JMIR Publications

ABSTRACT

Background:

Currently, there is no holistic theoretical approach available for guiding classification development. Based on our recent classification development research in the area of patient safety in health information technology, this focus area would benefit from a more systematic approach. Although some valuable theoretical and methodological approaches have been presented, typically classification development literature is limited to methodological development in a specific domain or it is practically oriented.

Objective:

Main purposes of this study are to fill the methodological gap in classification development research by exploring possible elements of systematic development based on previous literature, and to promote sustainable and well-grounded classification outcomes by identifying a set of recommended elements. Specifically, the aim is to answer the following question: what are the main elements for systematic classification development based on research evidence and our use case?

Methods:

The study applies qualitative research approach. Based on the previous literature preliminary elements for classification development were specified: defining a concept model, documenting the development process, incorporating multidisciplinary expertise, validating results, and maintaining the classification. The elements were compiled as guiding principles for the research process and tested in our use case of patient safety incidents.

Results:

Results illustrate classification development based on the chosen elements with four examples of technology-induced errors. Examples from the use case regard usability, system downtime, clinical workflow and medication section problems. The study results confirm and thus, suggest that a more comprehensive and theory-based systematic approach promotes well-grounded classification work by enhancing transparency and possibilities for assessing the development process.

Conclusions:

We recommend further testing of the preliminary main elements presented in this study. The research presented herein could serve as a basis for future work. Our recently developed classification and the use case presented here serve as an example. Data retrieved from e.g., other source electronic health records and use contexts could refine and validate the suggested methodological approach. Clinical Trial: Study permission, update March 23, 2020, License HUS/200/2020 (Finland)