Recall Boost: qualitative analysis of clinical consultations

Perceived clinician validation predicts measurable short-term recall benefits: participants who described a validating consultation had higher odds of recalling brief audio health messages.

• A July 20, 2026 PLOS ONE proof-of-concept experiment (Lee et al.) found ~18.5% higher odds of recalling each of 20 short audio health messages after describing a validating versus an invalidating consultation.
• The study analysed a final sample of n = 245 adults with chronic pain, with data collected Oct 8–30, 2024 and the paper published in PLOS ONE.
• Automated scoring plus human QA worked at scale: a rule-based keyword matcher scored recall with 97.53% agreement versus manual checks, and 12 suspected AI-generated descriptions were removed during cleaning.

The methods used an autobiographical recall writing task, immediate audio exposure, and an incidental free-recall test to measure downstream memory for short health messages.

The team recruited participants via Prolific and asked them to write about a prior validating or invalidating healthcare consultation, then immediately played 20 short audio health tips and later ran an incidental free-recall test.

Recall was scored per item (1 or 0) using a rule-based keyword algorithm implemented in R, and a 10% random subset was manually double-checked producing 97.53% agreement before full scoring.

The primary inferential model was a binomial generalized linear mixed-effects model (GLMM) with random intercepts for participant and message, and the final model kept condition and pain duration as predictors.

Teams doing qualitative analysis should note the replicable pipeline used here: autobiographical prompts, audio stimuli, automated scoring plus manual validation, and mixed-effects modelling to handle nested item-level responses.

This checklist lists practical steps to reproduce the study using an AI-enabled qualitative workflow.

• 1) Collect audio of consultations and participant autobiographical prompts, store data securely.
• 2) Transcribe audio with a medical or custom dictionary and run PII redaction.
• 3) Preprocess free text (lowercase, punctuation removal) and build a keyword scaffold or classifier for the 20 target messages.
• 4) Auto-score recall with a rule-based matcher and validate on a random 10% human-coded subset, iterate until agreement exceeds 95%.
• 5) Tag each consultation for perceived validation or invalidation using a mixed AI and human codebook workflow.
• 6) Export item-level data with one row per message per participant and fit binomial GLMMs to estimate odds ratios while accounting for participant and message random effects.
• 7) Visualise code co-occurrence, segment differences, and quote-level evidence for reports or training materials.

The PLOS ONE study shows perceived clinician validation predicts better short-term recall, about a 19% increase in odds per message.

For teams running qualitative analysis of clinical consultations, combining robust QA, item-level modelling, and multimodal ingestion is the fastest path from data to decision.

Ready to operationalise this pipeline? Try Evidano for free.

If you want a reproducible starter, import your transcripts and run a 2-week pilot using the checklist above; Evidano speeds coding, validation, and export so mixed models and stakeholder reports are ready sooner.