This study examines the intention to use personal health records and associated factors among healthcare providers. The study revealed that healthcare providers’ intention to use personal health records was 424 (57.6%) [95.0%: CI: 53.9–61.2]. This showed that more than half of healthcare providers intended to use personal health records for making better decisions, schedule appointments, enhance patient engagement, and reduce the amount of information missed while communicating verbally. In a resource-limited setting, they encounter challenges with the technology infrastructure and exhibit low socioeconomic status to adopt the new technology. This study showed that healthcare providers’ intention to use personal health records was promising.
A study conducted in Ethiopia revealed scores of 39.8% and 46.5% (28, 58), which are lower than our study. The majority of study participants may not be familiar with technologies, which could be the cause of this inconsistency. Another reason for this discrepancy could be the small sample size (n = 423) and study participants.
This finding is lower than another study done in Saudi Arabia (70%) (27). The possible reasons for the differences might be variations in awareness about the use of personal health records. Another probable explanation for this discrepancy might be due to the study participants. In the same way, this finding was lower than that of a study done in the Republic of Korea (72%) (66). This discrepancy might be due to the study participants. Another reason might be the emphasis on general healthcare professionals being aware of new digital health technologies that are beneficial to health. Another study was done in Canada (61%) (4) and Malaysia (78%) (46), which is higher than our study finding. The possible explanation for this discrepancy might be that the majority of participants in our study were not highly familiar with personal health records. Additionally, inadequate e-health literacy, difficulties in knowledge regarding the use of personal health records, a lack of awareness about the importance of personal health records for healthcare services, and a low level of emerging digital health technology in Ethiopia (67)
The proposed model illustrates 72% of the variance (R2 = 0.72) in the intention of healthcare providers to use personal health records. In our investigation, the intention to use personal health records was significantly associated with performance expectancy, effort expectancy, social influence, and facilitating conditions, indicating that 4 out of 6 path relationships in the proposed model were directly associated with the intention to use PHRs. In comparison, hedonic motivation and habits did not significantly influence the intention to use personal health records. Accordingly, H1, H2, H3, and H4 are supported.
Based on the findings, the following perspectives are presented to improve Ethiopian healthcare professionals’ intentions to use personal health records:
According to our study, performance expectancy had a direct effect on healthcare providers’ intention to use personal health records (β = 0.325, P < 0.01), which was the most dominant factor in the intention to use personal health records. This entails that healthcare providers are more likely to intend to use personal health records when they perceive them to be very useful and to complete healthcare tasks more quickly. The findings of this study are consistent with previous studies in Ethiopia (β = 0.39, P < 0.01, β = 0.298, P < 0.01) (28, 35), Jordan (β = 4.78, P < 0.001) (68), Taiwan (β = 0.078, P = 0.041) (41), Portugal (β = 0.285, P < 0.01) (69), Saudi Arabia (β = 0.22, P < 0.01, β = 0.17, P = 0.03) (26, 38), and England (β = 0.343, P < 0.01) (37). This study revealed that PHR systems are directly related to improvements in the performance of healthcare providers. The possible reason for this could be that the usefulness of e-health technology (PHRs) is recognized by similar technologies such as electronic health records. Another possible reason might be that healthcare providers in the workplace are influenced by the usefulness of PHRs for improving patient engagement, making better decisions, and enhancing daily workflow (68). Likewise, personal health records are an emerging technology, and healthcare providers have little experience with them. The effect of performance expectancy on the intention to use personal health records is usually stronger for these types of users (60).
This finding has demonstrated that effort expectancy had a direct effect on healthcare providers’ intention to use personal health records (β = 0.289, P < 0.01). This study indicated that healthcare providers who had to assume that, if it is easy to learn how they use personal health records, have clear use of personal health records, and easily become skilled at using personal health records, the intention to use personal health records could be enhanced. These results align with findings in different countries, such as Ethiopia (β = 0.377, P < 0.001, β = 0.385, P < 0.05, β = 0.24, P < 0.001) (28, 35, 70), Taiwan (β = 0.07, P = 0.028) (41), Jordan (β = 4.86, P < 0.001) (68), Iran (β = 2.21, P < 0.01) (71), England (β = 0.16, P < 0.001) (37), Saudi Arabia (β = 0.33, P < 0.001) (25), and Canada (β = 0.45, P = 0.002) (72). The possible reason might be that healthcare providers currently have experience with information technologies. As a result, they could believe that they could use personal health records with little effort. Furthermore, if this technology offers the necessary capabilities, healthcare providers would be willing to devote the effort needed to use it (73).
Therefore, while adopting personal health records, they should be easy to understand and operate by users for sustainable adoption of technologies in the future.
This finding contrasts with another study done in Iran and Saudi Arabia (29, 74). The possible reason for this discrepancy could be assessing the factors with a smaller sample size (n = 303). This might also be due to their perception that using PHRs will simplify their tasks and that information can also be managed in a clear and systematic way.
