BME363 Applications of Artificial Intelligence in Healthcare ECA July 2026: ChatEHR and LLM Adoption at an Academic Medical Center, Singapore

Updated: 24 Oct 2026 Free Assignment Question
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      University Singapore University of Social Science (SUSS)
      Subject BME363: Applications of Artificial Intelligence in Healthcare
      Course BME363 Applications of Artificial Intelligence in Healthcare
      University Singapore University of Social Sciences (SUSS)
      Assessment End-of-Course Assessment (ECA), individual
      Semester July Semester 2026
      Weighting 70% of the course marks (100 marks in total)
      Source Article Stanford study “Adoption and Use of LLMs at an Academic Medical Center” (arXiv 2602.00074)
      Referencing APA, official answer template (Appendix A) with declaration and AI Attribution Table

      The BME363 ECA for July 2026 is built around one case: how a large academic medical center rolled out ChatEHR, a platform that puts large language models (LLMs) inside the electronic health record. Students read the Stanford paper (and other cited sources) and answer six linked parts on LLM limitations, platform capabilities and evaluation, automations, governance, pilot value and implementation challenges.

      Question 1

      Please refer to the Stanford article titled “Adoption and Use of LLMs at an Academic Medical Center” at https://arxiv.org/abs/2602.00074 or any relevant online sources (with appropriate citations) in order to answer the questions below.

      Question 1a

      Demonstrate your understanding of the current limitations of large language models (LLMs) in medicine, and the requirements of their safe and effective use in clinical care. (15 marks)

      Question 1b

      Examine and discuss the capabilities and evaluation framework of the ChatEHR platform. (20 marks)

      Question 1c

      Describe the concept of automations in the context of the ChatEHR platform. Illustrate your answer with ONE (1) example of how automations can be used in a healthcare setting. (10 marks)

      Question 1d

      Discuss the governance and team design for the ChatEHR platform. (15 marks)

      Question 1e

      Analyse the value assessment results of the automations and user interface pilot studies of ChatEHR platform. In your answer, compare the benefits demonstrated by each pilot and explain the factors that influence their estimated value. (20 marks)

      Question 1f

      Examine the challenges and limitations faced in the implementation of the ChatEHR platform. (20 marks)

      Submission Requirements

      • Use the official answer template in Appendix A (course code, ECA title, PI number, name, tutorial group, date of submission).
      • Complete the declaration on originality, no resubmission for another course, and use or non-use of AI tools under the Academic Integrity Rules.
      • If AI tools were used, include the AI Attribution Table (Table A) with the tool, purpose, exact prompts, generated output and how it was used, and keep your original work as evidence.
      • Reference all sources, including AI-generated content, in APA style. Submit through Canvas; late work follows the ECA marks deduction scheme.

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      Most BME363 scripts lose marks in the same places: Question 1a lists generic LLM risks without tying them to clinical safety requirements, 1b describes ChatEHR features but skips how the team actually evaluated them, and 1e repeats pilot numbers instead of comparing the automation and user-interface pilots and explaining what drives their estimated value. A tutor who knows SUSS ECA marking can help you build each part from the Stanford paper with clear APA citations and a correct AI Attribution Table. Our SUSS ECA assignment help can review your structure and argument, and our healthcare and nursing assignment experts can explain clinical workflow points such as EHR summarisation and safety checks. You can also compare the earlier BME363 TMA on code-free deep learning or browse more SUSS assignment questions.

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