Content Analysis of Serious Illness Conversation Documentation: Structured vs. Free-Text Information.

CONTEXT: Clear, accessible, and thorough documentation of serious illness conversations helps ensure that critical information patients share with clinicians is reflected in their future care. OBJECTIVES: We sought to characterize and compare serious illness conversations recorded in two different ways in the electronic health record to better understand patterns of serious illness conversation documentation. METHODS: We performed content analysis of serious illness conversations documented in the electronic health record, whether documented via structured tab or free-text clinical notes, for patients (n = 150) with advanced cancer who started a treatment associated with a poor prognosis between October 2020 and June 2022. A multidisciplinary team iteratively developed a codebook to classify serious illness conversation content (e.g., goals/hopes) on a preliminary sample (n = 30), and two researchers performed mixed deductive-inductive coding on the remaining data (n = 120). We reviewed documentation from 34 patients with serious illness conversations documentation in the structured tab only, 43 with documentation in only free-text clinical notes, and 44 with documentation of both types. We then compared content between documentation types. RESULTS: Information documented more frequently in structured tabs included fears/worries and illness understanding; clinical notes more often included treatment preferences, deliberations surrounding advance directives, function, and trade-offs. Qualitative insights highlight a range of length and detail across documentation types, and suggest notable authorship by palliative and social work clinicians. CONCLUSION: How serious illness conversations are documented in the electronic health record may impact the content captured. Future quality improvement efforts should seek to consolidate documentation sources to improve care and information retention.

The Temperature Feature of ChatGPT: Modifying Creativity for Clinical Research.

More clinicians and researchers are exploring uses for large language model chatbots, such as ChatGPT, for research, dissemination, and educational purposes. Therefore, it becomes increasingly relevant to consider the full potential of this tool, including the special features that are currently available through the application programming interface. One of these features is a variable called temperature, which changes the degree to which randomness is involved in the model's generated output. This is of particular interest to clinicians and researchers. By lowering this variable, one can generate more consistent outputs; by increasing it, one can receive more creative responses. For clinicians and researchers who are exploring these tools for a variety of tasks, the ability to tailor outputs to be less creative may be beneficial for work that demands consistency. Additionally, access to more creative text generation may enable scientific authors to describe their research in more general language and potentially connect with a broader public through social media. In this viewpoint, we present the temperature feature, discuss potential uses, and provide some examples.

Bolstering Advance Care Planning Measurement Using Natural Language Processing.

Despite its growth as a clinical activity and research topic, the complex dynamic nature of advance care planning (ACP) has posed serious challenges for researchers hoping to quantitatively measure it. Methods for measurement have traditionally depended on lengthy manual chart abstractions or static documents (e.g., advance directive forms) even though completion of such documents is only one aspect of ACP. Natural language processing (NLP), in the form of an assisted electronic health record (EHR) review, is a technological advancement that may help researchers better measure ACP activity. In this article, we aim to show how NLP-assisted EHR review supports more accurate and robust measurement of ACP. We do so by presenting three example applications that illustrate how using NLP for this purpose supports (1) measurement in research, (2) detailed insights into ACP in quality improvement, and (3) identification of current limitations of ACP in clinical settings.