Subscribe to RSS
Data Model Requirements for a Digital Cognitive Aid for Anesthesia to Support Intraoperative Crisis ManagementFunding This research was supported by the Funk Stiftung (grant number RM-FS3–2017–1).
14 August 2019
19 January 2020
11 March 2020 (online)
Objective The aim of this study is to define data model requirements supporting the development of a digital cognitive aid (CA) for intraoperative crisis management in anesthesia, including medical emergency text modules (text elements) and branches or loops within emergency instructions (control structures) as well as their properties, data types, and value ranges.
Methods The analysis process comprised three steps: reviewing the structure of paper-based CAs to identify common text elements and control structures, identifying requirements derived from content, design, and purpose of a digital CA, and validating requirements by loading exemplary emergency checklist data into the resulting prototype data model.
Results The analysis of paper-based CAs identified 19 general text elements and two control structures. Aggregating these elements and analyzing the content, design and purpose of a digital CA revealed 20 relevant data model requirements. These included checklist tags to enable different search options, structured checklist action steps (items) in groups and subgroups, and additional information on each item. Checklist and Item were identified as two main classes of the prototype data model. A data object built according to this model was successfully integrated into a digital CA prototype.
Conclusion To enable consistent design and interactivity with the content, presentation of critical medical information in a digital CA for crisis management requires a uniform structure. So far it has not been investigated which requirements need to be met by a data model for this purpose. The results of this study define the requirements and structure that enable the presentation of critical medical information. Further research is needed to develop a comprehensive data model for a digital CA for crisis management in anesthesia, including supplementation of requirements resulting from simulation studies and feasibility analyses regarding existing data models. This model may also be a useful template for developing data models for CAs in other medical domains.
Protection of Human and Animal Subjects
Human and/or animal subjects were not included in the project.
- 1 Hepner DL, Arriaga AF, Cooper JB. , et al. Operating room crisis checklists and emergency manuals. Anesthesiology 2017; 127 (02) 384-392
- 2 Gaba DM. Perioperative cognitive aids in anesthesia: what, who, how, and why bother?. Anesth Analg 2013; 117 (05) 1033-1036
- 3 Marshall S. The use of cognitive aids during emergencies in anesthesia: a review of the literature. Anesth Analg 2013; 117 (05) 1162-1171
- 4 Marshall SD, Sanderson P, McIntosh CA, Kolawole H. The effect of two cognitive aid designs on team functioning during intra-operative anaphylaxis emergencies: a multi-centre simulation study. Anaesthesia 2016; 71 (04) 389-404
- 5 Stanford Anesthesia Cognitive Aid Group. Emergency Manual: Cognitive Aids for Perioperative Critical Events. 3rd ed. Stanford, CA: Stanford; 2016
- 6 Marshall SD. Helping experts and expert teams perform under duress: an agenda for cognitive aid research. Anaesthesia 2017; 72 (03) 289-295
- 7 Weiser TG, Haynes AB, Dziekan G, Berry WR, Lipsitz SR, Gawande AA. ; Safe Surgery Saves Lives Investigators and Study Group. Effect of a 19-item surgical safety checklist during urgent operations in a global patient population. Ann Surg 2010; 251 (05) 976-980
- 8 McEvoy MD, Hand WR, Stoll WD, Furse CM, Nietert PJ. Adherence to guidelines for the management of local anesthetic systemic toxicity is improved by an electronic decision support tool and designated “Reader”. Reg Anesth Pain Med 2014; 39 (04) 299-305
