Simulated Conversations With Virtual Humans to Improve Patient-Provider Communication and Reduce Unnecessary Prescriptions for Antibiotics: A Repeated Measure Pilot Study
Bibliographic record
Abstract
Background: Despite clear evidence that antibiotics do not cure viral infections, the problem of unnecessary prescribing of antibiotics in ambulatory care persists, and in some cases, prescribing patterns have increased. The overuse of antibiotics for treating viral infections has created numerous economic and clinical consequences including increased medical costs due to unnecessary hospitalizations, antibiotic resistance, disruption of gut bacteria, and obesity. Recent research has underscored the importance of collaborative patient-provider communication as a means to reduce the high rates of unnecessary prescriptions for antibiotics. However, most patients and providers do not feel prepared to engage in such challenging conversations. Objectives: The aim of this pilot study was to assess the ability of a brief 15-min simulated role-play conversation with virtual humans to serve as a preliminary step to help health care providers and patients practice, and learn how to engage in effective conversations about antibiotics overuse. Methods: A total of 69 participants (35 providers and 34 patients) completed the simulation once in one sitting. A pre-post repeated measures design was used to assess changes in patients’ and providers’ self-reported communication behaviors, activation, and preparedness, intention, and confidence to effectively communicate in the patient-provider encounter. Changes in patients’ knowledge and beliefs regarding antibiotic use were also evaluated. Results: Patients experienced a short-term positive improvement in beliefs about appropriate antibiotic use for infection (F1,30=14.10, P=.001). Knowledge scores regarding the correct uses of antibiotics improved immediately postsimulation, but decreased at the 1-month follow-up (F1,30=31.16, P.10) Patients with lower levels of activation exhibited positive, short-term benefits in increased intent and confidence to discuss their needs and ask questions in the clinic visit, positive attitudes regarding participation in SDM with their provider, and accurate beliefs about the use of antibiotics (P<.10). The results also suggest small immediate gains in providers’ attitudes about SDM (mean change 0.20; F1,33= 8.03, P=.01). Conclusions: This pilot study provided preliminary evidence on the efficacy of the use of simulated conversations with virtual humans as a tool to improve patient-provider communication (ie, through increasing patient confidence to actively participate in the visit and physician attitudes about SDM) for engaging in conversations about antibiotic use. Future research should explore if repeated opportunities to use the 15-min simulation as well as providing users with several different conversations to practice with would result in sustained improvements in antibiotics beliefs and knowledge and communication behaviors over time. The results of this pilot study offered several opportunities to improve on the simulation in order to bolster communication skills and knowledge retention.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".