PCN45 Systems Models to Identify Implementation Requirements for Disruptive and Complex Health Technologies: A Conceptual Model Reflecting the Organization of Whole-Genome Sequencing in Lung Cancer

Abstract

After the value of complex disruptive health technologies is established, health organizations face numerous decisions regarding their implementation, which in turn affect accessibility and affordability. This conceptual paper shows how dynamic simulation modeling and systems science can be utilized in HTA to inform organizational decisions regarding the use of complex disruptive health technologies. Using the case study of the nationwide implementation of Whole Genome Sequencing (WGS) into clinical practice in lung cancer in the Dutch healthcare system, we demonstrated how explicitly modeling of the organization of care provides essential insight into the affordability and accessibility of care. We adopted the principles of systems modeling, and thus also consider the broader system in which WGS may be implemented. The relevant system and actors have been identified in discussion with stakeholders and were implemented as a discrete-event simulation model in AnyLogic. The model included workflows of hospitals, genomic services, and molecular tumor boards, while also considering technical and social aspects of WGS. Analysis of three scenarios, describing differences in the organization of care, was conducted to illustrate the potential consequences to the affordability and accessibility of care. Both intermediate outcomes, such as diagnostic turnaround time, guideline-based treatment decisions, access to WGS, as well as final outcomes, such as the number of patients deceased and cost per patient, were used to assess the consequences of differences in the organization of care related to WGS. We have shown how the organization of care and the way WGS is integrated into clinical practice can influence the potential value of WGS. Our approach is, therefore, useful for determining which system changes are required to accommodate and make optimal use of new health technologies. Hence, our systems approach complements conventional approaches focusing on expected impact in a hypothetical perfect organizational context.