Download PDF
Methods Inf Med 2013; 52(01): 18-32
DOI: 10.3414/ME11-01-0099
Review Article
Schattauer GmbH

A Task-based Support Architecture for Developing Point-of-care Clinical Decision Support Systems for the Emergency Department

S. Wilk
1   Institute of Computing Science, Poznan University of Technology, Poland
2   Telfer School of Management, University of Ottawa, Canada
,
W. Michalowski
2   Telfer School of Management, University of Ottawa, Canada
,
D. O’Sullivan
2   Telfer School of Management, University of Ottawa, Canada
3   School of Informatics, City University London, United Kingdom
,
K. Farion
3   School of Informatics, City University London, United Kingdom
,
J. Sayyad-Shirabad
4   Departments of Pediatrics and Emergency Medicine, University of Ottawa, Canada
,
C. Kuziemsky
2   Telfer School of Management, University of Ottawa, Canada
,
B. Kukawka
1   Institute of Computing Science, Poznan University of Technology, Poland
› Author Affiliations
Further Information

Publication History

received: 12 December 2011

accepted: 01 September 2012

Publication Date:
20 January 2018 (online)

    Permissions and Reprints

Summary

Objectives: The purpose of this study was to create a task-based support architecture for developing clinical decision support systems (CDSSs) that assist physicians in making decisions at the point-of-care in the emergency department (ED). The backbone of the proposed architecture was established by a task-based emergency workflow model for a patient-physician encounter.

Methods: The architecture was designed according to an agent-oriented paradigm. Specifically, we used the O-MaSE (Organization-based Multi-agent System Engineering) method that allows for iterative translation of functional requirements into architectural components (e.g., agents). The agent-oriented paradigm was extended with ontology-driven design to implement ontological models representing knowledge required by specific agents to operate.

Results: The task-based architecture allows for the creation of a CDSS that is aligned with the task-based emergency workflow model. It facilitates decoupling of executable components (agents) from embedded domain knowledge (ontological models), thus supporting their interoperability, sharing, and reuse. The generic architecture was implemented as a pilot system, MET3-AE – a CDSS to help with the management of pediatric asthma exacerbation in the ED. The system was evaluated in a hospital ED.

Conclusions: The architecture allows for the creation of a CDSS that integrates support for all tasks from the task-based emergency workflow model, and interacts with hospital information systems. Proposed architecture also allows for reusing and sharing system components and knowledge across disease-specific CDSSs.

Keywords

Clinical decision support system - point-of-care - system architecture - agent-oriented paradigm - ontology-driven design
 

