Uncertainty, emergence and adaptation: A complex adaptive systems approach to quality improvement

AbstractAbstractThe healthcare quality improvement (QI) literature is replete with examples stating that continued failure to regard healthcare as a complex adaptive system (CAS) reduces the effectiveness of quality improvement initiatives. Recommendations and strategies for managing change within CAS exist, but the specific mechanisms that bring about successful change within CAS and the implications for quality practitioners are under-explored. This article presents a generalizable model for QI within CAS and provides a specifically CAS explanation for incremental change. We develop a conceptual model from foundational CAS principles that is then operationalized as an agent-based simulation model. Our model captures critical complex system behavior in a generic manner easily applied to different improvement contexts. We tested the simulation model using a recognizably complex healthcare improvement case: reducing antipsychotic prescribing levels in aged residential care. Nonlinear phase transitions were observed in the agent network, conditioned on the network’s ability to learn solution options and simultaneously maintain cooperation. We believe that a CAS explanation of change can assist practitioners navigating complex QI activities.Keywords: agent-based modelcomplex adaptive systemshealthcare improvementimprovement networksproblem-solving strategies AcknowledgementsThe authors thank the anonymous peer reviewers for their constructive feedback to refine this article.Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationNotes on contributorsWilliam WilsonWilliam (Bill) Wilson is a Lean coach with Te Whatu Ora–Health New Zealand and a PhD candidate at Massey University, New Zealand. Bill has a master’s degree in quality systems management and extensive professional quality experience in diverse industry sectors. Bill’s research interests include Lean, Six Sigma, change management, and systems thinking.Scott McLachlanScott McLachlan has an MPhil in Information Science (Massey), a GDL (Waikato) and LLM (ANU), and completed his PhD in Computer Science (Health Informatics) with the Risk and Information Management (RIM) group at Queen Mary, University of London (QMUL) researching learning health systems and Bayesian-based decision solutions for health and legal applications. Scott is currently a lecturer in digital technologies for healthcare with King’s College London with a focus on the health law, ethics and application of AI to medicine.Kudakwashe DubeKudakwashe Dube has a PhD, BSc hons and BSc in Computer Science. Kudakwashe is a Research Fellow in Digital Health Technologies at King’s College London, UK, with a research focus on Computer Science and Health Informatics.Kathleen PotterDr. Kathleen Potter is a general practitioner and primary care researcher with a strong interest in end-of-life care and deprescribing in people living in residential age care. Kathleen has a particular passion for using clinical data to improve clinical care.Nihal JayamahaDr. Nihal Jayamaha is a senior lecturer attached to Massey University, New Zealand. Nihal received his PhD in 2008 from the same university, with an endorsement in Technology Management. Nihal’s teaching and research interests cover quality engineering, quality improvement, sustainability, quality management models and systems, and latent variable modeling techniques (e.g., structural equation modeling). Nihal has published in prestigious academic journals such as the International Journal of Production Research, Journal of Industrial Ecology, Environmental Science & Technology, Total Quality Management & Business Excellence, International Journal of Lean Six Sigma, International Journal of Quality and Reliability Management, Benchmarking: An International Journal, The TQM Journal, and Measuring Business Excellence.