Enhancing the Informed Adoption of Emerging Technologies in Healthcare Organizations: An Evaluation Approach and a Domain Ontology to Support Decision-Making

The healthcare industry is witnessing a rapid proliferation of new Health Technologies (HTs). Their adoption holds the potential to offer significant benefits to patients and providers, but their rapid evolution poses significant challenges for decision-makers in determining whether to introduce them into real-world settings. In this context, assessing the potential value of HTs to stakeholders through Health Technology Assessment (HTA) is crucial for effective decision-making. Such an assessment becomes particularly complex in the case of emerging technologies (ETs) to be introduced in services. The value that an ET offers to stakeholders depends on how the services in which the ET is implemented are designed. However, in the early stages of ET development and deployment, the alternative service configurations the ET can enable may not be known and may need to be refined during the evaluation process once new knowledge is gained. Moreover, the value model (i.e., the framework designed to quantify and compare the overall value of the alternative service configurations) may be unknown and unstable due to the large number of elements it depends on and the uncertainties in eliciting them. Due to the novelty of the emerging technology, the performance of the service configurations across the criteria comprising the value model is itself subject to uncertainty. Finally, as HT options proliferate and continue to evolve, healthcare organizations need effective tools to identify the most suitable technologies and ensure their smooth integration into clinical workflows. To support such a challenging decision-making process, both an HTA approach to evaluate the value of HTs (even when we have to assess the value of ETs in services) and tools to ensure the wise introduction of HTs into clinical processes are needed. However, to the best of our knowledge, no existing approach faces all these challenges comprehensively. Moreover, existing conceptualizations of expert knowledge on HTs are incomplete, as they focus solely on specific aspects of the knowledge domain and, to date, there are no tools, such as knowledge-based Decision Support Systems (DSS), fully supporting healthcare service providers in the introduction of new HTs. To fill in these gaps, this thesis presents a new HTA approach called Technology Enabled Services Value Assessment (TESVA) which supports decision-making in such a challenging environment, and an ontology intended as the backbone of a DSS to facilitate the informed adoption of HTs. The TESVA approach was developed by applying Design Science Research Methodology and successfully tested and refined through two real cases in the healthcare industry: the introduction of a mobile telepresence robot in a targeted socio-care service delivered to frail users in home settings, and the introduction of a hand exoskeleton in a targeted rehabilitation service for post-stroke patients in a hospital setting. Each evaluation cycle included post-project assessments with potential users and triangulation of data collected through participant observation. Lessons learned were formalized and used to improve the robustness and generalizability of the approach. The ontology was developed using a four-phase methodology: (i) Elicitation of knowledge through literature review and interviews with experts, (ii) Conceptualization of a preliminary conceptual map (CM), (iii) Co-design of a refined CM through five focus groups with 23 experts, and (iv) Development of the ontology. A knowledge-based, collaborative approach was adopted, given the absence of relevant datasets and the complex nature of the domain. Finally, Concept-Knowledge theory (C-K theory) is employed to examine the potential for integrating the TESVA approach with a DSS based on the proposed ontology. The analysis explores how the ontology could enhance the TESVA evaluation process, as well as how it might be refined to more effectively support the assessment of ETs through the TESVA approach.