Patient-level factors influencing adherence to follow-up imaging recommendations

Calvillo, Andrés Ángel-González; Kodaverdian, Laura Caroline; García, Roxana; Lichtensztajn, Daphne Y.; Bucknor, Matthew

doi:10.1016/j.clinimag.2022.07.006

Cited by 2 publications

(1 citation statement)

References 41 publications

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“…It has been shown that only 50% of recommended follow up is performed [4]. While some work has been done to study the effect of patient-level factors on adherence to follow up recommendation [5], negligible research effort has been directed towards making radiology reports readable to patients by taking patient-level factors into account.…”

Section: Introductionmentioning

confidence: 99%

Patient Centric Summarization of Radiology Findings using Large Language Models

Tariq,

Fathizadeh,

Ramaswamy

et al. 2024

Preprint

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Objective. Develop automated AI models for patient-sensitive summarization of radiology reports. Level of medical education or socio-economic background of a patient may dictate their level of understanding of medical jargon. Inability to understand primary findings from a radiology report may lead to unnecessary anxiety among patients or result in missed follow up. Materials and Methods. Computed tomography exams of chest were selected as a use-case for this study. Approximately 7K chest CT reports were collected from Mayo Clinic Enterprise. Summarization model was built on the T5 large language model (LLM) as its text-to-text transfer architecture is intuitively suited for abstractive text summarization, resulting in a model size of ~0.77B. Noisy groundtruth for model training was collected by prompting LLaMA 13B model. Results. We recruited both experts (board-certified radiologists) and laymen to manually evaluate summaries generated by model. Model-generated summaries rarely missed information as marked by majority opinion of radiologists. Laymen indicated 63% improvement in their understanding by reading layman summaries generated by the model. Comparative study with zero-shot performance of LLaMA indicated that LLaMA hallucinated and missed information 3 and 4 times more often, respectively, than the proposed model. Discussion. The proposed patient-sensitive summarization model can generate summaries for radiology reports understandable by patients with vastly different levels of medical knowledge. In addition, task-specific training allows for more reliable performance compared to much larger off-the-shelf models. Conclusions. The proposed model could improve adherence to follow up treatment suggested by radiology reports by increasing level of understanding of these reports for patients.

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Section: Introductionmentioning

confidence: 99%