Methods for investigating electronic health records and patient-provider interaction: A scoping review

Electronic Health Records (EHRs) have improved many aspects of healthcare but also introduced challenges, including increased documentation and reduced patient-provider interaction. With recent artificial intelligence (AI) developments in the EHR landscape, such as Automated Medical Reporting (AMR) technologies that aim to automate visit summarization, there is a need to understand how these innovations affect medical interactions. However, methodological inconsistencies in existing studies on EHR use and medical interactions limit study comparability. This scoping review identifies methods to study the impact of EHR use on patient-provider interaction, providing a foundation for future evaluations of AMR implementations.

A systematic search identified empirical studies (2015-present) on patient-provider interaction involving EHRs, supplemented by three prior reviews. Fifty-nine studies met the inclusion criteria. Data extraction focused on research methods, concepts, indicators, and measurement tools used.

The review identified diverse methods to study EHR use within medical interactions, including video/audio analysis, structured observation, surveys, and interviews. Studies focused on concepts such as patient-centered communication, attention, satisfaction, computer interaction, and room layout, using diverse standardized and self-developed instruments. Most studies treated EHR use as separate from medical interaction. A smaller group used qualitative methods, such as conversation analysis and ethnography, to explore how EHR use is embedded in medical interaction. Indicators included gaze, typing, talk time, and consultation length.

Two dominant approaches emerged: one treating EHR use as separate from medical interaction, and another, less common, viewing both as functionally integrated. While various concepts and instruments were identified, many studies relied on unvalidated, non-experimental methods, limiting comparability and causal inference. A key gap remains in standardized methods that link broad communication concepts with specific functional behaviors. As AI-driven tools like AMR emerge, future research needs to adopt more integrative approaches to assess their impact and support human-centered EHR design.