NDM_2017_Carman_Final.pdf (344.51 kB)
Weak signals in healthcare: A case study on community-based patient discharge
conference contribution
posted on 2018-03-14, 15:54 authored by Evi Carman, Michael FrayMichael Fray, Patrick WatersonPatrick WatersonTo adjust performance to ensure the success of a task and prevent error, it is necessary to anticipate, identify and respond to variations in the work system. The objectives of this study were to develop a framework for the analysis of signals, which provide an indication of variations in the system, in the healthcare environment and qualitatively investigate signals in the context of community-based
patient discharge. In addition to the signals, both traditional (Safety-I) and proactive safety (SafetyII)
elements were investigated with six expert groups, from the field of community-based patient discharge. The signals identified and the safety elements were analysed using the SEIPS 2.0 model. The sources of the signals were identified as originating from work system elements. The proposed
framework and method provide a preliminary basis for the investigation of signals and assists in highlighting the role that these can play in safety behaviour.
Funding
The project was commissioned as a result of a successful joint bid for funding by Health Partnerships, a Division within Nottinghamshire Health Care NHS Foundation Trust and Loughborough University Design School.
History
School
- Design
Published in
The 13th International Conference on Naturalistic Decision MakingPages
314 - 321Citation
CARMAN, E-M., FRAY, M. and WATERSON, P., 2017. Weak signals in healthcare: A case study on community-based patient discharge. IN: Gore, J. and Ward, P. (eds). NDM13 Naturalistic Decision Making and Uncertainty. Proceedings of the 13th International Conference on Naturalistic Decision Making, Bath, UK, 20-23 June 2017, pp.314-321.Publisher
© The University of BathVersion
- AM (Accepted Manuscript)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Acceptance date
2017-03-17Publication date
2017Notes
This is a conference paper.ISBN
9780861971947Publisher version
Language
- en