Exploring a Large Language Model-Based Chatbot Use in Data Analysis: A Case Study of the Problems Related to the Do Not Attempt Resuscitation Order

Abstract

The interpretative framework was used to explore how healthcare professionals (HCPs), artificial intelligence (AI), and researchers construct and negotiate meaning, trust, and authority in critical, highly ethical medical contexts. This case study aimed to explore and test the integration of the Microsoft Copilot® large language model (LLM) chatbot into ethically sensitive clinical decision-making (DM) to identify the problems HCPs face in do-not-attempt-resuscitation (DNAR) order-making and key areas for development. A sample of 100 HCPs' views was analyzed using qualitative content analysis. AI identified 15 thematic categories emerging from responses that described issues related to DNAR protocols and DM. The frequency of opinions in categories ranged from 4 to 16 (Copilot®) and from 2 to 16 (researchers). Based on a three-level frequency scale, AI's interpretations were especially evident in categories describing the roles of patients and families as well as practical issues related to DNAR orders. In contrast, the researchers focused on the importance of communication and documentation.