Evidence map›Paper›PMID 41776696›Full record

ArticleAdvances in simulation (London, England)2026

Breaking bad news with reflective AI virtual experience (BRAVE): development and evaluation of a generative AI-based simulation tool.

Orit Karnieli-Miller, Zohar Elyoseph

Abstract read
In one paragraph

Article in Advances in simulation (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Orit Karnieli-MillerDepartment of Medical Education and Dr. Sol Amsterdam and Dr. David P. Schumann Endowed Chair in Medical Education, Gray Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, 69978, Israel. oritkm@tauex.tau.ac.il.
Zohar ElyosephSchool of Therapy, Counseling, and Human Development, Faculty of Education, University of Haifa, Haifa, Israel.

Funding

Ministry of Innovation, Science and Technology #0008610
6 · The paper itself

Abstract

backgroundBreaking bad news (BBN) to patients is complex and emotionally and cognitively demanding. The way bad news is delivered has implications for patients, their significant others, and healthcare professionals. Despite the development of structured BBN protocols, many health professionals report insufficient training and low self-efficacy for conducting these conversations. While best-practice BBN education exist, opportunities for repeated practice with high-quality feedback are limited, representing a critical barrier to scalability and global health equity, particularly in resource-constrained settings. We developed BRAVE (Breaking Bad News with AI Reflective Virtual Experience), a generative AI-based simulator that includes a written BBN encounter with a virtual patient. Unlike prior AI simulators that primarily offered structured summary feedback, BRAVE explicitly combines a prompted, dialogic reflective debrief followed by personalized feedback mapped to the SPwICES protocol.

methodsA prospective, single arm, pre-post design, which incorporates both quantitative and qualitative measures. Ninety-nine senior medical students completed one BRAVE session following a required BBN course. Pre/post questionnaires assessed self-efficacy, willingness to engage in BBN, and intention to apply the BBN protocol. User experience was rated on a 5-point scale. Quantitative analysis included paired-sample t-tests and descriptive statistics. Qualitative data from open-ended responses were analyzed using immersion/crystallization.

resultsSelf-efficacy increased from M = 6.95 to M = 7.62 (p<.001), willingness from M = 5.80 to M = 6.51 (p<.001), and intention to apply the protocol from M = 8.20 to M = 8.41 (p=.005). User experience was positive: interaction quality (M = 4.27), structured feedback (M = 4.19), reflective dialogue (M = 3.91), and interest in future use (M = 3.81). Qualitative analysis showed most students described the simulator as realistic, useful, and safe for practice, emphasizing opportunities to think, reread, and make mistakes with reduced embarrassment; a minority noted limits of the written modality (restricted nonverbal cues and occasional recognition errors), which were addressed during refinement.

conclusionsA brief text-based GenAI simulation integrating dialogic reflection with protocol-based feedback yielded improvements in learners’ confidence and willingness to conduct BBN and was well-received. BRAVE can complement existing curricula by offering scalable, psychologically safe, and feedback-rich practice; future controlled studies with performance-based outcomes are warranted.

Indexed as

AI simulatorBreaking bad newsClinical competenceCommunication skills trainingDelivering bad newsDifficult conversationsGenerative AIMedical educationVirtual patient

Identifiers

PMID41776696
PMCPMC13067485

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.