Implementing combined decision models in healthcare settings: the Simon and Pauker- Kassirer models
Bibliographic record
Abstract
Background: Modeling medical decision-making has attracted considerable attention over the years, and has become the topic of many investigations. Researchers have attempted to model this critical and extremely complex process from several different angles to enable hospital clinicians to engage in decision-making using empirical tools. Purpose: This paper takes a famous managerial model of decision-making in a non-medical setting and integrates it with a well- known model of medical decision-making to generate a unified illustration of the process. Both models deal with decision-making. However, Simon’s model is less easily applied to the unique process of medical decision making. The proposed integration may help bridge the gap between the models and approaches by creating a unified framework to deal with the challenge of medical decision making in hospital environments through empirical methods. Approach: Simon’s model of automation provides the general structure of the decision-making process by dividing it into three stages: Intelligence, Design and Choice. The Pauker & Kassirer model deals with probabilistic and statistical applications of clinical processes, and introduces a threshold approach and decision trees as the main decision tools. The discussion explores the advantages and disadvantages of each model and what can be gained by combining them. Research limitations: Although these models were used to form an integrated framework, they were developed almost three decades apart. Therefore, caution is of the essence when applying them to real-life circumstances, and further research is needed to validate this integration.
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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 | 0.023 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".