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.
翻译:虽然社会和环境因素是患者互动的核心,但反映这些因素的数据可能是不完整、模糊和主观的。我们试图建立一个概念框架,以描述和分类医学中存在、人际联系领域的数据。我们基于关系概念的本科发展自上而下的方法包括:(1) 广泛调查社会科学文献和系统文献审查医学中人际联系的20,000多篇文章,(3) 临床遭遇的关联人种学(5个试点,27个完整)和(4) 与40名医学和非医学专业人员的关联工作访谈。我们利用蛋白质编辑的网络本体学语言将模型正规化。我们通过手动专家审查和自动的文献说明,对存在的本体学进行反复评价和完善。结果和讨论:本体学有助于点名和分类否则模糊的概念。我们的模型将医疗遭遇和因素如通信、情感、工具和环境等归为主。本体学评估表明,“认知模型”(包括病人解释模型和护理提供者的方法)会影响了解并随后纳入“本体系”。我们基于感化分析、分析分析工具的“分析方法”可如何改进和解释性分析工具。