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The BME363 ECA for July 2026 uses a Stanford study of ChatEHR, a platform that brings large language models into the electronic health record of an academic medical center. Six linked parts ask you to explain the limits of LLMs in medicine and what safe use needs, examine the platform and how it was evaluated, describe its automations, discuss governance, compare pilot results and assess implementation challenges.
| 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.
Get Expert Help with BME363
Use the Stanford paper as evidence, not a summary: link each LLM limitation, such as hallucination, bias or privacy risk, to a specific safety requirement, and when you compare the automation and user-interface pilots, explain why their value differs. Complete the declaration and the AI Attribution Table carefully, since SUSS deducts marks for gaps.
Our healthcare and nursing assignment help can explain the clinical workflow points, and our essay writing service supports structure and APA referencing. For a related SUSS technology brief, see the ICT340 NEA Hawker Centre ECA.