Event Oriented Representation for Collaborative Activities (EORCA)

L. Pellegrin; N. Bonnardel; F. Antonini; J. Albanese; C. Martin; H. Chaudet

doi:10.1160/ME0417

Methods of Information in Medicine, Table of Contents

Methods Inf Med 2007; 46(05): 506-515
DOI: 10.1160/ME0417

Paper

Schattauer GmbH

Event Oriented Representation for Collaborative Activities (EORCA)

A Method for Describing Medical Activities in Severely-injured Patient Management

L. Pellegrin

¹Biomathematics and Medical Informatics research team, LIF UMR CNRS 6166, Faculty of Medicine, University of Aix-Marseilles, Marseilles, France

,

N. Bonnardel

²University of Provence, Research Centre in Psychology of Knowledge, Language and Emotion (PsyCLE; E.A. 3273), Department of Cognitive and Experimental Psychology, Aix-en-Provence, France

,

F. Antonini

³Critical Care and Intensive Care Unit, AP-HM, Hôpital Nord, Marseilles, France

,

J. Albanese

³Critical Care and Intensive Care Unit, AP-HM, Hôpital Nord, Marseilles, France

,

C. Martin

³Critical Care and Intensive Care Unit, AP-HM, Hôpital Nord, Marseilles, France

,

H. Chaudet

¹Biomathematics and Medical Informatics research team, LIF UMR CNRS 6166, Faculty of Medicine, University of Aix-Marseilles, Marseilles, France

› Author Affiliations

Abstract

Summary

Objectives: In this paper, we introduce a method that aims at describing components of medical activities that are performed by a medical team, including physicians and nurses, during patients’ management in an ICU (intensive care unit). This method is based on formal taskanalyses developed in cognitive ergonomics.

Our ultimate aim is to build a method covering the observation and the representation of collective activities during patients’ management, which should be re-usable by the team members in order to prepare themselvesfor accreditation.

Methods: This method comprises two main steps: – the formal observations of medical staff’s activities that occur during patient management, -a representation of the findings with regard to an ontology and a temporal flowchart, which describes actors and events related to patient management.

Results: This paper describes field studies performed in ICUs. This method has been used for analyzing the management of 24 cases of neurological and multiple traumas. We have represented the different actions of the medical team members (clinicians, nurses and outside medical consultants).

Conclusion: The results allow us to identify the specific features of these complexand time-constrained situations, especially about the strong collaborative activities between members of the patient-care teams, especially the interaction between information management and medical actions.

Keywords

Medical decision-making - collaborative work - task performance and analysis - intensive care unit

