An Exploration of Visual Analytic Techniques for XAI: Applications in Clinical Decision Support

Artificial Intelligence (AI) systems exhibit considerable potential in providing decision support across various domains. In this context, the methodology of eXplainable AI (XAI) becomes crucial, as it aims to enhance the transparency and comprehensibility of AI models\u27 decision-making processes. However, after a review of XAI methods and their application in clinical decision support, there exist notable gaps within the XAI methodology, particularly concerning the effective communication of explanations to users. This thesis aims to bridge these existing gaps by presenting in Chapter 3 a framework designed to communicate AI-generated explanations effectively to end-users. This is particularly pertinent in fields like healthcare, where the successful implementation of AI decision support hinges on the ability to convey actionable insights to medical professionals. Building upon this framework, subsequent chapters illustrate how visualization and visual analytics can be used with XAI in the context of clinical decision support. Chapter 4 introduces a visual analytic tool designed for ranking and triaging patients in the intensive care unit (ICU). Leveraging various XAI methods, the tool enables healthcare professionals to understand how the ranking model functions and how individual patients are prioritized. Through interactivity, users can explore influencing factors, evaluate alternate scenarios, and make informed decisions for optimal patient care. The pivotal role of transparency and comprehensibility within machine learning models is explored in Chapter 5. Leveraging the power of explainable AI techniques and visualization, it investigates the factors contributing to model performance and errors. Furthermore, it investigates scenarios in which the model outperforms, ultimately fostering user trust by shedding light on the model\u27s strengths and capabilities. Recognizing the ethical concerns associated with predictive models in health, Chapter 6 addresses potential bias and discrimination in ranking systems. By using the proposed visual analytic tool, users can assess the fairness and equity of the system, promoting equal treatment. This research emphasizes the need for unbiased decision-making in healthcare. Having developed the framework and illustrated ways of combining XAI with visual analytics in the service of clinical decision support, the thesis concludes by identifying important future directions of research in this area

Evidence-informed discharge planning model for stroke rehabilitation

Stroke is a leading cause of long-term disability (Benjamin et al., 2017) and patients with this diagnosis have been found to have higher incidences of inappropriately long hospital lengths of stay (McDonagh, Smith, & Goddard, 2000). Generalist training in occupational therapy curriculum coupled with variable research utilization (Dysart & Tomlin, 2002; McKenna et al., 2005) leads to inconsistent methods of evaluation and decreased communication between providers across settings. Furthermore, there are currently no standardized discharge planning models or guidelines for clinicians to follow when evaluating patients or making recommendations (Ilett, Brock, Graven, & Cotton, 2010). An evidence-informed discharge planning model was created to address these issues. This model utilizes a multidisciplinary approach, with guidelines for selecting and administering evaluations to quantify a patient’s functional status. Assessments are clustered into four domains: activities of daily living, balance and mobility, cognition, and other (i.e. visual inattention, motor control and spasticity). These assessments supplement a basic patient evaluation, and results are used to guide clinical decision making regarding recommendations for the next level of care. Stroke rehabilitation and care cannot be standardized, but the methods used to select measures and make discharge recommendations should have distinct guidelines. By choosing from a core set of measures, clinicians can use a common “language” to describe patient function and measure progress across settings over time. This will ensure patients are discharged to the appropriate level of rehabilitation to optimize their recovery, and it will also help prevent excessively long hospital admissions

