Developing an electronic health record system for kidney patients: A case study in Benghazi hospitals

Salwa Suleiman Ibrahim; Ahmed M Altriki; Ismail Essam Aldoukali; Miftah Nasser Almeslati; Mohamed Mustafa Salem Abushalaa

المؤلفون

Salwa Suleiman Ibrahim Department of Information System IT College, University of Benghazi Benghazi, Libya المؤلف
Ahmed M Altriki Department of Information System IT College, University of Benghazi Benghazi, Libya المؤلف
Ismail Essam Aldoukali Department of Information System IT College, University of Benghazi Benghazi, Libya المؤلف
Miftah Nasser Almeslati Department of Information System IT College, University of Benghazi Benghazi, Libya المؤلف
Mohamed Mustafa Salem Abushalaa Department of Information System IT College, University of Benghazi Benghazi, Libya المؤلف

الكلمات المفتاحية:

Electronic Patient Record History (EPRH)- Healthcare Information Systems- Digital Health Records-Clinical Decision Support-Patient Satisfaction - Data Security and Privacy-Health Information Technology-System Implementation Challenges

الملخص

The implementation of Electronic Patient Record History (EPRH) systems marks a pivotal advancement in modern healthcare, offering improved operational efficiency and clinical performance through the digitization of traditional paper-based records. These systems provide healthcare professionals with streamlined access to patient data, facilitating timely and evidence-based decision-making while minimizing the risk of information loss and reducing administrative costs. As a result, EPRH contributes significantly to the financial sustainability of healthcare institutions. EPRH platforms are equipped with advanced data analytics capabilities that support clinical research and enable the identification of health trends across patient populations. Empirical evidence suggests that EPRH systems contribute to higher levels of patient satisfaction, largely due to enhanced communication and engagement throughout the treatment process. Furthermore, EPRH strengthens the collaboration between physicians, care teams, and patients, ultimately leading to improved health outcomes. Notably, 85% of surveyed healthcare professionals reported that EPRH enhances the quality of care by ensuring the availability of accurate and timely information. Despite these benefits, challenges remain regarding data privacy and system complexity. Healthcare organizations must ensure strict compliance with legal and ethical standards to protect patient confidentiality. Approximately 50% of respondents identified system complexity as a barrier, emphasizing the need for comprehensive training programs. Additionally, 65% expressed concerns about data security and patient privacy, highlighting the importance of implementing robust protective measures. Nonetheless, the overall perception of EPRH remains positive, with 74% of participants supporting the transition from paper-based to electronic records .

التنزيلات

تنزيل البيانات ليس متاحًا بعد.

المراجع

Halpern, Y., Horng, S., Choi, Y., & Sontag, D. (2016). Electronic medical record phenotyping using the anchor and learn framework. *Journal of the American Medical Informatics Association*, 23(4), 731–740.

Beeksma, J., et al. (2019). Life expectancy prediction from EMRs using machine learning and NLP. *BMC Medical Informatics and Decision Making*, 19(1), 1–10.

Koopman, C., Jones, P., Simon, V., Showler, P., & McLevey, M. (2021). The role of medical records in data-driven medicine. *Health Informatics Journal*, 27(3), 1–15.

Lorenzi, N. M., Riley, R. T., & Blyth, J. C. (2000). Managing change: An overview. *Journal of the American Medical Informatics Association*, 7(2), 116–124.

Mami, M., Altriki, A., & Sallabi, O. (2021, October). Possibilities of Applying the Blended Learning Approach in the Faculty of Information Technology. In The 7th International Conference on Engineering & MIS 2021 (pp. 1-5).

Showler, P., Simon, V., & McLevey, M. (2021). Data-driven healthcare: Challenges and opportunities. *Health Policy and Technology*, 10(2), 1–8.

Future Work

The findings of this research open several avenues for future work in the field of Electronic Patient Record History (EPRH) systems and Health Information Technology (HIT). Longitudinal studies could be conducted to measure the long-term impact of EPRH systems on healthcare delivery and patient outcomes, providing insights into how these systems evolve over time and their sustained effectiveness. Additionally, exploring the integration of advanced technologies such as artificial intelligence (AI) and machine learning (ML) within EPRH systems could enhance data analytics and decision-making processes.

Focusing on user experience (UX) studies to understand healthcare professionals' interactions with EPRH systems is crucial, as identifying pain points and areas for improvement can lead to more user-friendly interfaces. Comparative studies between different healthcare settings (e.g., urban vs. rural, public vs. private) can assess how context influences the implementation and effectiveness of EPRH systems.

Investigating the implications of EPRH systems on healthcare policies and developing guidelines to standardize practices across institutions will ensure compliance with privacy laws.

Furthermore, exploring strategies to enhance patient engagement with EPRH systems is vital for improving healthcare outcomes. Studying the integration of EPRH systems with telehealth platforms can provide valuable insights into improving access to care, especially in underserved areas. Lastly, researching the scalability and adaptability of EPRH systems in various healthcare environments, particularly in low-resource settings, will be essential for broader adoption. By pursuing these future research directions, stakeholders can continue to enhance the effectiveness and efficiency of EPRH systems, ultimately improving the quality of healthcare delivery.

References

Halpern, Y., Horng, S., Choi, Y., & Sontag, D. (2016). Electronic medical record phenotyping using the anchor and learn framework. *Journal of the American Medical Informatics Association*, 23(4), 731–740.

