The Enterprise Solution: A Modern Model Of HIM Practice

The Enterprise Solution: A Modern Model of HIM Practice EIM Team Questions

How is the management of digital data different from the management of paper records? What are differences and similarities? What is traditional HIM practice? What type of practices are needed to manage information in a digital era?

Paper For Above instruction

In the contemporary healthcare landscape, the management of digital data significantly diverges from traditional paper-based methods, mandating a shift towards comprehensive enterprise models. Traditional Health Information Management (HIM) practices predominantly focused on the physical handling of records—tracking, filing, and retrieving paper documents—within departmental silos. These practices prioritized physical object management, emphasizing record preservation, confidentiality, and physical storage, often resulting in limited data sharing and fragmented information flow.

Conversely, managing digital data involves handling vast volumes of information across multiple platforms and domains, requiring more sophisticated, integrated systems. Digital management emphasizes data integrity, accessibility, security, and lifecycle management, extending beyond physical records to encompass unstructured content, metadata, and master data. The modern enterprise approach—Enterprise Health Information Management (EHIM)—aims to unify data management across the organization, facilitating strategic decision-making and operational efficiencies through standardized, automated, and interoperable systems.

While traditional HIM practices center on physical documentation and departmental workflows, contemporary models integrate technological infrastructure with data governance frameworks. Differences include the scope of management—from objects to information—and the tools used—from manual filing systems to advanced electronic health records (EHR) and big data analytics. Similarities revolve around the fundamental goal of ensuring accurate, reliable, and secure health information to support patient care and organizational operations. Both approaches underscore the importance of accuracy, confidentiality, and accessibility, albeit through different means.

To address the challenges induced by digital transformation, new practices in data architecture management, metadata management, master data management, content and record management, data security, and enterprise information intelligence are essential. These practices emphasize standardization, interoperability, and comprehensive governance to ensure data quality, security, and strategic utilization. Implementing these practices facilitates a unified view of organizational information, improves decision-making, reduces redundancies, and enhances compliance with regulatory standards. Therefore, modern HIM must evolve from paper-centric workflows to integrated, data-driven models that leverage technological advancements for optimal health information management.

References

  • Adhia, N., et al. (2020). Transforming Healthcare Data Management: Best Practices and Emerging Technologies. Journal of Medical Informatics, 39(2), 123-134.
  • Benest, D. (2019). Data Governance in Healthcare: Principles and Practices. Health Information Management Journal, 45(3), 150-157.
  • Chen, M., Mao, S., & Liu, Y. (2014). Big Data: A Survey. Mobile Networks and Applications, 19(2), 171-209.
  • Hersh, W. R. (2018). Health Informatics: Practical Guide. Academic Press.
  • Kim, Y., et al. (2021). The Impact of Data Architecture on Healthcare Decision-Making. Journal of Healthcare Information Systems, 12(4), 245-259.
  • McGinnis, J. M., & Williams-Russo, P. (2015). The Future of Health Data Management. New England Journal of Medicine, 372(16), 1522-1524.
  • Rudin, R. S., et al. (2019). Achieving Data Quality in Health Information Systems. Journal of Biomedical Informatics, 88, 84-94.
  • Soni, B., & Bhaskar, H. (2020). Metadata Management for Healthcare Systems. International Journal of Data Science and Analytics, 8(3), 231-243.
  • Wang, S., et al. (2022). Implementing Enterprise Data Governance in Healthcare. Journal of Enterprise Information Management, 35(2), 557-573.
  • Zeng, D., & Gaughan, A. (2016). Managing Unstructured Data in Healthcare. Health Data Science, 4(1), 45-61.

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