Large Language Model
Conditions
Keywords
feasibility, acceptability, usability
Brief summary
Hospitalization strips pediatric patients of the environments, objects, and people that shape their daily lives. Hospitalized pediatric patients routinely experience painful procedures, psychological distress, boredom, and a disorienting loss of personal identity. These experiences measurably worsen anxiety, reduce cooperation with care, and diminish the quality of the inpatient experience for both patients and families. Immersive digital interventions, including VR and tablet-based experiences, have emerged as a promising class of tools for addressing these challenges. Prior studies from The Stanford Chariot Program have demonstrated that digitally delivered, patient-centered experiences can meaningfully reduce procedural anxiety and improve engagement in hospitalized children. Yet, an important limitation persists in these technologies - current digital interventions largely remain in one-size-fits-all formats. Every child receives the same content, regardless of who they are, what they love, or what makes them feel at home in the world. This design limits therapeutic relevance, constrains engagement, and represents a missed opportunity to engage children, reduce anxiety, and enhance their quality of life during hospital stays.
Interventions
BEHAVIORALLLM-audiobook
Develop and refine a two-stage personalization LLM pipeline: (1) a structured brief interview template and (2) an LLM prompt chain for content generation. The interview instrument will be a structured guide (\~20 minutes) capturing details about the child's background, interests, and aspirations. All information collected during the interview will be deidentified prior to being passed into the LLM. Content Generation: Deidentified interview responses will be input to a standardized LLM prompt pipeline. The pipeline generates a structured patient profile that interprets the participant's interests, personality, and preferences, then uses that profile to produce a personalized narrative, formatted as a picture book story. AI voice synthesis will generate an audio narration of the story. AI image generation tools will produce a set of 6-10 accompanying illustrations. The final product - a synchronized audio picture book - will be delivered via VR headset at bedside.
Sponsors
Stanford University
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
SUPPORTIVE_CARE
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
9 Years to 17 Years
Healthy volunteers
No
Inclusion criteria
* Between age 9-17 * Current admission in inpatient unit * Able to understand and interact with a virtual reality audiobook in English
Exclusion criteria
* Legal guardian not present to obtain consent * Significant cognitive impairment and/or developmental delay * History of seizures * History of severe motion sickness * Severe visual or motor impairment limiting picture book participation * Child with active infection of the face or hand * Acute medical instability * Inability to understand English
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Determine the acceptability of the Large Language Model generated audiobook | Immediately after intervention | Semi-structured focus group interviews conducted in person immediately after intervention. Six core questions plus probing prompts assessing attitudes and opinions, and perceptions of the intervention experience |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| To assess the degree to which participants identified with the protagonist of the personalized audio picture book. | Immediately after intervention | Participants will complete the Cohen Identification Scale following the VR experience. Items are answered using a 5-point Likert scale ranging from 1 = strongly disagree to 5 = strongly agree. Item responses will be averaged to generate a composite score, with higher scores indicating greater identification with the main character. |
| To assess emotional state using Positive and Negative Affect Schedule - Child Form (PANAS-C) | baseline, Immediately after intervention | The PANAS-C measures the degree to which children experience positive and negative affective states, yielding two subscale scores: Positive Affect and Negative Affect. Items are answered using a 5-point Likert scale ranging from 1 = very slightly or not at all to 5 = extremely. Higher Positive Affect (PA) Score indicates a higher level of positive emotions while higher Negative Affect (NA) Score indicates a higher level of negative emotions. |
| To assess subjective wellbeing using the World Health Organization-Five Child Well-Being Index (WHO-5) | baseline, Immediately after intervention | The WHO-5 is a brief, validated self-report measure assessing current mental wellbeing across five positively worded items covering mood, relaxation, energy, rest, and daily interest. The WHO-5 instructs respondents to rate how they have felt "over the last two weeks,"; the scale will be modified in this study to ask the participants to reflect on their current state. Items are answered using a 5-point Likert scale ranging from 0 = never to 5 = all of the time. To calculate the overall wellbeing score, the responses to the five items are summed and the total is multiplied by 5, yielding a final score between 0 and 100. Higher scores reflect higher levels of wellbeing. |
Contacts
CONTACTSamuel Rodriguez, MD
CONTACTManYee Suen, MMedSc
STUDY_DIRECTORSamuel Rodriguez, MD
Stanford University
PRINCIPAL_INVESTIGATORThomas Caruso, MD,PhD
Stanford University
Outcome results
None listed