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

Abstract

Objective: 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.

Materials and methods: 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.

Observations: 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.

Discussion: 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.

Conclusion: 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.