An Ontology to Improve Accessibility and Quality of Patient Instructions

In the Finnish health care system, patient instructions are public documents written for patients to support them through the care. The ontology introduced in this paper addresses two problems with the patient instructions of Intermunicipal Hospital District of Southwest Finland: the instructions are not extensive and readable enough to fully support patients and the collection of these instructions is poorly organised. The ontology is being developed in cooperation with Intermunicipal Hospital District of Southwest Finland and will be evaluated as a part of a pilot study for an authoring tool in Turku University Hospital. To the best of our knowledge, there are no other suitable ontologies available for modelling Finnish patient instructions. The ontology is an OWL DL ontology produced with the Protégé resource. It models the document structure and the associated metadata specifying the usage and content of the document. The metadata include the topics essential in supporting patients, the characteristics of the intended reader, the phase of the care as well as the organisational hierarchy and the health care processes of the hospital district. The ontology can be utilised in the organisation of patient instructions and can aid in the efforts to improve their quality. The documents can be filtered through the provided facets for improved accessibility. The writing process can be supported by ontology-based methods that facilitate giving feedback: preferred or commonly used pieces of text can be automatically suggested based on a given topic and machine-learning methods can be employed to notify the author of text unintentionally drifting from one topic to another or not matching its assigned topic. The author can also be notified of missing or irrelevant topics by comparing the content of the document to its expected content. These approaches help to write coherent and comprehensive instructions. In the future, patient instructions could be personalised e.g. by focusing the content on issues specific to a particular patient while excluding irrelevant details.