Information Quality Challenges of Patient-Generated Data in Clinical Practice

Frontiers in Public Health

Nigel Shadbolt¹  Max Van Kleek¹  Richard Giordano²  Peter West²  Mark Weal³ 

[1] Department of Computer Science, University of Oxford, Oxford, United Kingdom;Faculty of Health Sciences, University of Southampton, Southampton, United Kingdom;Web and Internet Science, Faculty of Physical Science and Engineering, University of Southampton, Southampton, United Kingdom;

关键词: self-tracking;    quantified self;    personalized medicine;    information quality;    health informatics;    clinical decision making;   

DOI  :  10.3389/fpubh.2017.00284

来源: DOAJ

【 摘 要 】

A characteristic trend of digital health has been the dramatic increase in patient-generated data being presented to clinicians, which follows from the increased ubiquity of self-tracking practices by individuals, driven, in turn, by the proliferation of self-tracking tools and technologies. Such tools not only make self-tracking easier but also potentially more reliable by automating data collection, curation, and storage. While self-tracking practices themselves have been studied extensively in human–computer interaction literature, little work has yet looked at whether these patient-generated data might be able to support clinical processes, such as providing evidence for diagnoses, treatment monitoring, or postprocedure recovery, and how we can define information quality with respect to self-tracked data. In this article, we present the results of a literature review of empirical studies of self-tracking tools, in which we identify how clinicians perceive quality of information from such tools. In the studies, clinicians perceive several characteristics of information quality relating to accuracy and reliability, completeness, context, patient motivation, and representation. We discuss the issues these present in admitting self-tracked data as evidence for clinical decisions.

【 授权许可】

Unknown