Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-24T13:23:07.188Z Has data issue: false hasContentIssue false
  • English
  • Français

DESIGN THINKING IN DATA-INTENSIVE HEALTHCARE IMPROVEMENT: LESSONS FROM A PERIOPERATIVE CASE STUDY

Published online by Cambridge University Press:  19 June 2023

Daniel James Stubbs ,
Thomas Henry Bashford  and
Peter John Clarksons
Daniel James Stubbs*
Affiliation:
University of Cambridge Department of Engineering, Health Systems Design Group
Thomas Henry Bashford
Affiliation:
University of Cambridge Department of Perioperative, Acute, Critical, and Emergency Care (PACE)
Peter John Clarksons
Affiliation:
Department of Anaesthesia, Cambridge University Hospitals NHS Foundation Trust
*
Stubbs, Daniel James, University of Cambridge Department of Engineering, United Kingdom, [email protected]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Healthcare generates vast quantities of ‘routinely collected’ data that is recognised as a valuable substrate to drive improvement. Realising this benefit however, requires the sequential distillation of new knowledge before analytical findings are used to inform real-world change. This dichotomy requires the combination of techniques from data science (to derive meaningful knowledge) and improvement (to deliver change). Recognising this transdisciplinary need and the complexity of modern healthcare, we developed an improvement project to incorporate a ‘systems approach’ into the analysis of pseudonymised perioperative data for the purpose of redesigning the systems that deliver surgical care to older patients. This required the development of novel mixed-methods workflows combining tools used to realise a systems approach in practice and to support meaningful analysis, and to translate these findings towards ‘better’ care systems. This paper recounts the incorporation of these tools into ‘dataintensive improvement’ and reflects on the relevance of design thinking to improve the conduct of the necessary data science to achieve our ultimate aim, using data to improve services for older surgical patients.

Keywords

Systems Engineering (SE)Design processBig dataHealthcare Improvement
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 1337 - 1346
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

