Improving Clinical Pathways for Cancer Diagnosis by Understanding Physicians’ Mental Models
Bibliographic record
Abstract
Context: The Alberta Cancer Strategic Clinical Network (C-SCN), a unit of Alberta Health Services, created province-wide diagnosis and referral pathways for rectal bleeding (RB), iron-deficiency anemia (IDA), and lymphadenopathy (LA). Historically such initiatives have met with limited uptake. Objective: Apply cognitive engineering principles to improve design and implementation. Study Design and Analysis: Cross-sectional Cognitive Task Analysis study, using our previously published framework-guided qualitative analytic approach. Setting: Community primary care in Alberta. Population Studied: 8 community family physicians in Alberta, purposively sampled to exclude early adopters and opinion leaders. Range < 10 to > 30 years in practice; 6 women; none rural. Intervention/Instrument: Cognitive Task Analysis (mental simulation method) and “Think Aloud” protocol. Findings presented to C-SCN Leadership for revision of pathway content and implementation plan. Outcome Measures: Fit of pathways with the mental models and cognitive strategies of family physicians. Suggestions for improving fit. C-SCN actions in response to findings. Results: Physicians do not maintain detailed mental models for LA, and used the pathway for sensemaking. Physicians had well developed mental models for RB and IDA. They did not use the pathways for diagnosis, instead abstracting a few key points for sensemaking in the referral process and validating their decision making. The pathways’ direction for referral for endoscopy did not fit with physicians’ mental models. Suggestions provided to the C-SCN: not to attempt to change physicians’ mental models, but instead change the triage process for incoming referrals. Physicians did not follow any of the pathways as algorithms, but sought key cues from them to use in System 1-based (rapid, intuitive) cognitive strategies. C-SCN was advised to change the format to cluster those key cues and make them easy to find, and to avoid forcing physicians into slower System 2 thinking (potentially disrupting busy clinic workflow). Specific usability feedback was catalogued at the level of wording and format details, for use by the C-SCN pathway designers. The C-SCN team made major changes based on these recommendations. Conclusions: A cognitive engineering approach can provide a perspective on care/diagnostic pathways that results in substantially different design and implementation choices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".