Electronic health record adopters: a typology based on patients' privacy concerns and perceived benefits

Patients' adoption of electronic health records (EHRs) varies substantially. Although some countries, such as Estonia and Denmark, are sufficiently advanced in terms of EHR generalisation, others, such as France, are figuring out how to implement and disseminate EHRs. These governments must respond to patients' disparities to achieve the expected performance for healthcare systems and improve the quality of care delivery. This study investigates patients' perceived benefits and privacy concerns related to EHRs to develop a typology of patients, identify the characteristics of different clusters and propose practical measures for public policy-makers.

We conducted a cross-sectional study using online questionnaires.

An online quantitative survey was carried out in France. The final sample of EHR non-users (N = 1076) was fitted to be representative of the French population by age and gender, region and socioprofessional status. Hierarchical and non-hierarchical cluster analyses were performed. Several robustness check analyses were also performed.

Cluster analyses identified four patient clusters: the worried, who show the highest mean privacy concern and risk levels related to health data disclosure; the ready adopters, who lack privacy concerns and risk and are the most motivated by EHR benefits; the concerned adopters, who express far fewer privacy concerns and perceive EHR benefits more favourably than the worried adopters; and the balanced adopters, who are relatively similar to the ready adopters in their EHR motives and are still concerned about their health data, suggesting a segment that is easier to convince. Comparing clusters regarding the intentions to create EHRs and willingness to disclose health data confirms that ready adopters, followed by balanced adopters, are more likely to create an EHR and disclose health data. The concerned adopters and, finally, the worried exhibit the lowest intentions for EHR creation and data disclosure.

The results provide meaningful insights into patient profiles and expectations. The findings underscore the need to implement targeting policies for each cluster and design concrete solutions for improving EHR performance.