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Locum - Oncology Nurse - Medical Evaluation project

Robert Half
Posted 3 days ago, valid for a month
Location

London, Greater London EC1R 0WX

Contract type

Part Time

In order to submit this application, a Reed account will be created for you. As such, in addition to applying for this job, you will be signed up to all Reed’s services as part of the process. By submitting this application, you agree to Reed’s Terms and Conditions and acknowledge that your personal data will be transferred to Reed and processed by them in accordance with their Privacy Policy.

Sonic Summary

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  • We are looking for a fully remote Oncology Nurse for a 3-month project, requiring 40 hours of work per week.
  • The candidate will evaluate Large Language Model-generated summaries of medical records to ensure their accuracy and reliability.
  • Applicants should have experience as an Oncology Nurse with a strong understanding of clinical documentation and oncology treatments.
  • Experience in reviewing medical records is preferred, and familiarity with Large Language Models is advantageous but not essential.
  • Salary rates will depend on the candidate's experience, qualifications, and training.

Locum - Oncology Nurse - Medical LLM Evaluation Project - 3 months - Fully remote - 40 hours per week

We are seeking a highly skilled Oncology Nurse to work as part of team, evaluating Large Language Model (LLM)-generated summaries of medical records as part of a specialised project. The successful candidate will be instrumental in ensuring the accuracy and reliability of LLM summaries of clinical documents, such as medical records, laboratory results, pathology reports, and more.

Responsibilities:

  • Evaluate LLM-produced summaries of medical records, including clinical notes, labs, and pathology reports.
  • Work with a team to revise medical records and compare them with LLM-generated summaries.
  • Identify and highlight any discrepancies or omissions in the summary, ensuring that critical medical information is accurately captured.
  • Support model evaluation by answering standard questions based on the accuracy and comprehensiveness of the LLM summary.
  • Collaborate with the team to refine LLM outputs and ensure high-quality documentation for future use.
  • Input: Electronic copies of records and LLM-generated summaries (MS Word).
  • Output: Provide feedback in the form of a template, answering evaluation questions and flagging issues for further review.

Qualifications / Experience

  • An experienced Oncology Nurse with a strong understanding of clinical documentation, oncology treatments, and medical terminology.
  • Experience in evaluating or reviewing medical records is highly preferred.
  • Familiarity with Large Language Models (LLMs) and their application in clinical settings would be advantageous but by no means essential.
  • Strong attention to detail and the ability to identify and correct inaccuracies in complex medical information.
  • Ability to work independently and collaborate with a multidisciplinary team.
  • Proficiency in using electronic medical records and MS Word.

If you're passionate about improving the accuracy of AI tools in healthcare and have an extensive oncology background, we invite you to apply for this exciting opportunity to contribute to the future of medical documentation evaluation.

Robert Half Ltd acts as an employment business for temporary positions and an employment agency for permanent positions. Robert Half is committed to diversity, equity and inclusion. Suitable candidates with equivalent qualifications and more or less experience can apply. Rates of pay and salary ranges are dependent upon your experience, qualifications and training. If you wish to apply, please read our Privacy Notice describing how we may process, disclose and store your personal data: gb/en/privacy-notice.

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In order to submit this application, a Reed account will be created for you. As such, in addition to applying for this job, you will be signed up to all Reed’s services as part of the process. By submitting this application, you agree to Reed’s Terms and Conditions and acknowledge that your personal data will be transferred to Reed and processed by them in accordance with their Privacy Policy.