Effective Use of Clinical Decision Support in Critical Care: Using Risk Assessment Framework for Evaluation of a Computerized Weaning Protocol
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Background: Clinical decision support aids such as computerized weaning protocols (CWPs) aim to reduce medical errors and improve patient safety. However, the dynamic nature of critical care environments demands context-specific and complexity -inclusive assessment of these support tools for optimal results.
Objective: To apply and validate the use of a risk assessment method called Functional Resonance Accident Method (FRAM), which is originally proposed for adverse event analysis in the aviation industry, to evaluate effective use of a CWP in a medical intensive care unit.
Study Design and Methods: Multiple data collection methods including (1) ethnographic observations, (2) semi-structured interviews, and (3) review of hospital documents related to workflow, procedures, and training were used to simulate a FRAM based model of the CWP and identify factors affecting its use. Subsequently, we validated our findings by shadowing clinicians during 65 weaning attempts (120 h of in vivo data).
Results: The factors posing risk to effective use of CWP included misinterpretation of CWP’s sedation assessment scale, communication and collaboration breakdowns, problems with on-time support delivery, and negative perception of the protocol among clinicians. During the in-situ validation, we found that 45 of the 65 attempts were favorable, 16 fell under near-miss category, while the remaining four were unfavorable.
Conclusions: Non-linear risk assessment method based on resilience engineering concepts is an effective approach for identification of factors for safe use of decision support aids in the real- world health care environment.
KeywordsComplexity Medical error Risk assessment Computerized weaning protocol Clinical decision support
This research was supported by an award from the James S McDonnell Foundation (Grant 220020152) to Vimla Patel. We thank all of the clinicians for their time and involvement in the study.
Disclosures: The authors have nothing to disclose and have no Conflict of Interest.
- Burns SM (1999) Making weaning easier. Crit Care Nurs Clin North Am 11:465–479Google Scholar
- Friedman CP (2005) “Smallball” evaluation: a prescription for studying community-based information interventions. J Med Libr Assoc 93(4 Suppl):S43–S48Google Scholar
- Hollnagel E (2004) Barriers and accident prevention. Ashgate, AldershotGoogle Scholar
- Hollnagel E (2008) The changing nature of risks. Ergonomics Austr J 22(1–2):33–46Google Scholar
- Hollnagel E, Woods DD, Leveson N (eds) (2005) Resilience engineering: concepts and precepts. Ashgate Publishing Company, BrookfieldGoogle Scholar
- Hollnagel E, Pruchnicki S, Woltjer R, Etcher S. Analysis of Comair flight 5191 with the Functional Resonance Accident Model. Proceedings of the 8th International Symposium of the Australian Aviation Psychology Association, Sydney, 2008Google Scholar
- Lellouche F, Mancebo J, Jolliet RJP et al (2004) Computer-driven ventilation reduces duration of weaning: a multicenter randomized controlled study. Intensive Care Med 30:S69Google Scholar
- MacIntyre NR, Cook DJ, Ely EW et al (2001) Evidence-based guidelines for weaning and discontinuing ventilatory support: a collective task force facilitated by the American College of Chest Physicians; the American Association for Respiratory Care; and the American College of Critical Care Medicine. Chest 120(6 Suppl):375S–395SCrossRefGoogle Scholar
- McLean SE, Jensen LA, Schroeder DG, Gibney NR, Skjodt NM (2006) Improving adherence to a mechanical ventilation weaning protocol for critically ill adults: outcomes after an implementation program. Am J Crit Care 15:299–309Google Scholar
- Myneni S, Cohen T, Almoosa KF, Patel VL (2014) Standard solutions for complex settings: the idiosyncrasies of a weaning protocol use in practice. In Cognitive Informatics in Health and Biomedicine (pp. 183–202). Springer LondonGoogle Scholar
- Myneni S, McGinnis D, Almoosa K., Cohen T, Patel B, Patel V (2011) Socio-technical barriers to effective use of a weaning protocol in a medical intensive care unit. Critical Care Medicine, 39(12):153Google Scholar
- Osheroff JA, Pifer EA, Teich JM, Sittig DF, Jenders RA (2005) Improving outcomes with clinical decision support: an Implementer’s guide. Healthcare Information and Management Systems Society, ChicagoGoogle Scholar
- Sundström GA, Hollnagel E. Modelling risk in financial services systems: a functional risk modelling perspective. Third resilience engineering symposium, Antibes—Juan-les-Pins, France, 2008Google Scholar
- Van Maanen J (1996) Ethnography. In: Kuper A, Kuper J (eds) The social science encyclopedia, 2nd edn. Routledge, London, pp 263–265Google Scholar