BME363 Applications of AI in Healthcare ECA July 2026: ChatEHR and LLMs in Clinical Care Question, Singapore
BME363 End-of-Course Assessment, July Semester 2026
For the BME363 ECA (70% of the course) you work from a Stanford study on ChatEHR, a platform that places large language models inside an electronic health record. Six parts ask about the limits of LLMs in medicine, ChatEHR’s capabilities and evaluation, its automations, governance and team design, the value of its pilots and the challenges of putting it into practice.
| 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 |
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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Treat the Stanford ChatEHR paper as your evidence base: in Question 1a link each LLM limitation, such as hallucination, bias or privacy risk, to a concrete requirement for safe clinical use, and in 1e compare the automation pilots with the user-interface pilot and explain why their value differs. SUSS also checks the declaration and AI Attribution Table, so complete them carefully.Our essay help in Singapore can guide the argument and APA citations, and our citation and referencing check verifies your sources. For a related SUSS technology ECA, see the ICT340 NEA Hawker Centre question.
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