  • References

  • 1 Wyatt J, Spiegelhalter D. Field trials of medical decision-aids: potential problems and solutions. Proc Annu Symp Comput Appl Med Care. 1991: 3-7.
    Google Scholar
  • 2 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) 1339-1346.
    CrossrefPubMedGoogle Scholar
  • 3 Berner ES. Clinical decision support systems – state of the art. Rockville, MD: Agency for Healthcare Research and Quality. 2009.
    Google Scholar
  • 4 Garg AX, Adhikari NK, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J. et al Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 2005; 293 (10) 1223-1238.
    CrossrefPubMedGoogle Scholar
  • 5 Eichner J, Das M. Challenges and barriers to clinical decision support (CDS) design and implementation experienced in the Agency for Healthcare Research and Quality CDS demonstrations. Rockville, MD: Agency for Healthcare Research and Quality. 2010.
    Google Scholar
  • 6 Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ 2005; 330 7494 765-772.
    CrossrefPubMedGoogle Scholar
  • 7 Bates DW, Kuperman GJ, Wang S, Gandhi T, Kittler A, Volk L. et al Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J Am Med Inform Assoc 2003; 10 (06) 523-530.
    CrossrefPubMedGoogle Scholar
  • 8 Sittig DF, Wright A, Osheroff JA, Middleton B, Teich JM, Ash JS. et al Grand challenges in clinical decision support. J Biomed Inform 2008; 41 (02) 387-392.
    CrossrefPubMedGoogle Scholar
  • 9 Fieschi M, Dufour JC, Staccini P, Gouvernet J, Bouhaddou O. Medical decision support systems: old dilemmas and new paradigms?. Methods Inf Med 2003; 42 (03) 190-198.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 10 Isern D, Sanchez D, Moreno A. Agents applied in health care: A review. Int J Med Inform 2010; 79: 145-166.
    CrossrefPubMedGoogle Scholar
  • 11 Ciancarini P, Wooldridge M. Agent-oriented software engineering. In: Proceedings of the 22nd International Conference on Software Engineering ICSE’00. New York: ACM; 2000: 816-817.
    Google Scholar
  • 12 Wooldridge M, Ciancarini P. Agent-oriented software engineering: the state of the art. In Ciancarini P, Wooldridge M. editors Agent-Oriented Software Engineering. First International Workshop, AOSE 2000 Limerick, Ireland, June 10, 2000. Revised Papers. Berlin: Springer; 2001: 1-28.
    Google Scholar
  • 13 Jennings N. On agent-based software engineering. Artif Intell 2000; 117 (02) 277-296.
    CrossrefPubMedGoogle Scholar
  • 14 Farion K, Michalowski W, Wilk S, O’Sullivan D, Rubin S, Weiss D. Clinical decision support system for point of care use: ontology driven design and software implementation. Methods Inf Med 2009; 48 (04) 381-390.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 15 Michalowski W, Rubin S, Slowinski R, Wilk S. Mobile clinical support system for pediatric emergencies. Decis Support Syst 2003; 36 (02) 161-176.
    CrossrefPubMedGoogle Scholar
  • 16 Wright A, Sittig DF. A four-phase model of the evolution of clinical decision support architectures. Int J Med Inform 2008; 77 (10) 641-649.
    CrossrefPubMedGoogle Scholar
  • 17 Pokahr A, Braubach L, Jander K. Unifying agent and component concepts. In Dix J, Witteveen C. editors Multiagent System Technologies. The 8th German Conference, MATES 2010. Leipzig, Germany, September 27–29, 2010. Proceedings. Berlin/Heidelberg: Springer; 2010: 100-112.
    Google Scholar
  • 18 Gómez-Sanz J, Pavón J. Methodologies for developing multi-agent systems. J Univers Comput Sci 2004; 10 (04) 359-374.
    PubMedGoogle Scholar
  • 19 Hollinsworth D. The workflow reference model: Workflow Management Coallition. 1994.
    Google Scholar
  • 20 Huser V, Rasmussen LV, Oberg R, Starren JB. Implementation of workflow engine technology to deliver basic clinical decision support functionality. BMC Med Res Methodol 2011; 11: 43-61.
    CrossrefPubMedGoogle Scholar
  • 21 Lee J, Kim J, Cho I, Kim Y. Integration of workflow and rule engines for clinical decision support services. Stud Health Technol Inform 2010; 160 Pt 2 811-5.
    PubMedGoogle Scholar
  • 22 Zhao JL. Intelligent agents for flexible workflow systems. In Proceedings of the AIS Americas Conference on Information Systems. Baltimore: MD; 1997: 238-239.
    Google Scholar
  • 23 Isern D, Sánchez D, Moreno A. HeCaSe2: a multi-agent ontology-driven guideline enactment engine. In Burkhard H-D, Lindemann G, Verbrugge R, Varga L. editors Multi-agent systems and applications V. 5th International Central and Eastern European Conference on Multi-Agent Systems, CEEMAS 2007, Leipzig, Germany, September 25–27, 2007. Proceedings; Springer: 2007: 322-324.
    Google Scholar
