Week 10 Healthcare Data Standardization Type Your Name Here

Week 10healthcare Data Standardizationtype Your Name Heretype Your

Identify two or more issues with the existing healthcare data system, including concerns or problems highlighted in the case study. Recommend specific strategies for improving efficiency within the current system. Provide an overview of a nursing standard language used exclusively for nurses, such as NANDA-I, ICNP, NIC, or NOC, including a brief description, update frequency, and cross-mapping availability. Similarly, provide an overview of a multidisciplinary standard language such as LOINC, SNOMED CT, ABC codes, or CPT, with the same details. Create five open-ended questions to gather feedback from clinical staff on transitioning from the existing system to a new system, focusing on training, data migration, system downtime, and other relevant areas. Conclude with an analysis of Dow Chemical's organizational structure, discussing the reasons for adopting the matrix structure, its associated problems, and the reasons for its eventual abandonment in favor of a more streamlined, global divisional structure, considering the company's industry and competitive environment.

Paper For Above instruction

Healthcare organizations today face numerous challenges in maintaining efficient, accurate, and interoperable data systems. Existing systems often suffer from issues such as data silos, inconsistent terminology, and inefficient workflows that hinder clinical and administrative operations. Addressing these problems requires a comprehensive understanding of current systems, effective standardization strategies, and stakeholder engagement. This paper examines these aspects and provides insights into effective healthcare data standardization and organizational structures, drawing lessons from noteworthy corporate case studies like Dow Chemical’s restructuring efforts.

Existing System Issues

One of the primary issues with healthcare data systems revolves around data silos, where different departments or facilities operate autonomous systems that do not communicate effectively. Such fragmentation often leads to incomplete or inconsistent patient data, adversely affecting clinical decision-making (Bates et al., 2003). Additionally, inconsistent terminologies across systems contribute to errors, duplications, and delays. For example, different departments may use varied codes for diagnoses or procedures, making data sharing and aggregation difficult (Hersh et al., 2004). These issues are compounded by outdated workflows that rely heavily on manual data entry, increasing the risk of errors and reducing overall efficiency (Brennan et al., 2017).

Work-Arounds and Recommendations

To improve efficiency within the current system, adopting interim work-arounds focused on enhancing communication and manual processes is essential. For instance, implementing standardized data entry templates and regular staff training can reduce inconsistencies. Encouraging manual cross-referencing of codes and frequent audits can also mitigate errors temporarily (Fridsma et al., 2006). Moreover, establishing dedicated interface teams responsible for resolving system incompatibilities and facilitating real-time support can minimize downtime and data loss (Kellogg et al., 2016). These incremental improvements can serve as stepping stones towards comprehensive system overhaul or integration.

Overview of Nursing Standard Language

One prominent nursing standard language is NANDA-I (North American Nursing Diagnosis Association - International). NANDA-I provides a standardized taxonomy of nursing diagnoses, facilitating consistent communication about patient problems (Ackerman & McCaffrey, 2017). It is updated biannually, ensuring relevance to current nursing practices. The NANDA-I taxonomy is cross-mapped with other nursing terminologies such as NIC (Nursing Interventions Classification) and NOC (Nursing Outcomes Classification), supporting comprehensive care planning (Moorhead et al., 2018). These mappings allow for interoperability and integration with electronic health records, promoting a unified language across nursing documentation.

Overview of Multidisciplinary Standard Language

SNOMED CT (Systematized Nomenclature of Medicine—Clinical Terms) is a widely adopted multidisciplinary standard language that encompasses clinical terminology for diagnosis, procedures, and findings. SNOMED CT is updated quarterly to incorporate new medical knowledge and practices (Patterson et al., 2020). Its extensive hierarchical structure allows for precise data capture and semantic interoperability across various health information systems. Cross-maps exist between SNOMED CT and other coding systems like LOINC and CPT, enabling seamless data exchange and multi-institutional research (Bhattacharyya et al., 2021). This comprehensive terminology supports various healthcare functions, from documentation to decision support systems.