- 9 St Pierre M, Breuer G, Strembski D, Schmitt C, Luetcke B. Does an electronic cognitive aid have an effect on the management of severe gynaecological TURP syndrome? A prospective, randomised simulation study. BMC Anesthesiol 2017; 17 (01) 72
- 10 St Pierre M, Luetcke B, Strembski D, Schmitt C, Breuer G. The effect of an electronic cognitive aid on the management of ST-elevation myocardial infarction during caesarean section: a prospective randomised simulation study. BMC Anesthesiol 2017; 17 (01) 46
- 11 Watkins SC, Anders S, Clebone A. , et al. Mode of information delivery does not effect anesthesia trainee performance during simulated perioperative pediatric critical events: a trial of paper versus electronic cognitive aids. Simul Healthc 2016; 11 (06) 385-393
- 12 Goldhaber-Fiebert SN, Howard SK. Implementing emergency manuals: can cognitive aids help translate best practices for patient care during acute events?. Anesth Analg 2013; 117 (05) 1149-1161
- 13 Peute LWP, Jaspers MWM. The significance of a usability evaluation of an emerging laboratory order entry system. Int J Med Inform 2007; 76 (2-3): 157-168
- 14 Han YY, Carcillo JA, Venkataraman ST. , et al. Unexpected increased mortality after implementation of a commercially sold computerized physician order entry system. Pediatrics 2005; 116 (06) 1506-1512
- 15 Ash JS, Berg M, Coiera E. Some unintended consequences of information technology in health care: the nature of patient care information system-related errors. J Am Med Inform Assoc 2004; 11 (02) 104-112
- 16 International Organization for Standardization. ISO 9241–210:2010–03, Ergonomics of human-system interaction: part 210: human-centred design for interactive systems 2010. Geneva: Available at: https://www.iso.org/standard/52075.html . Accessed February 7, 2020
- 17 Schild S, Sedlmayr B, Schumacher A-K, Sedlmayr M, Prokosch H-U, St Pierre M. ; German Cognitive Aid Working Group. A digital cognitive aid for anesthesia to support intraoperative crisis management: results of the user-centered design process. JMIR Mhealth Uhealth 2019; 7 (04) e13226
- 18 Jacobson I, Christerson M, Jonsson P, Overgaard G. Object-Oriented Software Engineering: A Use Case Driven Approach. 1st ed. London, United Kingdom: Pearson; 1992
- 19 Horng S, Greenbaum NR, Nathanson LA, McClay JC, Goss FR, Nielson JA. Consensus development of a modern ontology of emergency department presenting problems-the hierarchical presenting problem ontology (HaPPy). Appl Clin Inform 2019; 10 (03) 409-420
- 20 Patel VL, Allen VG, Arocha JF, Shortliffe EH. Representing clinical guidelines in GLIF: individual and collaborative expertise. J Am Med Inform Assoc 1998; 5 (05) 467-483
- 21 Choi J, Currie LM, Wang D, Bakken S. Encoding a clinical practice guideline using guideline interchange format: a case study of a depression screening and management guideline. Int J Med Inform 2007; 76 (Suppl. 02) S302-S307
- 22 Boxwala AA, Peleg M, Tu S. , et al. GLIF3: a representation format for sharable computer-interpretable clinical practice guidelines. J Biomed Inform 2004; 37 (03) 147-161
- 23 Shelov E, Muthu N, Wolfe H. , et al. Design and Implementation of a Pediatric ICU Acuity Scoring Tool as Clinical Decision Support. Appl Clin Inform 2018; 9 (03) 576-587
- 24 Shiffman RN, Karras BT, Agrawal A, Chen R, Marenco L, Nath S. GEM: a proposal for a more comprehensive guideline document model using XML. J Am Med Inform Assoc 2000; 7 (05) 488-498
- 25 Noy NF, McGuinness DL. Ontology development 101: a guide to creating your first ontology. 2001 . Available at: http://protege.stanford.edu/publications/ontology_development/ontology101.pdf . Accessed February 7, 2020
- 26 Burian BK, Clebone A, Dismukes K, Ruskin KJ. More than a tick box: medical checklist development, design, and use. Anesth Analg 2018; 126 (01) 223-232
- 27 Musen MA. ; Protégé Team. The Protégé project: a look back and a look forward. AI Matters 2015; 1 (04) 4-12
- 28 Boxwala AA, Tu S, Peleg M. , et al. Toward a representation format for sharable clinical guidelines. J Biomed Inform 2001; 34 (03) 157-169