Streamlining Clinical Trial Recruitment: A Two-Stage Zero-Shot LLM Approach with Advanced Prompting

Mozhgan Saeidi
Proceedings of the 4th Machine Learning for Health Symposium, PMLR 259:886-896, 2025.

Abstract

Identifying patient eligibility for clinical trials is a critical bottleneck hindering medical research progress because many clinical trials allow only small, specific patient cohorts to be included and require a certain number of participating patients to yield definitive results. Manually screening patients through unstructured medical records is time-consuming and expensive. This paper explores the potential of large language models (LLMs) enhanced with medical context to automate patient eligibility assessments for clinical trials. We first design a two-stage zero-shot LLM approach to analyze a patient’s medical history (presented as unstructured text) and to determine their eligibility for a specific trial. We use advanced prompting strategies to guide the LLM toward faster and more targeted matches between trials and eligible patients. Additionally, a two-stage retrieval pipeline pre-filters potential trials using efficient retrieval techniques, reducing the number of trials considered for each patient. This two-way matching substantially improves processing speed and cost-effectiveness for clinical trial recruitment. Our method holds promise for streamlining clinical trial patient recruitment to accelerate medical advances.

Cite this Paper


BibTeX
@InProceedings{pmlr-v259-saeidi25a, title = {Streamlining Clinical Trial Recruitment: A Two-Stage Zero-Shot LLM Approach with Advanced Prompting}, author = {Saeidi, Mozhgan}, booktitle = {Proceedings of the 4th Machine Learning for Health Symposium}, pages = {886--896}, year = {2025}, editor = {Hegselmann, Stefan and Zhou, Helen and Healey, Elizabeth and Chang, Trenton and Ellington, Caleb and Mhasawade, Vishwali and Tonekaboni, Sana and Argaw, Peniel and Zhang, Haoran}, volume = {259}, series = {Proceedings of Machine Learning Research}, month = {15--16 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v259/main/assets/saeidi25a/saeidi25a.pdf}, url = {https://proceedings.mlr.press/v259/saeidi25a.html}, abstract = {Identifying patient eligibility for clinical trials is a critical bottleneck hindering medical research progress because many clinical trials allow only small, specific patient cohorts to be included and require a certain number of participating patients to yield definitive results. Manually screening patients through unstructured medical records is time-consuming and expensive. This paper explores the potential of large language models (LLMs) enhanced with medical context to automate patient eligibility assessments for clinical trials. We first design a two-stage zero-shot LLM approach to analyze a patient’s medical history (presented as unstructured text) and to determine their eligibility for a specific trial. We use advanced prompting strategies to guide the LLM toward faster and more targeted matches between trials and eligible patients. Additionally, a two-stage retrieval pipeline pre-filters potential trials using efficient retrieval techniques, reducing the number of trials considered for each patient. This two-way matching substantially improves processing speed and cost-effectiveness for clinical trial recruitment. Our method holds promise for streamlining clinical trial patient recruitment to accelerate medical advances.} }
Endnote
%0 Conference Paper %T Streamlining Clinical Trial Recruitment: A Two-Stage Zero-Shot LLM Approach with Advanced Prompting %A Mozhgan Saeidi %B Proceedings of the 4th Machine Learning for Health Symposium %C Proceedings of Machine Learning Research %D 2025 %E Stefan Hegselmann %E Helen Zhou %E Elizabeth Healey %E Trenton Chang %E Caleb Ellington %E Vishwali Mhasawade %E Sana Tonekaboni %E Peniel Argaw %E Haoran Zhang %F pmlr-v259-saeidi25a %I PMLR %P 886--896 %U https://proceedings.mlr.press/v259/saeidi25a.html %V 259 %X Identifying patient eligibility for clinical trials is a critical bottleneck hindering medical research progress because many clinical trials allow only small, specific patient cohorts to be included and require a certain number of participating patients to yield definitive results. Manually screening patients through unstructured medical records is time-consuming and expensive. This paper explores the potential of large language models (LLMs) enhanced with medical context to automate patient eligibility assessments for clinical trials. We first design a two-stage zero-shot LLM approach to analyze a patient’s medical history (presented as unstructured text) and to determine their eligibility for a specific trial. We use advanced prompting strategies to guide the LLM toward faster and more targeted matches between trials and eligible patients. Additionally, a two-stage retrieval pipeline pre-filters potential trials using efficient retrieval techniques, reducing the number of trials considered for each patient. This two-way matching substantially improves processing speed and cost-effectiveness for clinical trial recruitment. Our method holds promise for streamlining clinical trial patient recruitment to accelerate medical advances.
APA
Saeidi, M.. (2025). Streamlining Clinical Trial Recruitment: A Two-Stage Zero-Shot LLM Approach with Advanced Prompting. Proceedings of the 4th Machine Learning for Health Symposium, in Proceedings of Machine Learning Research 259:886-896 Available from https://proceedings.mlr.press/v259/saeidi25a.html.

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