Using Shared Decision-Making Tools and Patient-Clinician Conversations About Costs
Bibliographic record
Abstract
Objective To 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 Methods A 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. Results Most 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. Conclusion Using 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.
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 | 0.044 | 0.260 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".