Full Text

References

References
1 Reddy P, Pratt W, Dourish P, Shabot M. Sociotechnical requirements analysis for clinical systems. Methods Inf Med 2003; 42: 437-44.
2 Celi LA. et al. The eICU: it’snotjusttelemedicine. Crit Care Med 2001; 29: N183-N189.
3 Hanson W, Marshall BE. Artificial intelligence applications in the intensive care unit. Crit Care Med 2001; 29: 427-35.
4 De Clerq PA, Blom J, Korsten H, Hasman A. Approaches for creating computer-interpretable guidelines that facilitate decision-support. Artificial Intelligence in Medicine 2004; 31: 1-25.
5 Fuchsberger C. et al. Testing Asbru Guidelines and Protocols for Neonatal Intensive Care, Proc 10th Conf on Artificial Intelligence in Medicine, AIME 2005, Springer, Lecture Notes in Artificial Intelligence 3581. 2005: 101-10.
6 Stausberg J. et al. Guideline validation in multiple trauma care through business process modelling. Int J of Medical Informatics 2003; 70: 301-7.
7 Adhikari N, Lapinsky SE. Medical informatics in the intensive care unit: overview oftechnology assessment. J Crit Care 2003; 18: 41-7.
8 Reddy M. et al. Asking Questions: Information Needs in a Surgical Intensive Care Unit. Proc AMIA Symp. 2002: 657-51.
9 Brown PJ. et al. Information exchange in the NICU: what sources of patient data do physicians prefer to use?. Int J of Medical Informatics 2004; 73: 349-55.
10 Wang D. et al. Representation Primitives, Process Models and Patient Data in Computer-Interpretable Clinical Practice Guidelines: A Literature Review of Guideline Representation Models. Int J of Medical Informatics 2002; 68: 59-70.
11 Patel VL. et al. Toward a Framework for Computer-Mediated Collaborative Design in Medical Informatics. Methods Inf Med 1999; 38: 158-76.
12 Gaba DM. Dynamic decision-making in anesthesiology: Cognitive models and training approaches. Evans DA, Patel VL. Advanced Models of Cognition for Medical Training and Practice.. Heidelberg, Germany: Springer-Verlag; 1992: 123-48.
13 Xiao Y. et al. Task complexity in Emergency Medical Care and its implications for team coordination. Human Factors 1996; 38: 636-45.
14 Nyssen AS, De Keyser V. Improving Training in Problem Solving Skills: Analysis of Anesthetist’s Performance in Simulated Problem Situations. Le travail humain 1998; 61: 387-402.
15 Alberdi E. et al. Computerisation and decision making inneonatal intensive care: A cognitive engineering investigation. J Clin Monit 2000; 16: 85-94.
16 Reddy M. et al. Asking Questions: Information Needs in a Surgical Intensive Care Unit. Proc AMIA Symp. 2002: 647-51.
17 Bricon-Souf N. et al. Dynamic Workflow Model for complex activity in intensive care unit. Int J of Medical Informatics 1999; 53: 143-50.
18 Reddy MC, Dourish P, Pratt W. Coordinating Heterogeneous Work Information and Representation in Medical Care. Proc. of the 7th European Conference on Computer Supported Cooperative Work-ECSCW’01. 2001: 239-58.
19 Leplat J. Task complexity in work situations. Goodstein LP, Andersen HP, Olsen SE. Tasks, Errors and mental models.. London: Taylor & Francis; 1998: 105-15.
20 Reason J. Human Error.. Cambridge MA: Cambridge University Press; 1990
21 Hoc JM, Cacciabue PC, Hollnagel E. Expertise and technology. Hillsdale NJ: Lawrence Erlbaum Associate; 1995
22 De Keyser V, d’Ydewalle G, Vandierendonck A. Time and the dynamic control ofbehavior.. Göttingen: Hogrefe & Huber Publishers; 1998
23 Albanese J, Arnaud S. Traumatisme crânien chez le polytraumatisé. Sfar Conférences d’actualisation. 41ème congrès d’anesthésie et de réanimation.. Paris: 1999: 737-63.
24 Bullock R. Joint Section on Trauma and Critical Care of the American Association of Neurological Surgeons and the Brain Trauma Foundation. Guidelines for the Management of Severe Head Injury.. Park Ridge Ill.: American Association of Neurological Surgeons; 2000
25 Busse DK, Johnson CW. Identification and Analysis of Incidents in Complex, Medical Environments. Johnson CW. First Workshop on Human Error and Clinical Systems.. Glasgow: 1999: 101-20.
26 Gaba DM, Howard SK. Situation awareness. Anesthesiology Human Factors 1995; 37: 20-31.
27 Blandford A, Wong W. Situation awareness in emergency medical dispatch. Int J Human-Computer Studies 2004; 61: 421-52.
28 Cicourel AV. The Integration of Distributed Knowledge. Collaborative Medical Diagnosis. Galegher J, Kraut RE, Egido C. Intellectual Teamwork.. Hillsdale NJ: Lawrence Erlbaum Associates; 1990: 221-42.
29 Chaudet H. Extending the event calculus for tracking epidemic spread.. Artificial Intelligence in Medicine.; In press. Available online August 1, 2005-PMID: 16076554.
30 Sebillotte S. Méthodologie pratique d’analyse de la tâche en vue de l’extraction de caractéristiques pertinentes pour la conception d’interfaces. Rapport technique N° 163.. INRIA; 1994
31 Pellegrin L. et al. EORCA: a collaborative activities representation for building guidelines from field observations. Miksch S. et al. Artifical Intelligence in Medicine, 10th Con on Artifical Intelligence in Medicine, AIME 05, Aberdeen UK, Lectures notes in Computer Science.. Berlin: Springer Editions; 2005.. 111-20.
32 Gangemi A. et al. Sweetening ontologies with DOLCE. Gómez-Pérez A, Benjamins VR. Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web, 13th International Conference, EKAW 2002. Siguenza, Spain: Oct 1-4. Springer; 2002: 166-81.
33 Masolo C. et al. Wonder Web deliverable D18 Ontology Library (Final), IST Project 2001-33052 Wonder Web: Ontology Infrastructure for the Semantic Web. Deliverable. 2003 http://wonderweb.semanticweb.org/deliverables/documents/D18.pdf.
34 Castelfranchi C. Modelling Social Action for AI Agents. Artificial Intelligence 1998; 103: 157-82.
35 Manser T, Wehner T. Analysing action sequences: Variations in Action Density in the Administration of Anaesthesia. Cognition Technology & Work. 2002; 4: 71-81.
36 Rumbaugh J, Jacobson I, Booch G. The Unified Modelling Language Reference Manual.. Reading: Addison Wesley Longman Inc.; 1999
37 Ewing G. et al. Observational Data Acquired on a Neonatal Intensive Care Unit.. University of Aberdeen Computing Science Departmental Technical Report TR; 0205 2002
38 Moss J, Xiao Y. A comparison of communications needs in two operating room suites. Proc AMIA Symp. 2002: 545-7.
39 Hollenberg SM. et al. Practice parameters for hemodynamic support of sepsis in adult patients: 2004 update. Crit Care Med 2004; 32: 1928-48.
40 Sebillotte S. Méthodologie pratique d’analyse de la tâche en vue de l’extraction de caractéristiques pertinentes pour la conception d’interfaces. Technical report N° 163.. INRIA. Rocquencourt; France: 1994
41 Paterno F. Model-based Design and Evaluation of Interactive Applications.. Heidelberg: Springer Verlag; 1999
42 Van Welie M, Van der Veer GC. Groupware Task Analysis. Hollnagel E. Handbook of cognitive task design.. Mahwah, NJ: Erlbaum; 2003: 447-76.
43 Hoc JM. Towards a cognitive approach to human-machine cooperation in dynamic situations. Int J of Human-Computer Studies 2001; 54: 509-40.
44 Dojat M. et al. Scenario recognition for temporal reasoning in medical domains. Artificial Intelligence in Medicine 1998; 14: 139-55.