Staff Survey Questions

1. What challenges have you encountered during data migration from the old system to the new healthcare data platform?

2. In your opinion, what kind of training would best prepare staff for transitioning to the new system?

3. How can management support you in adapting to the new system, particularly regarding workflow changes?

4. What are your concerns about potential system downtime or technical issues during the transition period?

5. What suggestions do you have for ongoing support and updates once the new system is implemented?

Analysis of Dow Chemical's Organizational Structure

Dow Chemical initially adopted a matrix organizational structure to respond flexibly to both functional expertise and geographic markets. The matrix aimed to leverage the specialized knowledge of employees across functions such as R&D, manufacturing, and marketing while aligning with diverse regional and product-based business units (Bartlett & Ghoshal, 1989). However, the structure created significant challenges, including overlapping authority, turf battles, confusion over accountability, and decision-making delays. These issues are typical of matrix organizations, which often struggle with complex reporting lines and unclear authority gradients (Goold & Campbell, 2007).

As the global chemical industry became increasingly competitive and cost-driven, Dow shifted away from its matrix structure to a more streamlined, global divisional form. This transition was motivated by the need to reduce operational complexity, enhance cost control, and clarify accountability—vital factors in a fiercely competitive market (Daft, 2015). The new structure concentrated decision-making authority within distinct business divisions aligned with specific markets or products, enabling faster responses, reduced bureaucracy, and improved cost management (Cameron & Green, 2015). Given Dow’s focus on commodity chemicals where efficiency and low costs determine success, this organizational shift aligned well with industry demands and the company’s strategic objectives.

In summary, the move from a matrix to a divisional structure reflected a need for clearer lines of authority, operational simplicity, and cost leadership in an industry characterized by high price competition. The evolution exemplifies how organizational design must adapt to industry realities, balancing flexibility with control to sustain competitive advantage.

References

  • Ackerman, L., & McCaffrey, R. (2017). Nursing diagnoses: Definitions, proportions, and taxonomy. Journal of Nursing Scholarship, 49(2), 119-126.
  • Barlett, C. A., & Ghoshal, S. (1989). Managing Across Borders: The Transnational Solution. Harvard Business School Press.
  • Bates, D. W., et al. (2003). Ten commandments for effective clinical decision support. Journal of the American Medical Informatics Association, 10(2), 123-125.
  • Bhattacharyya, R., et al. (2021). Semantic interoperability in healthcare: A review of SNOMED CT and related standards. Healthcare Informatics Research, 27(4), 290-300.
  • Brennan, A., et al. (2017). Workflow analysis in health informatics: Improving efficiency. Journal of Biomedical Informatics, 67, 174-182.
  • Cameron, E., & Green, M. (2015). Making sense of change management. Kogan Page Publishers.
  • Daft, R. L. (2015). Organization Theory and Design. Cengage Learning.
  • Fridsma, D. B., et al. (2006). Interoperability of clinical systems: Key challenges and how to address them. Journal of Healthcare Information Management, 20(4), 25-32.
  • Goold, M., & Campbell, A. (2007). Desain Organisasi: Pengembangan Pemimpin dan Pengorganisasian untuk Kinerja. Gramedia Pustaka Utama.
  • Hersh, W. R., et al. (2004). Information technology in health care: An overview. Journal of the American Medical Informatics Association, 11(4), 289-293.
  • Hodgetts, R. M. (1999). Dow Chemical CEO William Stavropoulos on Structure and Decision Making. Academy of Management Executive, 13(3), 29-35.
  • Kellogg, M., et al. (2016). Supporting clinical decision support system implementation. Journal of Medical Systems, 40, 124.
  • Moorhead, S., et al. (2018). Nursing Outcomes Classification (NOC): Measurement of Outcomes for Nursing Practice. Elsevier.
  • Patterson, V., et al. (2020). SNOMED CT in clinical practice: A review of implementation and usability. Journal of Biomedical Informatics, 109, 103526.

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