THE ECOLOGY OF CLINICAL DECISION MAKING: PHYSICIANS’ PERCEPTIONS OF FACTORS THAT INFLUENCE CLINICAL PRACTICE DECISIONS AND IMPLICATIONS FOR PROVIDING HIGH-VALUE CARE
Bibliographic record
Abstract
Despite substantial healthcare costs, patient outcomes are sub-optimal in the United States and Canada compared to other countries that spend proportionally less on healthcare. This has led to recognition of the need to improve healthcare value, utilization of tools including clinical practice guidelines and development of initiatives such as the Choosing Wisely Campaign to achieve this goal. In spite of the intuitive appeal of these interventions designed to increase physician awareness of evidence and empower patients to engage in shared decision-making, they have had limited success in changing practice and physician prescribing behaviours. Using a mixed-methods approach, this thesis represents a purposeful attempt to understand the failure of existing approaches through an examination of the factors that influence clinical decision making. Specifically, the thesis integrates quantitative and qualitative methodologies to develop a deeper understanding of clinical decision-making. Consisting of a clinical vignette based survey, the quantitative study explores decision-making in four specific commonly encountered case contexts. After choosing the preferred management option, participants rated the influence of different factors on their decisions. Follow-up questions explored knowledge, attitudes and practices regarding incorporating cost considerations into decision-making. The results of the study were explored further in the qualitative component of the mixed study. The results indicate that clinical decision-making is influenced by an interrelated set of socioecological factors with evidence and clinical practice guidelines playing a secondary role. Because lack of knowledge is not a major factor in guideline discordant care, strategies to improve knowledge will have minimal effect in improving care. The qualitative study included an inquiry into the need for teaching and learning on the topic of cost and cost-effectiveness and sought input from physicians working in diverse settings on methods and topics that need to be included in medical education. The contributions of this thesis include a deeper understanding of the factors that influence clinical decision-making and suggestions for enhancing medical education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".