  • 24 Isern D, Moreno A, Sánchez D, Hajnal Á, Pedone G, Varga LZ. Agent-based execution of personalised home care treatments. Appl Intell 2009; 34 (02) 155-180.
    PubMedGoogle Scholar
  • 25 Braun L, Wiesman F, van der Herik J, Hasman A. Agent support in medical information retrieval. In Cortes U, Fox J, Moreno A, Nealon J. editors Proceedings of the 3rd Workshop on Agents Applied in Health Care, IJCAI 2005, AAAI. Edinburgh. 2005: 16-25.
    Google Scholar
  • 26 Horsky J, Gutnik L, Patel VL. Technology for emergency care: cognitive and workflow considerations. AMIA Annu Symp Proc. 2006: 344-348.
    Google Scholar
  • 27 Perina DG, Beeson MS, Char DM, Counselman FL, Keim SM, McGee DL. et al The 2007 model of the clinical practice of emergency medicine: the 2009 update 2009 EM Model Review Task Force. Ann Emerg Med 2011; 57 (03) e1-15.
    CrossrefPubMedGoogle Scholar
  • 28 Lenz R, Reichert M. IT support for healthcare processes – premises, challenges, perspectives. Data Knowl Eng 2007; 61 (01) 39-58.
    CrossrefPubMedGoogle Scholar
  • 29 Landman AB, Bernstein SL, Hsiao AL, Desai RA. Emergency department information system adoption in the United States. Acad Emerg Med 2010; 17 (05) 536-544.
    CrossrefPubMedGoogle Scholar
  • 30 Prietula MJ, Simon HA. The experts in your midst. Harvard Bus Rev 1989; 67 (01) 120-124.
    PubMedGoogle Scholar
  • 31 Bagga W, Molva R. Policy-based cryptography and applications. In Goos G, Hartmanis J, van Leeuwen J. editors Financial Cryptography and Data Security. 9th International Conference, FC 2005, Roseau, The Commonwealth of Dominica, February/March, 2005. Revised papers. Berlin: Springer; 2005: 72-87.
    Google Scholar
  • 32 Körner H, Söndenaa K, Söreide JA, Andersen E, Nysted A, Lende TH. Structured data collection improves the diagnosis of acute appendicitis. Br J Surg 1998; 85 (03) 341-344.
    CrossrefPubMedGoogle Scholar
  • 33 Kong GL, Xu DL, Yang JB. Clinical decision support systems: a review on knowledge representation and inference under uncertainties. Int J Comput Intell Syst 2008; 1 (02) 159-167.
    PubMedGoogle Scholar
  • 34 Bakken S, Currie LM, Lee N-J. Roberts WD, Collins SA, Cimino JJ. Integrating evidence into clinical information systems for nursing decision support. Int J Med Inform 2008; 77 (06) 413-420.
    CrossrefPubMedGoogle Scholar
  • 35 Shortliffe EH. The adolescence of AI in medicine: will the field come of age in the 90’s?. Artif Intell Med 1993; 5 (02) 93-106.
    CrossrefPubMedGoogle Scholar
  • 36 Burkle T, Schweiger R, Altmann U, Holena M, Blobel B, Dudeck J. Transferring data from one EPR to another: content-syntax-semantic. Methods Inf Med 1999; 38 4–5 321-325.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 37 Mori AR, Consorti F. Integration of clinical information across patient records: a comparison of mechanisms used to enforce semantic coherence. IEEE Trans Inf Technol Biomed 1998; 2 (04) 243-253.
    CrossrefPubMedGoogle Scholar
  • 38 Berlin A, Sorani M, Sim I. A taxonomic description of computer-based clinical decision support systems. J Biomed Inform 2006; 39 (06) 656-667.
    CrossrefPubMedGoogle Scholar
  • 39 Ammenwerth E, Buchauer A, Bludau B, Haux R. Mobile information and communication tools in the hospital. Int J Med Inform 2000; 57 (01) 21-40.
    CrossrefPubMedGoogle Scholar
  • 40 Folz-Murphy N, Partin M, Williams L, Harris CM, Lauer MS. Physician use of an ambulatory medical record system: matching form and function. Proc AMIA Symp. 1998: 260-264.
    Google Scholar
  • 41 DeLoach SA. Engineering organization-based multiagent systems. In Garcia A, Choren R, Lucena C, Giorgini P, Holvoet T, Romanovsky A. editors System Engineering for Multi-agent Systems IV: Research Issues and Practical Applications. Berlin: Springer; 2006: 109-125.
    Google Scholar
  • 42 Crubézy M, O’Connor M, Buckeridge DL, Pincus Z, Musen MA. Ontology-centered syndromic surveillance for bioterrorism. IEEE Intell Syst 2005; 20 (05) 26-35.
    PubMedGoogle Scholar
  • 43 Musen MA. Scalable software architectures for decision support. Methods Inf Med 1999; 38 4–5 229-238.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 44 Swartout W, Tate A. Ontologies. IEEE Intel Syst App 1999; 14 (01) 18-19.
    PubMedGoogle Scholar
  • 45 O’Sullivan D, Wilk S, Michalowski W, Farion K. Automatic indexing and retrieval of encounter-specific evidence for point-of-care support. J Biomed Inform 2010; 43 (04) 623-631.
    CrossrefPubMedGoogle Scholar
  • 46 Michalowski W, Wilk S, Farion K, Pike J, Rubin S, Slowinski R. Development of a decision algorithm to support emergency triage of scrotal pain and its implementation in the MET system. INFOR 2005; 43 (04) 287-301.
