Impact of Cost Conversations During Clinical Encounters Aided by Shared Decision-Making Tools on Medication Adherence
Bibliographic record
Abstract
Objective To investigate the impact of cost conversations occurring with or without the use of encounter shared decision-making (SDM) tools in medication adherence. Patients and Methods Using a coding scheme that included the occurrence and characteristics of cost conversation, we analyzed a randomly selected sample of 169 video recordings of clinical encounters. These videos were obtained during the conduct of practice-based randomized clinical trials comparing care with and without SDM tools for patients with diabetes, osteoporosis, and depression. Medication adherence was described in 2 ways: as a binary (yes/no) outcome, in which the patient met at least 80% adherence, or as a continuous variable, which was the percent of days that the patient adhered to their medication. The secondary analysis took place in 2018 from trials that ran between 2007 and 2015. Results Most patients were White (155, 93.4%), educated (104, 63.4% completed college), middle-aged (mean age, 58 years), female (104, 61.5%), and from diabetes (86, 50.9%), depression (43, 25.4%), and osteoporosis (40, 23.7%) trials. Cost conversations occurred in 119 clinical encounters (70%) and were more frequent in those encounters in which SDM tools were used ( P =.03). Furthermore, 97 (57.4%) of the participants reported more than 80% medication adherence and 70.3±29.34 percentage of days with adherent medication of 70 days. In the multiple regression model, the only factor associated with adherence (binary or continuous) was the condition of the trial in which people participated. For the participants who had cost conversations, the use of an SDM tool, their sex, the nature of cost conversation (direct or indirect), the nature of cost concerns (treatment or patient issue), and the clinician-offered strategies (yes or no) were not associated with adherence. Conclusion In this videographic analysis of SDM practice-based clinical trials, cost conversations were not associated with the general measures of medication adherence. Future studies should assess whether a tailored cost conversation intervention would impact the cost-related nonadherence among patients.
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 | 0.006 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".