Health information technology is to take the practice of identifying, collecting, storing, analyzing, and using patient data to a new level. Despite recent advancements in health IT, including the emergence of electronic health records (EHR) software, interoperability issues still prevent the aforementioned far-fetching goal from becoming a reality. This paper seeks to discuss common sources of interoperability challenges and review strategies for healthcare organizations that can reduce such barriers to cooperation.
Challenges Associated with Data Sharing Across Systems
The effectiveness of data sharing is affected by differences in professional cultures that play into terminology-related dissimilarities and the increasing multiformity of EHRs accessible for those involved in interprofessional partnerships. The drive for interprofessional collaboration between social workers and licensed medical and nursing specialists brings such barriers to light. Around 48% of social workers in community settings collect data to be uploaded to their clients’ EHR profiles, whereas the instances of terminology incoherence may arise in up to 50% of data transfers on interprofessional teams (Zerden et al., 2019). Today’s diversity of EHR types that have drastically different technical specifications also contributes to healthcare organizations’ obstacles to smooth data-sharing experiences, and the degree of uniformity varies depending on setting types. Notably, in outpatient, inpatient, and co-located settings, the chances of having no constant access to the same EHR are smaller than in other environments, including schools, but the inability to use the same EHR with other team members is still reported by 15% of social work professionals (Zerden et al., 2019). Thus, challenges that healthcare organizations face can be evaluated as persistent.
Examples of Interoperability Challenges
Numerous types of barriers to interoperability can be found in clinical practice and scholarly literature. As for the first example, modern EHR systems require clinicians to implement the Intelligent Medical Objects terminology for diagnosis recording, but the insufficient standardization of the extract-transform-load process may become the cause of diagnostic information losses during data transfer and retrieval procedures (Burrows et al., 2020). Such disruptions to information exchange heavily affect the collaborative research process in healthcare settings and require extra interventions, for instance, data mapping (Burrows et al., 2020). Another example stems from my colleagues’ experiences in a mental health inpatient setting. Like many EHR software vendors, the vendor that our setting collaborates with prevents the transmission of patient data outside of the hospital’s system on a free-of-charge basis, which might create obstacles to inter-organizational communication, incur extra costs, and limit the software’s functionality. Due to interoperability limitations, organizations may even be required to continue submitting payments to the providers of their former EHR systems to have access to old patient data entries.
Strategies to Address Interoperability Challenges
The first promising strategy requires substantial effort to be implemented. It involves collaborating with other healthcare providers and advocating for the use of the multinational corporation model to create common architectures that would tie fragmented systems together by introducing supranational players (Razzaque & Hamdan, 2021). Razzaque and Hamdan (2021) regard this course of action particularly helpful in relation to challenges that the International Organization for Standardization has not mitigated yet. The second strategy involves implementing collaborative health record (CHR) systems that integrate diverse providers’ work and are user-friendly enough to support patient engagement (Forchuk et al., 2020). CHRs are designed to maximize data exchange between providers and enable all physicians working with one patient to access and edit health-related information collaboratively (Forchuk et al., 2020). Thus, obstacles to interoperability can be addressed by healthcare organizations due to the emergence of new applications and organizational models.
Conclusion
To sum up, the opportunity to share patient data across diverse systems without losses and distortions is crucial to care quality and patients’ successful transitions between care facilities. Numerous challenges, ranging from fees for data transmissions between systems to terminology inconsistencies, continue to affect corporate EHR users. Possible strategies for overcoming these barriers include supporting the adoption of the multinational corporation model and implementing CHR applications.
References
Burrows, E. K., Razzaghi, H., Utidjian, L., & Bailey, L. C. (2020). Standardizing clinical diagnoses: Evaluating alternate terminology selection.AMIA Joint Summits on Translational Science, 2020, 71-79. Web.
Forchuk, C., Fisman, S., Reiss, J. P., Collins, K., Eichstedt, J., Rudnick, A., Isaranuwatchai, W., Hoch, J. S., Wang, X., Lizotte, D., Macpherson, S., & Booth, R. (2020). Improving access and mental health for youth through virtual models of care. In M. Jmaiel, M. Mokhtari, B. Abdulrazak, H. Aloulou, & S. Kallel (Eds.), The impact of digital technologies on public health in developed and developing countries: 18th international conference, ICOST 2020, Hammamet, Tunisia, June 24–26, 2020 proceedings (pp. 210-220). Springer.
Razzaque, A., & Hamdan, A. (2021). Artificial intelligence-based multinational corporate model for EHR interoperability on an E-health platform. In A. E. Hassanien, R. Bhatnagar, & A. Darwish (Eds.), Artificial intelligence for sustainable development: Theory, practice and future applications (pp. 71-81). Springer.
Zerden, L. D., Lombardi, B. M., & Richman, E. L. (2019). Social workers on the interprofessional integrated team: Elements of team integration and barriers to practice. Journal of Interprofessional Education & Practice, 17, 1-7. Web.