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Improving Data Quality in Primary Care: Modelling, Measurement, and the Design of Interventions
by Justin St-Maurice
Institution: | University of Waterloo |
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Year: | 2017 |
Keywords: | data quality; healthcare; cognitive work analysis; persuasion design; system modelling; comparisons; primary care; data codification; statistical process control; field study |
Posted: | 02/01/2018 |
Record ID: | 2153698 |
Full text PDF: | http://hdl.handle.net/10012/11726 |
In an era where governments around the world invest heavily in data collection and data management, poor-quality data is expensive and has many direct and indirect costs. While there are different types of data quality challenges, some of the more complex data quality problems depend on the design and production processes involved in generating data. Therefore, it is important to design systems that support better data quality. This involves understanding what quality means in a specific context, understanding how it can be measured, and identifying ways to encourage better data quality behaviours. Healthcare is not immune to the challenges of data quality and can be classified as a complex socio-technical system by virtue of its characteristics. As such, the study of healthcare data quality and its improvement is well suited for the domain of systems design and human factors engineering. Cognitive Work Analysis (CWA) is especially well suited for this task, as it can be used to better understand the context and workflow of users in complex socio-technical domains. It is a conceptual framework that facilitates the analysis of factors that shape human-information interaction and has been used in healthcare for over 20 years. The approach is work-centred, rather than user-centred, and it analyses the constraints and goals that shape information behaviour in the work environment. I used CWA as a framework to help me analyse the problem of data quality in healthcare. My research uses an instrumental case study approach to understand data quality in primary care. My goal was to answer three questions: In primary care, how are individual users influenced by their environment to input high-quality data? What techniques could be used to design systems that persuade users to enter higher-quality data? Is it possible to improve data quality in primary care by persuading users with the user interface of information systems in these complex socio-technical systems? The scope of work included modelling data quality, defining and measuring data quality in a primary care system, establishing design concepts that could improve data quality through persuasion, and testing the viability of some design concepts.I began analysing this problem by creating an abstraction hierarchy of patient treatment with medical records. This model can be used to represent patient treatment from a primary care perspective. The model helped explain the patient treatment ecosystem and how data is generated through patient encounters. After creating my model to represent patient treatment, I incorporated it into two CWAs of data quality and data codification. The first model represented codification in the primary care ecosystem, whereas the second model represented codification in community hospitals. After developing abstraction hierarchies for both domains, I analysed similar tasks from each system with control task analysis, strategies analysis, and worker competencies analysis. The tasks that I analysed related specifically to data
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