for pediatric emergencies. Journal of Decision Support Systems 2003; 36 (02) 161-76.
• 17 Ramnarayan P, Britto J. Paediatric clinical decision support systems. Arch Dis Child 2002; 87 (05) 361-2.
• 18 Brown DS, Motte S. Device design methodology for trauma applications. In. Karat CM, Lund A, Coutaz J, Karat J. editors Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI 98);1998. Los Angeles, USA. New York: ACM; 1998: 590-94.
• 19 Shiffman RN, Karras BT. Pen-based, mobile decision support in healthcare. ACM SIGBIO Newsletter 1999; 19 (02) 5-7.
• 20 Essex D. Wow! Wireless works!! Healthc Inform. 1999; 16 (10) 38-42. 44,46.
• 21 Jing J, Helal AS, Elmagarmid A. Client-server computing in mobile environments. ACM Computing Surveys 1999; 31 (02) 117-57.
• 22 Rae F. The Ottawa ankle rule. J Emerg Med 2001; 18 (02) 147.
• 23 Michalowski W, Rubin S, Slowinski R, Wilk S. Triage of acute abdominal paIn clinical use of a palm handheld in a pediatric emergency department. In Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS’04);2004. Big Island, Hawaii. Los Alamitos: IEEE; 2004: 60161a.
• 24 Berner ES, Ball MJ, Hannah KJ. editors Clinical Decision Support Systems: Theory and Practice. New York: Springer Verlag; 1999
• 25 Musen MA. Domain ontologies in software engineering: use of Protégé with the EON architecture. Method Inform Med 1998; 37 4-5 540-50.
• 26 Musen MA. Scalable software architectures for decision support. Method Inform Med 1999; 38 4-5 229-38.
• 27 Steel L. Second generation expert systems. Future Generation Computer Systems 1985; 1 (04) 213-21.
• 28 Puppe B, Ohmann C, Goos K, Puppe F, Mootz O. Evaluating four diagnostic methods with acute abdominal pain cases. Method Inform Med 1995; 34 (04) 361-8.
• 29 Pawlak Z, Slowinski R. Rough set approach to multi-attribute decision analysis. Eur J Oper Res 1994; 72: 443-59.
• 30 Slowinski R. Rough sets approach to decision analysis. AI Expert Magazine 1995; 10: 8-25.
• 31 Stefanowski J. On rough set based approaches to induction of decision rules. In. Skowron A, Polkowski L. editors. Rough Sets in Data Mining and Knowledge Discovery. Heidelberg: Physica-Verlag; 1998: 500-29.
• 32 Stefanowski J. Rough set based rule induction techniques for classification problems. In. Zimmermann HJ. editor Proceedings of the 6th European Congress on Intelligent Techniques and Soft Computing (EUFIT’98);1998. Aachen, Germany: Mainz Verlag; 1998: 109-13.
• 33 Greco S, Matarazzo B, Slowinski R. Dealing with missing data in rough set analysis of multi-attribute and multi-criteria decision problems. In. Zanakis SH, Doukidis G, Zopounidis C. editors. Decision Making: Recent Developments and Worldwide Applications. Kluwer Academic Publishers; 2000: 295-316.
• 34 Stefanowski J. Classification support based on the rough sets. Foundations of Computing and Decision Sciences 1993; 18 3-4 371-80.
• 35 Wilk S, Slowinski R, Michalowski W, Greco S. Supporting triage of children with abdominal pain in the emergency room. Eur J Oper Res 2005; 160 (03) 696-709.
• 36 Eriksson H, Puerta AR, Musen MA. Generation of knowledge-acquisition tools from domain ontologies. Int J Hum Comput Stud 1994; 41 (03) 425-53.
• 37 Johnson J. Selectors: Going beyond user-interface widgets. In. Bauersfeld P, Bennett J, Lynch G. editors Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI 92);1992. Monterey, USA. New York: ACM; 1992: 273-9.
• 38 Gennari JH, Musen MA, Fergerson RW, Grosso WE, Crubezy M, Eriksson H. et al. The evolution of Protégé: an environment for knowledge-based systems development. Int J Hum Comput Stud 2003; 58 (01) 89-123.
• 39 Michalowski W, Kersten M, Wilk S, Slowinski R. Designing man-machine interactions for mobile clinical systems: MET triage support using Palm handhelds. Eur J Oper Res 2005 (in press).
• 40 Rector AL, Horan B, Fitter M, Kay S, Newton PD, Nowlan WA. et al. User centered development of a general practice medical workstation: The PEN&PAD experience. In. Bauersfeld P, Bennett J, Lynch G. editors Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI 92);1992. Monterey, USA. New York: ACM; 1992: 447-53.
• 41 Raskin J. The Human Interface. New York: Addison Wesley; 2000
• 42 Sittig DF, Kuperman GJ, Fiskio J. Evaluating physician satisfaction regarding user interactions with an electronic medical record system. J Am Med Inform Assoc 1999; 400-4.
• 43 Ostrem J, Palm OS. ® user interface guidelines. 2003. Available from. http://www.palmos.com/dev/support/docs/uiguidelines.pdf Accessed June 15 2004
• 44 Korner H, Sondenaa K, Soreide JA, Andersen E, Nysted A, Lende TH. Structured data collection improves the diagnosis of acute appendicitis. Br J Surg 1998; 85 (03) 341-4.
• 45 Guerlain S, Le Beau K, Thompson M, Donnelly C, McClelland H, Syverud S. et al. The effect of a standardized data collection form on the examination and diagnosis of patients with abdominal pain. In Proceedings of the Human Factors and Ergonomics Society 45th Annual Meeting;2001. Minneapolis, USA: Human Factors and Ergonomics Society; 2001: 1284-9.
• 46 de Dombal FT. Computer-aided decision support in clinical medicine. Int J Biomed Comput 1989; 24 (01) 9-16.
• 47 Satyanarayanan M. Fundamental challenges of mobile computing. In Proceedings of the 14th ACM Symposium on Principles of Distributed Computing (PODC’95);1995. Ottawa, Canada: ACM; 1995: 1-7.
• 48 Quinn J. An HL7 (Health Level Seven) overview. J AHIMA 1999; 70 (07) 32-4.
• 49 SNOMED International. SNOMED Clinical Terms. 2004. Available from. http://www.snomed.org/snomedct Accessed June 16 2004
• 50 WHO Statistical Information System. IDC-10. The international statistical classification of diseases and related health problems, tenth revision. Available from. http://www.who.int/whosis/icd10/ Accessed August 4 2004
• 51 Regenstrief Institute Inc. Logical Observation Identifiers Names and Codes. Available from. http://www.loinc.org/ Accessed August 4 2004
• 52 IETF: The Internet Engineering Task Force. TSL: Transport Layer Security. Available from. http://www.ietf.org/html.charters/tls-charter.html Accessed June 17 2004
• 53 Sutton GC. Computer-aided diagnosis: A review. Br J Surg 1989; 76 (01) 82-5.