Using Shared Decision-Making Tools and Patient-Clinician Conversations About Costs
Bibliographic record
Abstract
ObjectiveTo determine how shared decision-making (SDM) tools used during clinical encounters that raise cost as an issue impact the incidence of cost conversations between patients and clinicians.Patients and MethodsA randomly selected set of 220 video recordings of clinical encounters were analyzed. Videos were obtained from eight practice-based randomized clinical trials and one quasi-randomized clinical trial (pre- and post-) comparing care with and without SDM tools. The secondary analysis took place in 2018 from trials ran between 2007 and 2015.ResultsMost patient participants were white (85%), educated (38% completed college), middle-aged (mean age 56 years), and female (61%). There were 105 encounters with and 115 without the SDM tool. Encounters with SDM tools were more likely to include both general cost conversations (62% vs 36%, odds ratio [OR]: 9.6; 95% CI: 4 to 26) as well as conversations on medication costs specifically (89% vs 51%, P=.01). However, clinicians using SDM tools were less likely to address cost issues during the encounter (37% vs 51%, P=.04). Encounters with patients with less than a college degree were also associated with a higher incidence of cost conversations.ConclusionUsing SDM tools that raise cost as an issue increased the occurrence of cost conversations but was less likely to address cost issues or offer potential solutions to patients’ cost concerns. This result suggests that SDM tools used during the consultation can trigger cost conversations but are insufficient to support them.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".