  Physicians considering clinical trials for their patients are met with the
laborious process of checking many text based eligibility criteria. Large
Language Models (LLMs) have shown to perform well for clinical information
extraction and clinical reasoning, including medical tests, but not yet in
real-world scenarios. This paper investigates the use of InstructGPT to assist
physicians in determining eligibility for clinical trials based on a patient's
summarised medical profile. Using a prompting strategy combining one-shot,
selection-inference and chain-of-thought techniques, we investigate the
performance of LLMs on 10 synthetically created patient profiles. Performance
is evaluated at four levels: ability to identify screenable eligibility
criteria from a trial given a medical profile; ability to classify for each
individual criterion whether the patient qualifies; the overall classification
whether a patient is eligible for a clinical trial and the percentage of
criteria to be screened by physician. We evaluated against 146 clinical trials
and a total of 4,135 eligibility criteria. The LLM was able to correctly
identify the screenability of 72% (2,994/4,135) of the criteria. Additionally,
72% (341/471) of the screenable criteria were evaluated correctly. The
resulting trial level classification as eligible or ineligible resulted in a
recall of 0.5. By leveraging LLMs with a physician-in-the-loop, a recall of 1.0
and precision of 0.71 on clinical trial level can be achieved while reducing
the amount of criteria to be checked by an estimated 90%. LLMs can be used to
assist physicians with pre-screening of patients for clinical trials. By
forcing instruction-tuned LLMs to produce chain-of-thought responses, the
reasoning can be made transparent to and the decision process becomes amenable
by physicians, thereby making such a system feasible for use in real-world
scenarios.
