Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Amalberti R (2001) The paradoxes of almost totally safe transportation systems. Saf Sci 37:109–126
Ash JS, Sittig DF, Poon EG, Guappone K, Campbell E, Dykstra RH (2007) The extent and importance of unintended consequences related to computerized provider order entry. J Am Med Inform Assoc 14(4):415–423
Bates DW, Kuperman GJ, Wang S, Gandhi T, Kittler A, Volk L et al (2003) Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J Am Med Inf Assoc 10:523–530
Brochard L, Rauss A, Benito S et al (1994) Comparison of three methods of gradual withdrawal from ventilatory support during weaning from mechanical ventilation. Am J Respir Crit Care Med 150:896–903
Burns SM (1999) Making weaning easier. Crit Care Nurs Clin North Am 11:465–479
Burns KE, Meade MO, Lessard MR, Keenan SP, Lellouche F (2009) Wean earlier and automatically with New technology (the WEAN study): a protocol of a multicentre, pilot randomized controlled trial. Trials 10:81
Cabana MD, Rand CS, Powe NR, Wu AW, Wilson MH, Abboud PA et al (1999) Why don’t physicians follow clinical practice guidelines? A framework for improvement. JAMA 282:1458–1465
Ely EW, Bennett PA, Bowton DL, Murphy SM, Florance AM, Haponik EF (1999) Large scale implementation of a respiratory therapist driven protocol for ventilator weaning. Am J Respir Crit Care Med 159(2):439–446
Ely EW, Meade MO, Haponik EF, Kollef MH, Cook DJ, Guyatt GH et al (2001) Mechanical ventilator weaning protocols driven by non-physician healthcare professionals: evidence-based clinical practice guidelines. Chest 20:454S–463S
Esen F, Denkel T, Telci L et al (1992) Comparison of pressure support ventilation and intermittent mandatory ventilation during weaning in patients with acute respiratory failure. Adv Exp Med Biol 317:371–376
Esteban A, Frutos F, Tobin MJ et al (1995) A comparison of four methods of weaning patients from mechanical ventilation. N Engl J Med 332:345–350
Friedman CP (2005) “Smallball” evaluation: a prescription for studying community-based information interventions. J Med Libr Assoc 93(4 Suppl):S43–S48
Herrera IA, Woltjer R (2010) Comparing a multi-linear (STEP) and systemic (FRAM) method for accident analysis. Reliab Eng Syst Saf 95(12):1269–1275
Hollnagel E (2004) Barriers and accident prevention. Ashgate, Aldershot
Hollnagel E (2008) The changing nature of risks. Ergonomics Austr J 22(1–2):33–46
Hollnagel E, Woods DD, Leveson N (eds) (2005) Resilience engineering: concepts and precepts. Ashgate Publishing Company, Brookfield
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, 2008
Kaplan B (2001) Evaluating informatics applications-clinical decision support systems literature review. Int J Med Inform 64(1):15–37
Kawamoto K, Houlihan CA, Balas EA, Lobach DF (2005) Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ 330:765
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:S69
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–395S
Mack EH, Wheeler DS, Embi PJ (2009) Clinical decision support systems in the pediatric intensive care unit. Pediatr Crit Care Med 10:23–28
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 15:299–309
Meade MO, Ely EW (2002) Protocols to improve the care of critically ill pediatric and adult patients. JAMA 288:2601–2603
Morel G, Amalberti R, Chauvin C (2008) Articulating the differences between safety and resilience: the decision-making process of professional sea-fishing skippers. Hum Factors 50:1–16
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 London
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):153
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, Chicago
Osheroff JA, Teich JM, Middleton B, Steen EB, Wright A, Detmer DE (2007) A roadmap for national action on clinical decision support. J Am Med Inf Assoc 14:141–145
Patel VL, Cohen T (2008) New perspectives on error in critical care. Curr Opin Crit Care 14:456–459
Randolph AG, Clemmer TP, East TD et al (1998) Evaluation of compliance with a computerized protocol: weaning from mechanical ventilator support using pressure support. Comput Methods Programs Biomed 57:201–215
Rose L, Presneill JJ, Johnston L, Cade JF (2008) A randomized, controlled trial of conventional versus automated weaning from mechanical ventilation using SmartCare/PS. Intensive Care Med 34(10):1788–1795, Epub 2008 Jun 25
Sinuff T, Cook D, Giacomini M, Heyland D, Dodek P (2007) Facilitating clinician adherence to guidelines in the intensive care unit: a multicenter, qualitative study. Crit Care Med 35:2083–2089
Sundström GA, Hollnagel E. Modelling risk in financial services systems: a functional risk modelling perspective. Third resilience engineering symposium, Antibes—Juan-les-Pins, France, 2008
Van Maanen J (1996) Ethnography. In: Kuper A, Kuper J (eds) The social science encyclopedia, 2nd edn. Routledge, London, pp 263–265
Vitacca M, Clini E, Porta R, Ambrosino N (2000) Preliminary results on nursing workload in a dedicated weaning center. Intensive Care Med 26(6):796–799
Weiss CH, Amaral LA (2010) Moving the science of quality improvement in critical care medicine forward. Am J Respir Crit Care Med 182(12):1461–1462
Acknowledgements
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Myneni, S., McGinnis, D., Almoosa, K., Cohen, T., Patel, B., Patel, V.L. (2016). Effective Use of Clinical Decision Support in Critical Care: Using Risk Assessment Framework for Evaluation of a Computerized Weaning Protocol. In: Gupta, A., Patel, V., Greenes, R. (eds) Advances in Healthcare Informatics and Analytics. Annals of Information Systems, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-319-23294-2_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-23294-2_10
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23293-5
Online ISBN: 978-3-319-23294-2
eBook Packages: Business and ManagementBusiness and Management (R0)