Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2023 12:00 AM
End Date
7-1-2023 12:00 AM
Description
Digital health data quality is a critical concern in the healthcare industry, jeopardizing the secondary use of data for revolutionizing population health, and hindering patient care and organizational outcomes. Limited published evidence exists for explaining why these data quality issues emerge. The Odigos framework is a notable exception asserting that data quality issues emerge from three worlds: material world (e.g., technology artifact), personal world (e.g., technology users/use), and social world (e.g., organizations/ institutions) but has yet to systematically unpack the elements within these worlds. Through deductive and inductive analysis of interview data from a case study of the Emergency Department of Australia’s first large digital hospital, we apply and extend the Odigos framework by identifying elements emanating from the three worlds and their interrelationships as root causes of data quality issues. These elements can then be used by hospitals to develop strategies to proactively improve their digital health data quality.
Recommended Citation
Eden, Rebekah; Syed, Rehan; Makasi, Tendai; Andrews, Robert; Ter Hofstede, Arthur; Wynn, Moe; Donovan, Raelene; Eley, Robert; and Staib, Andrew, "Revealing the Root Causes of Digital Health Data Quality Issues: A Qualitative Investigation of the Odigos Framework" (2023). Hawaii International Conference on System Sciences 2023 (HICSS-56). 7.
https://aisel.aisnet.org/hicss-56/hc/adoption/7
Revealing the Root Causes of Digital Health Data Quality Issues: A Qualitative Investigation of the Odigos Framework
Online
Digital health data quality is a critical concern in the healthcare industry, jeopardizing the secondary use of data for revolutionizing population health, and hindering patient care and organizational outcomes. Limited published evidence exists for explaining why these data quality issues emerge. The Odigos framework is a notable exception asserting that data quality issues emerge from three worlds: material world (e.g., technology artifact), personal world (e.g., technology users/use), and social world (e.g., organizations/ institutions) but has yet to systematically unpack the elements within these worlds. Through deductive and inductive analysis of interview data from a case study of the Emergency Department of Australia’s first large digital hospital, we apply and extend the Odigos framework by identifying elements emanating from the three worlds and their interrelationships as root causes of data quality issues. These elements can then be used by hospitals to develop strategies to proactively improve their digital health data quality.
https://aisel.aisnet.org/hicss-56/hc/adoption/7