The findings in this study demonstrate that social influence had a direct effect on healthcare providers’ intention to use personal health records (β = 0.216, P < 0.01). This result revealed that users are motivated to use personal health records when people who are important to them or influence their behavior think that they should use a personal health record. In another way, this finding suggests that people who have a great influence on users can motivate the acceptance of personal health record systems. This finding is consistent with prior studies conducted in different countries, such as Ethiopia (β = 0.18, P < 0.001) (28), Iran (β = 2.63, P < 0.01) (71), Thailand (β = 0.17, P < 0.001) (75), South Korea (β = 0.10, P < 0.001) (76), Portugal (β = 0.10; p < 0.05) (51), Republic of Korea (β = 0.493, P < 0.001) (66), and Saudi Arabia (β = 0.19, P < 0.001) (38). The possible reason could be that healthcare providers perceive pressure from hospital management, patients, and health professionals to use a new system (77). In order to increase their passion and intention to use personal health records in their place of work, healthcare professionals might encounter pressure from an external body.
Another finding of this study demonstrated that facilitating conditions had a positive influence on intention to use personal health records (β = 0.242, P < 0.01). This finding indicates that the availability of resources, support, and knowledge is necessary to motivate healthcare providers to use personal health records. This confirms that the adoption of e-health technology cannot be increased by only enhancing personal health records itself but requires the availability of resources and knowledge necessary to use personal health records systems. On the other hand, the results showed the presence of the resources and the knowledge needed to use the system, the degree of compatibility of the new system with other systems in use, and the availability of assistance in the case of system difficulties. This finding is in line with studies done in Ethiopia (β = 0.23, P < 0.001) (28), Iran (β = 2.84, P < 0.01) (71), South Korea (β = 0.27, P < 0.001) (76), and the Republic of Korea (β = 0.221, P < 0.001) (66). The probable reason could be due to healthcare providers believing that they will be able to help them by connecting with experts to easily learn about new systems (78). Another possible reason might be that healthcare providers believe that PHRs are supported by the health sector transformation plan. Therefore, facilitating conditions are important to motivate users (45).
The other explanation might be that healthcare professionals think they will have access to the resources and technical assistance needed to use personal health records at work (77). Since organizational preparedness and training are key parts of facilitating conditions, healthcare professionals may believe that taking training will put them in a more favorable situation to use PHRs.
This study examines whether there is any gender difference present in the effect of the factors on intention to use PHRs. The results revealed that gender did not moderate the effects of PE→BI, EE→BI, SI→BI, FC→BI, HM→BI, and HA→BI. This result is consistent with prior studies that indicated that gender non-significantly moderate the effects of PE, EE, SI, FC, HM, and HA on the intention to use personal health records (29, 46, 79). This may be because the intention to use personal health records shows no significant difference between females and males.
The findings of this study revealed that the relationship between performance expectancy and healthcare providers’ intention to use personal health records was positively moderated by age (β = 0.269, P < 0.001). This shows that a significant difference exists in performance expectations between younger and older healthcare providers for those who intend to use the personal health record system. This finding showed that the younger healthcare providers had a greater influence on the relationship between performance expectancy and intention. This result is in line with other findings conducted in China (β = 0.33, P < 0.001, β = 0.553, P < 0.01) (80) and Germany (β = 0.03, P < 0.001) (81). A possible reason for this could be that older healthcare providers have less exposure to emerging digital health technology. On the other hand, younger healthcare providers are more likely to feel at ease and understand the value of personal health records since they might be exposed to similar technology (80).
The results of this study showed that the relationship between social influence and healthcare providers’ intention to use personal health records was positively moderated by age (β = 0.272, P < 0.001). This demonstrates that younger and older age groups have quite different expectations regarding social influence for those who intend to use personal health records. This result indicated that the relationship between social influence and intention to use personal health records was more strongly influenced by the younger healthcare providers.
These findings are consistent with other studies conducted in Saudi Arabia (25). The most likely explanation is that younger healthcare providers are encouraged to use personal health records when others are significant to them or have the ability to influence their behavior.
The findings of this study indicated that the relationship between facilitating conditions and healthcare providers’ intention to use personal health records was positively moderated by age (β = 0.362, P < 0.001). This implies that younger and older age groups have quite different facilitating conditions for those who intend to use personal health records. This result proved that the relationship between facilitating conditions and intention to use personal health records was significantly influenced by the older healthcare providers. This finding aligns with other studies conducted in China (33). A possible explanation could be that older healthcare providers tend to uphold the availability of adequate support more highly than younger healthcare providers. Another probable reason might be that older healthcare providers face more difficulties in responding to new systems, which affects their learning process of new technology compared to younger healthcare providers.
Implications of the study
Finally, this study provides theoretical and practical implications based on the findings. Theoretically, the study focuses on the proportion and factors that affect healthcare providers’ intention to use personal health records. Our findings might alleviate any worries about personal health records being accepted in resource-limited settings. Due to the limited evidence on the intention of personal health records, it is a baseline for researchers, especially in resource-limited settings. Therefore, the UTAUT2 model's significance for assessing healthcare providers’ intention to utilize personal health records is statistically supported by our study, and the findings may be relevant to other countries. Our understanding of the significance of the key factors of intention to use personal health records for health management is also improved by this study.
Practically, this study offers insights for health facilities, managers, and decision-makers in the healthcare industry to improve the use and acceptability of personal health records among healthcare providers. Developers can enhance the usability of personal health records in the healthcare sector by ensuring that they complete healthcare services more quickly and that top management supports, allocates resources, and provides knowledge for users to achieve the relative advantage of e-health technology. Finally, the results of this study could lead to better technology usage and could also be considered by healthcare providers and policymakers before making decisions about further spending on new health information system implementation.