Design and Evaluation of User-Centered Explanations for Machine Learning Model Predictions in Healthcare

Challenges in interpreting some high-performing models present complications in applying machine learning (ML) techniques to healthcare problems. Recently, there has been rapid growth in research on model interpretability; however, approaches to explaining complex ML models are rarely informed by end-user needs and user evaluations of model interpretability are lacking, especially in healthcare. This makes it challenging to determine what explanation approaches might enable providers to understand model predictions in a comprehensible and useful way. Therefore, I aimed to utilize clinician perspectives to inform the design of explanations for ML-based prediction tools and improve the adoption of these systems in practice. In this dissertation, I proposed a new theoretical framework for designing user-centered explanations for ML-based systems. I then utilized the framework to propose explanation designs for predictions from a pediatric in-hospital mortality risk model. I conducted focus groups with healthcare providers to obtain feedback on the proposed designs, which was used to inform the design of a user-centered explanation. The user-centered explanation was evaluated in a laboratory study to assess its effect on healthcare provider perceptions of the model and decision-making processes. The results demonstrated that the user-centered explanation design improved provider perceptions of utilizing the predictive model in practice, but exhibited no significant effect on provider accuracy, confidence, or efficiency in making decisions. Limitations of the evaluation study design, including a small sample size, may have affected the ability to detect an impact on decision-making. Nonetheless, the predictive model with the user-centered explanation was positively received by healthcare providers, and demonstrated a viable approach to explaining ML model predictions in healthcare. Future work is required to address the limitations of this study and further explore the potential benefits of user-centered explanation designs for predictive models in healthcare. This work contributes a new theoretical framework for user-centered explanation design for ML-based systems that is generalizable outside the domain of healthcare. Moreover, the work provides meaningful insights into the role of model interpretability and explanation in healthcare while advancing the discussion on how to effectively communicate ML model information to healthcare providers

Integrating design literacy within Chinese health promoting hospitals

The World Health Organization (WHO) established the concept of Health Promoting Hospitals (HPH) in 1986, with the main aim to expand the role of hospitals from treatment-centred to health promotion-centred cultures and by doing so, empowering and facilitating deeper community context. Design research and social healthcare have permeated western studies. However, when it comes to design practice in Chinese HPH, research suggests hospital managements still tend to believe design is only a last-minute intervention rather than drawing comprehensively and synthetically from a design research perspective. This is the gap my PhD practice-led research aims to fill, by employing designerly research, Chinese HPH practitioners may access and apply a systematic, comprehensive understanding of what design thinking means for HPH implementation. The research asks: Can design thinking create a supportive, sustained and creative community setting for Chinese HPH? Can the WHO philosophy of HPH and design epistemology be adapted and situated in Chinese hospitals through design research? The practice in this research is reflected in field trips, two case studies and design frameworks. First, the research examines Chinese hospitals in Central and East China, looking at the role of design in HPH and investigating the level of design penetration within that context between 2017 and 2019 using field trips and action research. Second, two case studies were conducted with two focus groups through participatory communication design (PCD) research: (1) participatory action research into a low-literate group targeting medical consumption issues in Hantun village; (2) a participatory dental health promotion course for children aged 4-8 in Wuhan. Both case studies propose a change from “top-down” policymaking to adopting a “bottom-up” strategy; from expert-dominated to participatory and democratic approaches. The core of HPH activities is enabling people – patients, professionals and communities – to design their own experiences, services, tools and artefacts. Finally, the design frameworks offer a pluralistic, situated, nuanced and inclusive process for Chinese HPHs. My main contribution to knowledge is within the Chinese HPH field, developing and proposing comprehensive design-thinking frameworks – designerly ways of knowing, thinking, and doing – increasing accessibility to the inclusivity of design ontology, epistemology and methodology, within Chinese HPH context. A secondary contribution to knowledge is situated in the design fields. It defines PCD through participatory design, communication design and communication theory as a blended theoretical construction, developing novel PCD methods and transitional communication methods as extensive methodology. These design contributions address gaps in current design research

Charge Nurse Expertise: Implications for Decision Support of the Nurse-Patient Assignment Process

University of Minnesota Ph.D. dissertation. 2019. Major: Health Informatics. Advisor: David Pieczkiewicz. 1 computer file (PDF); 201 pages.Each day, across thousands of medical-surgical inpatient nursing units, charge nurses make decisions about which nurse will care for each patient. Recent attempts have been made to introduce health information technology (HIT) solutions to automate the nurse-patient assignment process. This research investigated charge nurse decision making during the nurse-patient assignment process as an exemplar of the larger question: How can we leverage information technology to improve decision making in healthcare, while respecting individual clinician expertise and the unique context of individualized patient care? Four primary questions were used to guide research of the process, decision factors, goals and context of nurse-patient assignments. A mixed-methods approach of qualitative interviews (N = 11) and quantitative surveys (N = 135) was used. Findings related to the charge nurse decision making process indicate that measurable, nurse-sensitive indicators of patient outcomes have not yet been standardized for nurse-patient assignments. HIT solutions and quality improvement efforts should define, collect and analyze measurable outcome criteria prior to attempting to improve or augment existing nurse-patient assignment practices to prevent unintended consequences. When clear outcome measurements have been identified, informatics researchers and professionals should investigate the ability of machine learning to recognize goal priorities and factor weighting from patient, nurse and environmental factors within existing HIT solutions. Until that time, HIT solutions augmenting the nurse-patient assignment process should be designed with flexible configurations, to enable goals, decision factors and factor weights can be varied by hospital, unit, charge nurse and shift, in order to best meet the needs of charge nurses