References

Ball, J. (2019), ‘The Double Diamond: A universally accepted depiction of the design process’, Design Council, 1 October, available at: https://www.designcouncil.org.uk/our-work/news-opinion/double-diamond-universally-accepted-depiction-design-process/ (accessed 26 October 2022).Google Scholar
Berger, J.O. and Berry, D.A. (1988), ‘Statistical Analysis and the Illusion of Objectivity’, American Scientist, Sigma Xi, The Scientific Research Society, Vol. 76 No. 2, pp. 159–165.Google Scholar
Breznau, N., Rinke, E.M., Wuttke, A., Adem, M., Adriaans, J., Alvarez-Benjumea, A., Andersen, H.K., et al. (2021), ‘Observing Many Researchers Using the Same Data and Hypothesis Reveals a Hidden Universe of Uncertainty’, MetaArXiv, 24 March, https://dx.doi.org/10.31222/osf.io/cd5j9.Google Scholar
Clarkson, P.J. (2020), ‘Improving Improvement: A toolkit for Engineering Better Care’, Cambridge, available at: http://www.iitoolkit.com/ (accessed 20 March 2022).Google Scholar
Clarkson, P.J., Bogle, D., Dean, J., Tooley, M., Trewby, J., Vaughan, L., Adams, E., et al. (2017), Engineering Better Care: A Systems Approach to Health and Care Design and Continuous Improvement, Royal Academy of Engineering, London.Google Scholar
Clarkson, P.J., Buckle, P., Coleman, R., Stubbs, D., Ward, J.R., Jarrett, J., Lane, R., et al. (2004), ‘Design for patient safety: a review of the effectiveness of design in the UK health service’, Journal of Engineering Design, Vol. 15 No. 2, pp. 123–140.CrossRefGoogle Scholar
Committee on engineering and the healthcare system. (2005), Building a Better Delivery System: A New Engineering/Healthcare Partnership, edited by Reid, P.P., Compton, D., Grossman, J.H. and Fanjiang, G., National Academies Press, Washington, D.C.Google Scholar
Crowe, B., Gaulton, J.S., Minor, N., Asch, D.A., Eyet, J., Rainosek, E., Flint, K., et al. (2022), ‘To improve quality, leverage design’, BMJ Quality & Safety, Vol. 31 No. 1, pp. 70–74.CrossRefGoogle ScholarPubMed
‘Data Protection Act 2018’. (2018), 23 May, available at: https://www.legislation.gov.uk/ukpga/2018/12/contents/enacted (accessed 22 March 2021).Google Scholar
De Mauro, A., Greco, M. and Grimaldi, M. (2016), ‘A formal definition of Big Data based on its essential features’, Library Review. Great Britain. Forestry Commission, Vol. 65 No. 3, pp. 122–135.Google Scholar
Fitzgerald, L. (1999), ‘Case studies as a research tool’, Quality & Safety in Health Care, Vol. 8 No. 2, pp. 75–75.CrossRefGoogle ScholarPubMed
Goldacre, B. (2022), Better, Broader, Safer: Using Health Data for Research and Analysis, Department of Health and Social Care.Google Scholar
Hernan, M.A. and Robins, J.M. (2020), Causal Inference: What If (the Book), Chapman & Hall/CRC, Boca Raton.Google Scholar
Hripcsak, G. and Albers, D.J. (2013), ‘Next-generation phenotyping of electronic health records’, Journal of the American Medical Informatics Association: JAMIA, Vol. 20 No. 1, pp. 117–121.CrossRefGoogle ScholarPubMed
Huntington-Klein, N., Arenas, A., Beam, E., Bertoni, M., Bloem, J.R., Burli, P., Chen, N., et al. (2021), ‘The influence of hidden researcher decisions in applied microeconomics’, Economic Inquiry, Vol. 59 No. 3, pp. 944–960.CrossRefGoogle Scholar
International Organisation for Standardisation (ISO). (2015), Health Informatics - HL7 Electronic Health Records - System Functional Model (EHR FM), No. 2.Google Scholar
Kandel, S., Paepcke, A., Hellerstein, J.M. and Heer, J. (2012), ‘Enterprise Data Analysis and Visualization: An Interview Study’, IEEE Transactions on Visualization and Computer Graphics, Vol. 18 No. 12, pp. 2917–2926.CrossRefGoogle ScholarPubMed
Kohler, K. and Ercole, A. (2020), ‘Can network science reveal structure in a complex healthcare system? A network analysis using data from emergency surgical services’, BMJ Open, Vol. 10 No. 2, p. e034265.CrossRefGoogle Scholar
Kolb, D. (1983), Experiential Learning: Experience as the Source of Learning and Development, Prentice Hall, Hoboken, NJ.Google Scholar
Komashie, A., Ward, J., Bashford, T., Dickerson, T., Kaya, G.K., Liu, Y., Kuhn, I., et al. (2021), ‘Systems approach to health service design, delivery and improvement: a systematic review and meta-analysis’, BMJ Open, Vol. 11 No. 1, p. e037667.CrossRefGoogle ScholarPubMed
Kun, P., Mulder, I. and Kortuem, G. (2018), ‘Design Enquiry Through Data: Appropriating a Data Science Workflow for the Design Process’, presented at the Proceedings of the 32nd International BCS Human Computer Interaction Conference, BCS Learning and Development, https://dx.doi.org/10.14236/ewic/HCI2018.32.CrossRefGoogle Scholar
Lin, H.-S., Watts, J.N., Peel, N.M. and Hubbard, R.E. (2016), ‘Frailty and post-operative outcomes in older surgical patients : a systematic review’, BMC Geriatrics, BMC Geriatrics, Vol. 16, p. 157.CrossRefGoogle ScholarPubMed
National Confidential Enquiry into Perioperative Outcome & Death. (2010), Elective and Emergency Surgery in the Elderly: An Age Old Problem, National Confidential Enquiry into Patient Outcome & Death.Google Scholar
NHS Health Research Authority. (2022), ‘Is my study research?’, available at: http://www.hra-decisiontools.org.uk/research/ (accessed 25 October 2022).Google Scholar
Qian, G. and Mahdi, A. (2020), ‘Sensitivity analysis methods in the biomedical sciences’, Mathematical Biosciences, Vol. 323, p. 108306.CrossRefGoogle ScholarPubMed
Rumsfeld, J.S., Joynt, K.E. and Maddox, T.M. (2016), ‘Big data analytics to improve cardiovascular care: Promise and challenges’, Nature Reviews. Cardiology, Nature Publishing Group, Vol. 13 No. 6, pp. 350–359.Google Scholar
Rutter, H., Savona, N., Glonti, K., Bibby, J., Cummins, S., Finegood, D.T., Greaves, F., et al. (2017), ‘The need for a complex systems model of evidence for public health’, The Lancet, Elsevier Ltd, Vol. 390 No. 10112, pp. 2602–2604.CrossRefGoogle ScholarPubMed
Shah, A. (2019), ‘Using data for improvement’, BMJ, Vol. 364, p. l189.CrossRefGoogle ScholarPubMed
Simon, H.A. (1969), The Sciences of the Artificial, M.I.T. Press, Cambridge.Google Scholar
Skivington, K., Matthews, L., Simpson, S.A., Craig, P., Baird, J., Blazeby, J.M., Boyd, K.A., et al. (2021), ‘A new framework for developing and evaluating complex interventions: update of Medical Research Council guidance’, BMJ, Vol. 374, p. n2061.CrossRefGoogle ScholarPubMed
Stoudt, S., Vásquez, V.N. and Martinez, C.C. (2021), ‘Principles for data analysis workflows’, PLoS Computational Biology, Vol. 17 No. 3, p. e1008770.CrossRefGoogle ScholarPubMed
Stubbs, D.J. (2023), A Systems Approach to the Use of Routinely Collected Data for Healthcare Improvement, edited by Clarkson, P.J., University of Cambridge, https://dx.doi.org/10.17863/CAM.94434.CrossRefGoogle Scholar
Stubbs, D.J., Bashford, T., Gilder, F.J., Nourallah, B., Ercole, A., Levy, N. and Clarkson, P.J. (2022a), ‘Can process mapping and a multi-site Delphi of perioperative professionals inform our understanding of system-wide factors that may impact operative risk?’, BMJ Open, Vol. 12, p. e064105.CrossRefGoogle Scholar
Stubbs, D.J., Davies, B., Hutchinson, P., Menon, D.K. and Improving Care in Elderly Neurosurgery Initiative (ICENI). (2022b), ‘Challenges and opportunities in the care of chronic subdural haematoma: perspectives from a multi-disciplinary working group on the need for change’, British Journal of Neurosurgery, Vol. 36 No. 5, pp. 600–608.CrossRefGoogle Scholar
Stubbs, D.J., Davies, B.M., Bashford, T., Joannides, A.J., Hutchinson, P.J., Menon, D.K., Ercole, A., et al. (2020), ‘Identification of factors associated with morbidity and postoperative length of stay in surgically managed chronic subdural haematoma using electronic health records: a retrospective cohort study’, BMJ Open, Vol. 10 No. 6, p. e037385.CrossRefGoogle ScholarPubMed