Considering Cocreation for the Choosing Wisely List
Bibliographic record
Abstract
To the Editor: In their August 2016 article, Lakhani et al1 created an innovative Choosing Wisely list to improve awareness and empowerment among medical students in reducing overuse. This project was expansive and engaged students from all 17 medical schools across Canada. As broad and inclusive as this undertaking was, the list was developed without input from the primary end user—the patient. The American Board of Internal Medicine (ABIM) Foundation intended Choosing Wisely to promote conversations between clinicians and patients about unnecessary care. These lists were developed by clinician societies with messaging to patients facilitated by media outlets and Consumer Reports. These were successful in spreading the message, but the architects of Choosing Wisely have recently shifted focus from awareness to implementation, understanding that systems change, and not knowledge alone, is needed to improve outcomes. Health care is a service that is cocreated between providers and patients.2 Far from a product developed from a manufacturer for a consumer, health care service is a complex system with great potential for patients to improve value at every stage, including design, production, delivery, and evaluation. Instead of involving patients after the Choosing Wisely list is finalized, we propose involving patients from its inception. Two significant drivers of overuse perceived by clinicians include fear of litigation and patient demand.3 These barriers could be lowered if health care standards were developed in partnership with patients. Imagine a process in which brainstorming recommendations for the Choosing Wisely list included what matters most to patients, as told by the patients. Perhaps what matters to patients in reducing unnecessary care is discussion about cost, or potential harms of testing and treatment. A Delphi process could facilitate equal say among clinicians and patients in this collaborative process. Cocreation could revitalize Choosing Wisely list development and improve implementation in the years to come. Irene Timothy LeeMedical student, Icahn School of Medicine at Mount Sinai, New York, New York; [email protected] John Di Capua, MHSMedical student, Icahn School of Medicine at Mount Sinai, New York, New York. Hyung J. Cho, MDDirector of quality and patient safety and assistant professor of medicine, Icahn School of Medicine at Mount Sinai, New York, New York.
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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 | 0.004 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.018 | 0.026 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".