Interdisciplinary systems may restore the healthcare professional-patient relationship in electronic health systems.

Abstract

To develop a framework that models the impact of electronic health record (EHR) systems on healthcare professionals' well-being and their relationships with patients, using interdisciplinary insights to guide machine learning in identifying value patterns important to healthcare professionals in EHR systems. A theoretical framework of EHR systems' implementation was developed using interdisciplinary literature from healthcare, information systems, and management science focusing on the systems approach, clinical decision-making, and interface terminologies. Healthcare professionals balance personal norms of narrative and data-driven communication in knowledge creation for EHRs by integrating detailed patient stories with structured data. This integration forms 2 learning loops that create tension in the healthcare professional-patient relationship, shaping how healthcare professionals apply their values in care delivery. The manifestation of this value tension in EHRs directly affects the well-being of healthcare professionals. Understanding the value tension learning loop between structured data and narrative forms lays the groundwork for future studies of how healthcare professionals use EHRs to deliver care, emphasizing their well-being and patient relationships through a sociotechnical lens. EHR systems can improve the healthcare professional-patient relationship and healthcare professional well-being by integrating norms and values into pattern recognition of narrative and data communication forms.