    PubMedGoogle Scholar
  • 47 Błaszczyński J, Farion K, Michalowski W, Wilk S, Rubin S, Weiss D. Mining clinical data: selecting decision support algorithm for the MET-AP system. In Miksch S, Hunter J, Keravnou ET. editors Artificial Intelligence in Medicine. 10th Conference on Artificial Intelligence in Medicine, AIME 2005, Aberdeen, UK, July 23–27, 2005. Proceedings. Berlin/Heidelberg/New York: Springer; 2005: 429-433.
    Google Scholar
  • 48 Nadkarni PM, Marenco L, Chen R, Skoufos E, Shepherd G, Miller P. Organization of heterogeneous scientific data using the EAV/CR representation. J Am Med Inform Assoc 1999; 6 (06) 478-493.
    CrossrefPubMedGoogle Scholar
  • 49 Wilk S, Michalowski W, Farion K, Kersten M. Interaction design for mobile clinical decision support systems: the MET system solutions. Found Comput Decis Sci 2007; 32 (01) 47-61.
    PubMedGoogle Scholar
  • 50 Tu SW, Campbell JR, Glasgow J, Nyman MA, McClure R, McClay J. et al The SAGE Guideline Model: achievements and overview. J Am Med Inform Assoc 2007; 14 (05) 589-598.
    CrossrefPubMedGoogle Scholar
  • 51 Sayyad Shirabad J, Wilk S, Michalowski W, Farion K. Implementing an Integrative Multi-agent Clinical Decision Support System with Open Source Software. J Med Syst 2012; 36 (01) 123-137.
    CrossrefPubMedGoogle Scholar
  • 52 Wilk S, Michalowski W, Farion K, Sayyad Shirabad J. MET3-AE system to support management of pediatric asthma exacerbation in the emergency department. Stud Health Technol Inform 2010; 160 Pt 2 841-845.
    PubMedGoogle Scholar
  • 53 Bellifemine FL, Caire G, Greenwood D. Developing multi-agent systems with JADE. Chichester: John Wiley & Sons Ltd; 2007.
    Google Scholar
  • 54 Gennari JH, Musen MA, Fergerson RW, Grosso WE, Crubézy 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.
    CrossrefPubMedGoogle Scholar
  • 55 Khondoker MR, Mueller P. Comparing ontology development tools based on an online survey. In Proceedings of the World Congress on Engineering (WCE). London: 2010: 188-192.
    Google Scholar
  • 56 The Cochrane Collaboration (Internet). The Cochrane Library (cited August 3, 2011). Available from. http://www.thecochranelibrary.com/.
    PubMedGoogle Scholar
  • 57 Alberta Clinical Practice Guidelines Program. Guidelines for the management of acute asthma in adults and children: Alberta Medical Association; 2002.
    PubMedGoogle Scholar
  • 58 Ducharme FM, Chalut D, Plotnick L, Savdie C, Kudirka D, Zhang X. et al The Pediatric Respiratory Assessment Measure: a valid clinical score for assessing acute asthma severity from toddlers to teenagers. J Pediatr 2008; 152 (04) 476-480. 80 e1
    CrossrefPubMedGoogle Scholar
  • 59 Lavrac N. Selected techniques for data mining in medicine. Artif Intell Med 1999; 16 (01) 3-23.
    CrossrefPubMedGoogle Scholar
  • 60 Mirth Corporation [Internet]. Mirth Connect (cited Aug 8, 2011). Available from. http://www.mirthcorp.com/community/mirth-connect.
    PubMedGoogle Scholar
  • 61 Henry K, Campbell S, Maki M. A comparison of observed and self-reported compliance with universal precautions among emergency department personnel at a Minnesota public teaching hospital: implications for assessing infection control programs. Ann Emerg Med 1992; 21 (08) 940-946.
    CrossrefPubMedGoogle Scholar
  • 62 O’Sullivan D, Doyle J, Michalowski W, Wilk S, Farion K, Kuziemsky C. Assessing the motivation of MDs to use computer-based support at the point-of-care in the Emergency Department. AMIA Annu Symp Proc. 2011: 1045-1054.
    Google Scholar
  • 63 Kawamoto K, Lobach DF. Design, implementation, use and preliminary evaluation of SEBASTIAN, a standards-based Web service for clinical decision support. AMIA Annu Symp Proc. 2005: 380-384.
    Google Scholar
  • 64 Wright A, Sittig DF. SANDS: a service-oriented architecture for clinical decision support in a National Health Information Network. J Biomed Inform 2008; 41 (06) 962-981.
    CrossrefPubMedGoogle Scholar
  • 65 Wright A, Sittig DF. A framework and model for evaluating clinical decision support architectures. J Biomed Inform 2008; 41 (06) 982-990.
    CrossrefPubMedGoogle Scholar
  • 66 Lopez DM, Blobel B. Enhanced semantic interoperability by profiling health informatics standards. Methods Inf Med 2009; 48 (02) 170-177.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 67 Hine MJ, Farion K, Michalowski W, Wilk S. Decision making by emergency room physicians and residents: implications for the design of clinical decision support systems. Int J Healthc Inf Syst Inform. 2009; 4 (02) 17-35.
    PubMedGoogle Scholar