Open AccessAn Evaluation of Understandability of Patient Journey Models in Mental Health
Author(s) -
Jennifer Percival,
Carolyn McGregor
Publication year - 2016
Publication title -
jmir human factors
Language(s) - English
Resource type - Journals
ISSN - 2292-9495
DOI - 10.2196/humanfactors.5640
Subject(s) - usability , clarity , process (computing) , computer science , process management , identification (biology) , health care , information model , knowledge management , information system , management science , human–computer interaction , engineering , software engineering , biochemistry , chemistry , botany , electrical engineering , economics , biology , economic growth , operating system
Background There is a significant trend toward implementing health information technology to reduce administrative costs and improve patient care. Unfortunately, little awareness exists of the challenges of integrating information systems with existing clinical practice. The systematic integration of clinical processes with information system and health information technology can benefit the patients, staff, and the delivery of care. Objectives This paper presents a comparison of the degree of understandability of patient journey models. In particular, the authors demonstrate the value of a relatively new patient journey modeling technique called the Patient Journey Modeling Architecture (PaJMa) when compared with traditional manufacturing based process modeling tools. The paper also presents results from a small pilot case study that compared the usability of 5 modeling approaches in a mental health care environment. Method Five business process modeling techniques were used to represent a selected patient journey. A mix of both qualitative and quantitative methods was used to evaluate these models. Techniques included a focus group and survey to measure usability of the various models. Results The preliminary evaluation of the usability of the 5 modeling techniques has shown increased staff understanding of the representation of their processes and activities when presented with the models. Improved individual role identification throughout the models was also observed. The extended version of the PaJMa methodology provided the most clarity of information flows for clinicians. Conclusions The extended version of PaJMa provided a significant improvement in the ease of interpretation for clinicians and increased the engagement with the modeling process. The use of color and its effectiveness in distinguishing the representation of roles was a key feature of the framework not present in other modeling approaches. Future research should focus on extending the pilot case study to a more diversified group of clinicians and health care support workers.