Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2022 12:00 AM
End Date
7-1-2022 12:00 AM
Description
Electronic Health Record (EHR) systems are the predominant information system (IS) used by healthcare clinicians and have been the source of both great success and pain. User engagement with EHR systems is unique from traditional IS contexts in significant ways. Prior research explains EHR usage and success primarily on traditional technology acceptance research (i.e., TAM, UTAUT). However, these models assume that EHR engagement is no different from IS systems in general business domains. Yet, the healthcare context is far more regulated than most. Based on qualitative focus group sessions with a leading healthcare analytics firm (KLAS Research), we identify the role of mandates, penalties, and enforcements from government, organizations, associations, and insurance companies in explaining EHR engagement. We validate a measurement instrument for these factors and demonstrate that their inclusion can improve model fit five times over a traditional UTAUT-based model (R2 = 54.8% versus 10.2%).
An Institutional Theory Perspective on EHR Engagement: Mandates, Penalties, and Enforcement
Online
Electronic Health Record (EHR) systems are the predominant information system (IS) used by healthcare clinicians and have been the source of both great success and pain. User engagement with EHR systems is unique from traditional IS contexts in significant ways. Prior research explains EHR usage and success primarily on traditional technology acceptance research (i.e., TAM, UTAUT). However, these models assume that EHR engagement is no different from IS systems in general business domains. Yet, the healthcare context is far more regulated than most. Based on qualitative focus group sessions with a leading healthcare analytics firm (KLAS Research), we identify the role of mandates, penalties, and enforcements from government, organizations, associations, and insurance companies in explaining EHR engagement. We validate a measurement instrument for these factors and demonstrate that their inclusion can improve model fit five times over a traditional UTAUT-based model (R2 = 54.8% versus 10.2%).
https://aisel.aisnet.org/hicss-55/hc/adoption/3