Informing the design of Integrating Data Software in Critical Care: A Systematic Review and Meta-Ethnography on the Use of Continuous Monitoring Data
CCCF ePoster library. Kolodzey L. Oct 28, 2015; 117363; P108
Lauren Kolodzey
Lauren Kolodzey
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Topic: Systematic Review/Meta-analysis

Informing the design of Integrating Data Software in Critical Care: A Systematic Review and Meta-Ethnography on the Use of Continuous Monitoring Data

Ying Ling Lin, L. Kolodzey, C. Nickel, P. Trbovich, A. Guerguerian

Interdepartmental Division of Critical Care Medicine, Hospital for Sick Children, Toronto, Canada | Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada | Learning Institute, Hospital for Sick Children, Toronto, Canada | Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada | Interdepartmental Division of Critical Care Medicine, Hospital for Sick Children, Toronto, Canada


In the modern technology-driven intensive care unit (ICU), the complex critically-ill patient easily generates over a thousand individual data points on a daily basis. In addition to clinical notes, medical images, and laboratory test results, ICU clinicians interpret immense arrays of continuous monitoring data generated by technological devices throughout the clinical environment, potentially resulting in the experience of information overload. This problem of overwhelming continuous monitoring data may be solved through software that integrates this data and displays it on a single screen. We propose a global review of studies related to different steps of the user-centred design cycle and divide these into two parts to answer two distinct questions.


In Part 1 of this systematic review, we focus on the qualitative studies that reveal which data are generated by continuous monitoring technologies in the ICU, as well as how this information is used by clinicians. In other words, these studies provide insight into the context, users, and work of the critical care environment as related to continuous monitoring. This part of the systematic review thus aims to answer the following research question: In critical care, what data and information from continuous monitoring technology impact clinical decision-making, and how?


In the present systematic review, guided by the Preferred Reporting Items for Systematic Reviews (PRISMA) statement, we report the current human factors studies which are user-driven, rather than technology-driven, to inform the context of use and interface design of integrating data displays, while also evaluating the impact of these displays on clinical performance.

The systematic search of the literature was conducted in five databases: three medical (Ovid, Embase, CCRCT), one engineering (Web of Science), and one psychology (PsycINFO), from January 2000 to May 2014.

A tool to report qualitative study completeness, from healthcare research, is proposed and was successfully applied to this review’s studies with very good agreement, and a meta-ethnography technique was used to integrate and synthesize the qualitative concepts therein.


The systematic search returned a total of 6111 articles with 20 articles matching the relevance criteria. A second screening of references from these 20 articles yielded a further four relevant articles. From the total of 24 articles, seven studies were found to collectively answer this question.

Conclusion: This systematic review of human factors studies, of both qualitative and quantitative nature, is the first of its kind. The review highlights the variety of reporting techniques of qualitative study findings. In addition, the strength of synthesis using the meta-ethnography technique was demonstrated.


1. Ahmad, S., et al., Continuous multi-parameter heart rate variability analysis heralds onset of sepsis in adults. PLoS One, 2009. 4(8): p. e6642.

2. Gomez, H., et al., Development of a multimodal monitoring platform for medical research. Conf Proc IEEE Eng Med Biol Soc, 2010. 2010: p. 2358-61.

3. Peute, L.W., et al., A framework for reporting on Human Factor/Usability studies of Health Information Technologies. Stud Health Technol Inform, 2013. 194: p. 54-60.

4. Bowling, A., Research methods in health: investigating health and health services. 2014: McGraw-Hill Education (UK).

5. Staggers, N. and J.W. Blaz, Research on nursing handoffs for medical and surgical settings: an integrative review. J Adv Nurs, 2012. 69(2): p. 247-62.

6. Le Roux, P., Physiological monitoring of the severe traumatic brain injury patient in the intensive care unit. Curr Neurol Neurosci Rep, 2013. 13(3): p. 331.

7. Rasmussen, J., W. Rouse, and L.P. Goodstein, Discriminative Display Support for Process Operators, in Human Detection and Diagnosis of System Failures. 1981, Springer US. p. 433-449.

8. McAlpine, B. and D. VanKampen, Clinical engineering and information technology: Working together to implement device integration. Biomedical Instrumentation and Technology WHO, 2011. 45(6): p. 445-449.

9. Brown, H., et al., Continuous Monitoring in an Inpatient Medical-Surgical Unit: A Controlled Clinical Trial. The American journal of medicine, 2014. 127(3): p. 226-232.

10. Kurosu, M., Human-Computer Interaction: Human-Centred Design Approaches, Methods, Tools and Environments: 15th International Conference, HCI International 2013, Las Vegas, NV, USA, July 21-26, 2013, Proceedings. 2013: Springer Berlin Heidelberg.

11. Pagliari, C., Design and Evaluation in eHealth: Challenges and Implications for an Interdisciplinary Field. Journal of Medical Internet Research, 2007. 9(2): p. e15.

12. Weir, C.R., N. Staggers, and S. Phansalkar, The state of the evidence for computerized provider order entry: a systematic review and analysis of the quality of the literature. Int J Med Inform, 2009. 78(6): p. 365-74.

13. Harden, A., Mixed-methods systematic reviews: Integrating quantitative and qualitative findings. Focus: A Publication of the National center for the Dissemination of Disability Research (NCDDR) Technical Brief, 2010(25).

14. Noblit, G.W. and R.D. Hare, Meta-ethnography: Synthesizing qualitative studies. Vol. 11. 1988: Sage.

15. Britten, N., et al., Using meta ethnography to synthesise qualitative research: a worked example. Journal of Health Services Research & Policy, 2002. 7(4): p. 209-215.

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