Supporting clinicians in the decision of including or excluding PGHD for the clinical decision process.

Abstract

The integration of Patient-Generated Health Data (PGHD) into clinical decision-making holds immense potential for enhancing personalized healthcare. However, trust remains a critical barrier to its widespread adoption by clinicians. This thesis addresses the fundamental question of how clinicians can be assisted in deciding whether to trust or distrust PGHD in clinical workflows. Through a multi-phased approach, which includes a comprehensive literature review, interviews with clinicians, and a co-development process, this research investigates the factors that influence clinician trust in PGHD and presents a solution in the form of a decision-support tool: the PGHD Trust Canvas. The study begins by reviewing the state of the art on clinician trust in PGHD, examining the complexities of PGHD data, such as its variability in quality and provenance, and the inherent challenges clinicians face when interpreting this data. The review highlights the lack of standardized frameworks for evaluating PGHD, which contributes to clinician skepticism about its reliability and actionability. Factors such as governance, data quality, and the potential risks associated with clinical decision-making based on PGHD are analyzed in-depth, leading to the identification of key issues that need to be addressed. Through a series of qualitative interviews with clinicians, this research identifies the core factors that shape clinician trust in PGHD. These include the source and quality of the data, the context in which the data is used, and the level of risk involved in clinical decisions. The findings reveal that while PGHD can provide valuable insights, its integration into clinical workflows is hindered by concerns over governance, liability, and the lack of clear standards for data validation. Clinicians are particularly wary of relying on PGHD in high-risk clinical scenarios due to the variability and potential inaccuracies of the data. In response to these challenges, the PGHD Trust Canvas was developed in close collaboration with clinicians. This decision-support tool provides a structured framework that guides healthcare professionals through the process of evaluating PGHD across multiple dimensions, including data quality, provenance, risk, and governance. Inspired by models such as the Business Model Canvas, the Ethics Canvas, and the Trust Canvas, the PGHD Trust Canvas offers a practical solution to help clinicians make informed decisions about whether to trust or distrust PGHD in their clinical practice. The validation of the PGHD Trust Canvas was conducted through qualitative interviews with a new group of clinicians, who evaluated its usability and usefulness in clinical settings. The results of this validation study demonstrate that the tool is particularly valuable in non-urgent care settings, such as nursing homes, where clinicians have more time to assess data. In these environments, the PGHD Trust Canvas provides clarity and confidence in decision-making, helping to improve patient outcomes by reducing the risk of relying on untrustworthy data. However, the tool's effectiveness is limited in high-pressure environments, where quick decision-making is required. The research also highlights the tool’s potential as an educational resource, supporting the training of clinicians in the use of PGHD. This thesis describes a significant contribution to the field of digital health by providing a structured, practical tool to assist clinicians in the decision-making process regarding PGHD. The PGHD Trust Canvas addresses the critical trust issues that currently hinder the integration of PGHD into clinical practice and offers a solution that can improve patient safety and clinical outcomes. Additionally, the study provides valuable insights into the governance, policy, and training implications of PGHD, paving the way for future research and the development of standardized frameworks for its use in healthcare.