Holmström, Paul2022-09-302022-09-302022-09-30978-91-8009-272-2 (PRINT)978-91-8009-273-9 (PDF)http://hdl.handle.net/2077/68062Background The healthcare sector is under considerable pressure for cost savings and to increase efficiency. Healthcare is complex with staff of multiple professions and a variety of patient care pathways. Time pressure and minimal margins for errors, as well as tension between the hierarchical structure and the power of the professions, can make it challenging to implement new policies or procedures. Action Research (AR) is frequently used to engage staff in change processes. Outside Sweden, System Dynamics (SD) is often used to model and simulate complex issues in healthcare. Group Model Building using SD has been established to engage staff in the modelling but requires learning of the basics of SD by the participants. To overcome this barrier, it is desirable to develop methods to use SD modelling integrated into AR projects, but little research has been published about this. The overall purpose of this thesis is to deepen the understanding of using SD, by itself or combined with AR, to support groups of healthcare professionals and researchers working with change and improvement processes. Materials and methods Two research projects and five improvement cases in healthcare were studied. The research projects used SD methodology to study disease characteristics and preventive effects by different interventions. Epidemiological data from disease-specific quality registers, scientific publications, and hospital systems were used. The cases were re-analysed in depth by a multidisciplinary work group (SD, AR, medical sciences) using iterative abductive qualitative methodology. A structure for studying consultative projects was used to identify steps in the workflows of the cases. Socioanalytical questions were used to bridge between the AR and SD perspectives. Results The two research projects were epidemiological in nature and the simulations made it possible to study phenomena which were difficult to isolate and examine in reality. The projects resulted in models depicting disease trajectories which were used to test different scenarios and suggest relevant clinical interventions. In the five improvement cases, AR contributed to high levels of engagement among the participants and to the building of confidence in and ownership of the results. AR also ensured that the SD models were adequate, relevant, and rooted in reality. SD provided a coherent and consistent systems overview of the complex and complicated structure of each improvement case, offered causal rigor, and provided ample opportunities for reality checks. During the cases, the two methods were deeply integrated and always present in experiential learning processes. In both the research projects and the improvement cases, workflows and model development were adapted to each group. All cases went through divergent and convergent phases leading to shared points of reference, “project and case specific multiprofessional knowledge repositories”. It was ensured that the voice of each participant was heard and that this inspired engagement, interaction, and exploratory mutual learning activities. The facilitator had an intermediary role, acting as an "interpreter" between the group and the simulation model, ensuring that the model elucidated the issues at hand. Mutually agreed solutions were tested in silico. Conclusions The two research projects demonstrated that SD is well-suited for policy planning of disease prevention in Swedish healthcare. The methodology is cost effective and allows simulations to be carried out in silico for testing without risk to patients or organisational efficiency. It also increases the understanding of systemic interdependencies between various patient-related and intervention-related factors for different diseases. Policymakers can for instance be assisted in choosing the intervention with greatest preventive impact by being presented with likely effects from expected or plausible scenarios. The five improvement cases showed that integrating SD into AR for problems in healthcare can achieve useful, comprehensive, and robust outcomes. Results by this methodology will, by design, be calibrated to local needs and circumstances and is thereby likely to improve chances of sustained actualisation. The addition of simulations will increase certainty about expected results and speed up the problem-solving process.enghealthcareimprovementchangeimplementationaction researchsystem dynamicssimulationtext