Evidence map›Paper›PMID 42139718›Full record

ArticleJMIR AI2026

A Language Model for Pediatric Occupational Therapy Documentation: Model Development and Pilot Study.

Rachel DiMaio, Tia Tuinstra, Trevor Yu, Ilona Koshy, Brendan Wylie-Toal, Bryan Tripp

Abstract read
In one paragraph

Article in JMIR AI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Rachel DiMaioDepartment of Systems Design Engineering, Faculty of Engineering, University of Waterloo, 200 University Ave W, Waterloo, N2L 3G1, Canada, 1 (519) 888-4567.ORCID http://orcid.org/0009-0002-2658-4932
Tia TuinstraDepartment of Systems Design Engineering, Faculty of Engineering, University of Waterloo, 200 University Ave W, Waterloo, N2L 3G1, Canada, 1 (519) 888-4567.ORCID http://orcid.org/0009-0000-3241-859X
Trevor YuDepartment of Systems Design Engineering, Faculty of Engineering, University of Waterloo, 200 University Ave W, Waterloo, N2L 3G1, Canada, 1 (519) 888-4567.ORCID http://orcid.org/0009-0005-5838-614X
Ilona KoshyKidsAbility Centre for Child Development, Waterloo, ON, Canada.ORCID http://orcid.org/0009-0005-1113-0884
Brendan Wylie-ToalSchool of Environment, Enterprise and Development, Faculty of the Environment, University of Waterloo, Waterloo, ON, Canada.ORCID http://orcid.org/0009-0004-0486-9798
Bryan TrippDepartment of Systems Design Engineering, Faculty of Engineering, University of Waterloo, 200 University Ave W, Waterloo, N2L 3G1, Canada, 1 (519) 888-4567.ORCID http://orcid.org/0000-0002-2150-297X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: In occupational therapy, progress notes and other client-related administrative tasks are essential for providing treatment but are time-consuming. Therapists spend at least as much time on these tasks as providing care, which contributes to growing waitlists. Objective: This study aimed to create a custom large language model to make the process of writing progress notes more efficient by converting point-form scratch notes from pediatric occupational therapy treatment sessions into draft documentation in subjective-objective-assessment-plan format. Methods: Using a dataset of redacted historical progress notes, various training methods, including domain-adaptive pretraining and low-rank adaptation fine-tuning, were applied to train Llama 2 and 3 models. Since the historical notes lacked corresponding scratch notes, few-shot prompting with human-in-the-loop evaluations was used to generate synthetic scratch notes. This pairing of historical notes and generated scratch notes enabled effective fine-tuning of the Llama models on the desired task. The final model, a fine-tuned Llama 3 8B Instruct model, was piloted in a pediatric rehabilitation center and compared with Microsoft Copilot. Ten therapists used both models for 3 weeks each. Results: The custom model notes scored higher than manually written notes on clarity, completeness, relevance, and organization (P<.001) , and similarly on conciseness. They scored higher than those from Copilot on conciseness (P<.001). However, in this small pilot, a significant reduction in time spent on documentation when using the custom model versus manual notes was not detected. Follow-up investigation revealed that time savings were observed only when therapists were coached to write sparse scratch notes; however, they tended to revert to detailed notes after coaching for which the model was not shown to improve efficiency. Conclusions: The model had the capacity to save time when therapists provided brief input to the model. However, in practice, therapists preferred to provide detailed input. Used in this way, the model improved note quality rather than saving time.

Indexed as

documentationelectronic medical recordslarge language modelsmodel developmentnatural language processingoccupational therapypilot studySOAP notes

Identifiers

PMID42139718
PMCPMC13179052

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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.