A Sustainable Future In The Implementation Of Clinical Pharmacogenomics

Purpose: The sustainability of clinical pharmacogenomics requires further study of clinical education on the topic, its effects on clinical workflow, and the responsibilities of different providers for its delivery. Tools from the discipline of implementation science were utilized herein to help achieve the purposes of the three studies. The broad purpose of this dissertation is to advance the work of clinical pharmacogenomic implementation through a more rigorous convergence with implementation science. Methods: Three studies constitute the whole of this dissertation. The first is a scoping review that provides a broad characterization of the methods utilized in available peer-revieliterature focusing on provider use of and experience with using pharmacogenomics in practice or the study setting. The second study used semi-structured in-depth interviews to elicit strategies and perspectives from leadership in current implementation programs using the Consolidated Framework for Implementation Science (CFIR) Process Domain. The third used a cross-sectional quantitative survey with experimental vignettes to explore the potential for pharmacist-physician collaboration using newly developed implementation science outcomes. Results: The scoping review included 25 studies, with many focused on the interactions of providers with clinical decision support systems and adherence to therapeutic recommendations represented. Results from the interviews were extensive but several highlights included a focus on understanding pharmacogenomic use prior to implementation, high-touch informal communication with providers, and the power of the patient case. The survey analysis revealed that the primary care physicians believe that it is more appropriate to deliver clinical pharmacogenomics when a pharmacist is physically located in a clinic and is responsible for managing and modifying a drug therapy based on these results. Conclusion: These three studies further the convergence of implementation science and genomic medicine, with particular focus on pharmacogenomics and the foundational concept of implementation science, sustainability. The scoping review should provide future researchers with a landscape of available and previously used methodologies for interventional pharmacogenomic studies. The interview results will help new implementers of pharmacogenomics steer around avoidable hurdles or make them easier to address. The survey results showcase the potential for pharmacist-physician collaboration in clinical pharmacogenomics

A qualitative study and thematic analysis concerning the applicability and efficacy of service design processes applied to healthcare service innovation

The emergence of design and systems development have arisen as legitimate tools for facilitating innovative practices in the delivery of care within public services. Development mechanisms deriving from design led thinking and their associated methodologies have evidently been adopted to construct services that fulfill the interest of public health outcomes. Moreover, the innovations are being generated in response to multi-faceted factors necessitating the need to generate new solutions to improve health outcomes. These factors range from changing, and increasingly complex health needs of the current local population; to advances in medical science; and of the development trends emerging in medical technologies further enhancing potential in health outcomes. The research is conducted to answer key questions regarding both relevance and effective impact, surrounding the adoption of design-led thinking and processes in developing health services within the National Healthcare Service in Wales. The research focus and scope of this investigation encompasses the development processes that are integral to creating innovative services. These processes consist of a combination of tools and principles used by stakeholders with the aim of improving the delivery of health related outcomes. The research has sought to address its core questions by implementing a thematic interpretive analysis, to qualitatively extrapolate how live engagement with design processes among relevant stakeholders facilitate innovative interventions. The relevance and effectiveness by which design processes seek to generate ideas against a set criterion is therefore sought through the interpreted narrative of transcribed data from participant stakeholders. From the analytical narrative, the research aims to establish an original framework that would help stakeholders make sense of the developmental means by which innovative services emerge and can concurrently be evaluated for their appositeness to a design exercise

Evaluating mobile health applications as digital therapeutical products

The emergence of new technological advancements and the unprecedented expansion of mobile phone usage has led to the exponential growth of Mobile Health Applications (mHealth apps) development and implementation in the global markets. mHealth apps have created innovative channels to diagnose, treat, monitor, and engage with patients in various healthcare settings, and therefore, it is an important exploration in the fields of information technology, healthcare, and cognitive behavioural sciences. However, a significant portion of mHealth apps has been identified to be developed without scientific or clinical evidence. The objective of implementing the proposed “mHealth App Evaluation Tool” and its validation of the perceived usefulness of the tool from clinicians, mHealth app developers and end-users is to provide a solution for addressing the current gap in evaluating the efficacy of unregulated mHealth apps. An extensive review of the literature from 2010 to 2022 was conducted in three separate phases, gathering and synthesising the core concepts of the mHealth app landscape, proposed frameworks and parameters, the evolution and construction of unidimensional and multidimensional scales and the use of multi-stakeholder participation for a holistic evaluation process. The proposed mHealth app evaluation tool was developed on the foundation of six design drivers: modifiability, scalability, multi-stakeholder involvement, simultaneous management of multiple evaluation projects, ease of use and accessibility. The development of the tool utilised the RestFul API pattern, leveraging Laravel PHP and Vue.js frameworks. The data collection process was completed in two separate phases. The first phase involved the data obtained from the participant’s evaluation of the WYSA app using the proposed mHealth App Evaluation Tool. The system auto-generated an associated average score out of 5 against each evaluation. The second phase involved the data collection during the 30 minutes interview session. Due to the ever-changing nature of software applications, it is inevitable that the elements of mHealth app evaluation will continue to evolve and change over time. What is deemed to be necessary and critical in evaluating mHealth apps today may not be so in years to come. The mHealth App Evaluation tool addresses the need for future criteria modifications, scalability, and the necessity to obtain expert knowledge from multiple stakeholders for a holistic mHealth app evaluation