Using Ethnographic Methods to Classify the Human Experience in Medicine: A Case Study of the Presence Ontology

TL;DR

This paper develops the Presence Ontology, a conceptual framework derived from ethnographic methods, to classify and understand interpersonal connection factors in medicine, aiming to improve healthcare data annotation and clinical tools.

Contribution

It introduces a novel ontology based on ethnographic relationality to systematically classify interpersonal factors in medical encounters.

Findings

Ontology categorizes communication, emotions, tools, and environment.

Relational ethnography aids in representing experiential concepts like empathy and trust.

Ontology can support informatics applications for healthcare data and clinical tools.

Abstract

Objective Although social and environmental factors are central to provider patient interactions, the data that reflect these factors can be incomplete, vague, and subjective. We sought to create a conceptual framework to describe and classify data about presence, the domain of interpersonal connection in medicine. Methods Our top down approach for ontology development based on the concept of relationality included 1) broad survey of social sciences literature and systematic literature review of more than 20,000 articles around interpersonal connection in medicine, 3) relational ethnography of clinical encounters (5 pilot, 27 full) and 4) interviews about relational work with 40 medical and nonmedical professionals. We formalized the model using the Web Ontology Language in the Protege ontology editor. We iteratively evaluated and refined the Presence Ontology through manual expert review and automated annotation of literature. Results and Discussion The Presence Ontology facilitates the naming and classification of concepts that would otherwise be vague. Our model categorizes contributors to healthcare encounters and factors such as Communication, Emotions, Tools, and Environment. Ontology evaluation indicated that Cognitive Models (both patients explanatory models and providers caregiving approaches) influenced encounters and were subsequently incorporated. We show how ethnographic methods based in relationality can aid the representation of experiential concepts (e.g., empathy, trust). Our ontology could support informatics applications to improve healthcare such annotation of videotaped encounters, clinical instruments to measure presence, or EHR based reminders for providers. Conclusion The Presence Ontology provides a model for using ethnographic approaches to classify interpersonal data.