Beeksma, J., et al. (2019). Life expectancy prediction from EMRs using machine learning and NLP. *BMC Medical Informatics and Decision Making*, 19(1), 1–10.

Koopman, C., Jones, P., Simon, V., Showler, P., & McLevey, M. (2021). The role of medical records in data-driven medicine. *Health Informatics Journal*, 27(3), 1–15.

Lorenzi, N. M., Riley, R. T., & Blyth, J. C. (2000). Managing change: An overview. *Journal of the American Medical Informatics Association*, 7(2), 116–124.

Mami, M., Altriki, A., & Sallabi, O. (2021, October). Possibilities of Applying the Blended Learning Approach in the Faculty of Information Technology. In The 7th International Conference on Engineering & MIS 2021 (pp. 1-5).

Showler, P., Simon, V., & McLevey, M. (2021). Data-driven healthcare: Challenges and opportunities. *Health Policy and Technology*, 10(2), 1–8.

Future Work

The findings of this research open several avenues for future work in the field of Electronic Patient Record History (EPRH) systems and Health Information Technology (HIT). Longitudinal studies could be conducted to measure the long-term impact of EPRH systems on healthcare delivery and patient outcomes, providing insights into how these systems evolve over time and their sustained effectiveness. Additionally, exploring the integration of advanced technologies such as artificial intelligence (AI) and machine learning (ML) within EPRH systems could enhance data analytics and decision-making processes.

Focusing on user experience (UX) studies to understand healthcare professionals' interactions with EPRH systems is crucial, as identifying pain points and areas for improvement can lead to more user-friendly interfaces. Comparative studies between different healthcare settings (e.g., urban vs. rural, public vs. private) can assess how context influences the implementation and effectiveness of EPRH systems.

Investigating the implications of EPRH systems on healthcare policies and developing guidelines to standardize practices across institutions will ensure compliance with privacy laws.

Furthermore, exploring strategies to enhance patient engagement with EPRH systems is vital for improving healthcare outcomes. Studying the integration of EPRH systems with telehealth platforms can provide valuable insights into improving access to care, especially in underserved areas. Lastly, researching the scalability and adaptability of EPRH systems in various healthcare environments, particularly in low-resource settings, will be essential for broader adoption. By pursuing these future research directions, stakeholders can continue to enhance the effectiveness and efficiency of EPRH systems, ultimately improving the quality of healthcare delivery.

References

Halpern, Y., Horng, S., Choi, Y., & Sontag, D. (2016). Electronic medical record phenotyping using the anchor and learn framework. *Journal of the American Medical Informatics Association*, 23(4), 731–740.

Beeksma, J., et al. (2019). Life expectancy prediction from EMRs using machine learning and NLP. *BMC Medical Informatics and Decision Making*, 19(1), 1–10.

Koopman, C., Jones, P., Simon, V., Showler, P., & McLevey, M. (2021). The role of medical records in data-driven medicine. *Health Informatics Journal*, 27(3), 1–15.

Lorenzi, N. M., Riley, R. T., & Blyth, J. C. (2000). Managing change: An overview. *Journal of the American Medical Informatics Association*, 7(2), 116–124.

Mami, M., Altriki, A., & Sallabi, O. (2021, October). Possibilities of Applying the Blended Learning Approach in the Faculty of Information Technology. In The 7th International Conference on Engineering & MIS 2021 (pp. 1-5).

Showler, P., Simon, V., & McLevey, M. (2021). Data-driven healthcare: Challenges and opportunities. *Health Policy and Technology*, 10(2), 1–8.

C. Koopman, P. Jones, V. Simon, P. Showler, and M. McLevey, "The role of medical records in data-driven medicine," Health Informatics Journal, vol. 27, no. 3, pp. 1-15, 2021. doi: 10.1177/14604582211029130.

N. M. Lorenzi, R. T. Riley, and J. C. Blyth, "Managing change: An overview," Journal of the American Medical Informatics Association, vol. 7, no. 2, pp. 116-124, 2000. doi: 10.1136/jamia.2000.0070116.

Patient satisfaction with electronic medical records, Journal of Healthcare Quality, vol. 40, no. 5,

pp. 290-297, 2018.

P. Showler, V. Simon, and M. McLevey, "Data-driven healthcare: Challenges and opportunities," Health Policy and Technology, vol. 10, no. 2, pp. 1-8, 2021. doi: 10.1016/j.hlpt.2021.100512.

S. Z. Strasser, J. G. Klann, and K. B. Wagholikar, "EMR data integration for clinical decision support," Journal of Biomedical Informatics, vol. 100, pp. 1-10, 2019. doi: 10.1016/j.jbi.2019.103318.

The future of electronic health records in developing countries, IEEE Access, vol. 9, pp. 12345- 12356, 2021. doi: 10.1109/ACCESS.2021.3067890.

The role of artificial intelligence in healthcare, IEEE Intelligent Systems, vol. 36, no. 5, pp. 1-10, 2021. doi: 10.1109/MIS.2021.3102998.

M. Tominanto, E. Purwanto, and N. Yuliani, "Outpatient electronic medical records software (PHP/MySQL)," International Journal of Advanced Computer Science and Applications, vol. 9, no. 12, pp. 1-7, 2018. doi: 10.14569/IJACSA.2018.091201.

M. Tominanto, "Predicting mortality after COVID-19 using EHR data and age-stratified generalized linear models," Journal of Medical Systems, vol. 44, no. 11, pp. 1-9, 2020. doi: 10.1007/s10916-020-01660-0.