Context: Rectal cancer treatment decisions involve tradeoffs between outcomes like living with a permanent stoma versus long-term bowel dysfunction. The needs of rectal cancer patients and practitioners to partake in shared decision making are unknown. For such a complex decision, a patient decision aid that prepares patients to make informed, values-based decisions is warranted. Methods: 1) A systematic review, to characterize the prevalence of long-term dysfunction 2) Needs assessments, conducted with rectal cancer patients and practitioners, 3) Development of a decision aid. Results: 1) Significant variability exists in reporting rectal cancer outcomes. The rate of bowel dysfunction is high. 2) Rectal cancer patients recall little of the outcomes discussed preoperatively. They do not perceive having any surgical options. Practitioners are inconsistently engaging patients in shared decision-making. 3) A patient decision aid was developed that a) incorporated systematic review results and; b) addressed the needs, barriers and facilitators raised. Conclusions: Shared decision-making in rectal cancer surgery is limited. A decision aid to improve patient decision-making was developed.

Timely and appropriate clinical decisions can be lifesaving, and decision support systems could help facilitate this. However, user adoption of clinical decision support systems (CDSS) and their impact on patient care have been disappointing. Contemporary theories in information systems and several evaluation studies have failed to explain or predict the adoption of CDSS. To find out why I conducted a qualitative inquiry using the constructivist grounded theory method. Guided by the theory of planned behaviour, I designed a functional clinical decision support system called DermML. Then, I used it as a stimulus to elicit responses through semi-structured interviews with doctors, a community to which I also belong. Besides the interview data, I also collected demographic data from the participants and anonymous clickstream data from DermML. I found that the clinical community is diverse, and their knowledge needs are varied yet predictable. Using theoretical sampling, constant comparison and iterative conceptualization, I scaled my findings to a substantive theory that explains the difference in practitioners' knowledge needs and predicts adoption based on CDSS type and use context. Having designed DermML myself, the data provided me with design insights that I have articulated as prescriptive design theory. I posit that GT can generate explanatory and predictive theories and prescriptive design theories to guide action. This study eliminates the boundaries between the developers of CDSS, study participants, future users and knowledge mobilization partners. I hope the rich data I collected and the insights I derived help improve the adoption of CDSS and save lives. / Thesis / Doctor of Philosophy (PhD)

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Intelligent decision systems have the potential to support and greatly amplify human decision-making across a number of industries and domains. However, despite the rapid improvement in the underlying capabilities of these “intelligent” systems, increasing their acceptance as decision aids in industry has remained a formidable challenge. If intelligent systems are to be successful, and their full impact on decision-making performance realized, a greater understanding of the factors that influence recommendation acceptance from intelligent machines is needed. Through an empirical experiment in the financial services industry, this study investigated the effects of perceived behavioral similarity (similarity state) on the dependent variables of recommendation acceptance, decision performance and decision efficiency under varying conditions of uncertainty (volatility state). It is hypothesized in this study that behavioral similarity as a design element will positively influence the acceptance rate of machine recommendations by human users. The level of uncertainty in the decision context is expected to moderate this relationship. In addition, an increase in recommendation acceptance should positively influence both decision performance and decision efficiency. The quantitative exploration of behavioral similarity as a design element revealed a number of key findings. Most importantly, behavioral similarity was found to positively influence the acceptance rate of machine recommendations. However, uncertainty did not moderate the level of recommendation acceptance as expected. The experiment also revealed that behavioral similarity positively influenced decision performance during periods of elevated uncertainty. This relationship was moderated based on the level of uncertainty in the decision context. The investigation of decision efficiency also revealed a statistically significant result. However, the results for decision efficiency were in the opposite direction of the hypothesized relationship. Interestingly, decisions made with the behaviorally similar decision aid were less efficient, based on length of time to make a decision, compared to decisions made with the low-similarity decision aid. The results of decision efficiency were stable across both levels of uncertainty in the decision context.

Intelligent decision systems have the potential to support and greatly amplify human decision-making across a number of industries and domains. However, despite the rapid improvement in the underlying capabilities of these “intelligent” systems, increasing their acceptance as decision aids in industry has remained a formidable challenge. If intelligent systems are to be successful, and their full impact on decision-making performance realized, a greater understanding of the factors that influence recommendation acceptance from intelligent machines is needed. Through an empirical experiment in the financial services industry, this study investigated the effects of perceived behavioral similarity (similarity state) on the dependent variables of recommendation acceptance, decision performance and decision efficiency under varying conditions of uncertainty (volatility state). It is hypothesized in this study that behavioral similarity as a design element will positively influence the acceptance rate of machine recommendations by human users. The level of uncertainty in the decision context is expected to moderate this relationship. In addition, an increase in recommendation acceptance should positively influence both decision performance and decision efficiency. The quantitative exploration of behavioral similarity as a design element revealed a number of key findings. Most importantly, behavioral similarity was found to positively influence the acceptance rate of machine recommendations. However, uncertainty did not moderate the level of recommendation acceptance as expected. The experiment also revealed that behavioral similarity positively influenced decision performance during periods of elevated uncertainty. This relationship was moderated based on the level of uncertainty in the decision context. The investigation of decision efficiency also revealed a statistically significant result. However, the results for decision efficiency were in the opposite direction of the hypothesized relationship. Interestingly, decisions made with the behaviorally similar decision aid were less efficient, based on length of time to make a decision, compared to decisions made with the low-similarity decision aid. The results of decision efficiency were stable across both levels of uncertainty in the decision context.

Thesis (M. Sc.)--University of Alberta, 1997. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

Thesis (M. Sc.)--University of Alberta, 1997. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

published_or_final_version / Industrial and Manufacturing Systems Engineering / Master / Master of Philosophy

No description available.