Evaluate at least 2 managerial challenges related to clinical indices, databases, and registries from the perspective of a hospital’s health information management function. Recommend at least 2 best practices or policies related to the management of secondary data sources
With respect to data warehousing, appraise at least 2 approaches in data warehouse design that supports quality data management from varying sources, processing/storage of data throughout the warehouse model, and meaningful output into the presentation layer. Summarize your recommendations in the executive summary.
Analyze data and information from the point of creation or collection, through the management, storage, and transformation into information and duration of its required retention period
Cite your resources for each question. In text citation and in a listed reference sheet in APA format.
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Introduction:
As a medical professor in charge of creating college assignments and answers for medical college students, I understand the importance of clinical indices, databases, and registries in modern healthcare management. This essay aims to evaluate managerial challenges related to clinical indices, databases, and registries from the perspective of a hospital’s health information management function, recommend best practices or policies related to the management of secondary data sources, appraise approaches in data warehouse design that supports quality data management and meaningful output into the presentation layer, and analyze data and information from the point of creation or collection to its required retention period.
Question 1: Evaluate at least 2 managerial challenges related to clinical indices, databases, and registries from the perspective of a hospital’s health information management function. Recommend at least 2 best practices or policies related to the management of secondary data sources.
One of the significant managerial challenges related to clinical indices, databases, and registries from the perspective of a hospital’s health information management function is the lack of standardization. The absence of standardized terminologies and concepts across different databases and registries can lead to a mismatch of data, inconsistency in data reporting, and errors in data processing. Another challenge is the inadequacy of skilled personnel. The management of clinical indices, databases, and registries requires a highly specialized skillset, which may not be readily available in the hospital’s health information management function.
To address these challenges, two best practices or policies related to the management of secondary data sources are recommended. Firstly, there should be a standardized vocabulary, terminology, and concepts across all healthcare datasets. This approach will ensure the consistency of data reporting, matching of data, and data processing accuracy. Secondly, training and retraining of health information management professionals on data management and analysis are essential. By providing adequate training and retraining, the hospital’s health information management function can build a team of highly skilled personnel capable of managing clinical indices, databases, and registries efficiently.
Sources:
Dean, B. B., Lam, J., Natoli, J. L., Butler, Q., Aguilar, D., Nordyke, R. J., & Stephens, J. (2015). Review: use of electronic medical records for health outcomes research: a literature review. Journal of medical systems, 39(1), 1-13.
Sittig, D. F., & Singh, H. (2014). A new sociotechnical model for studying health information technology in complex adaptive healthcare systems. Quality & Safety in Health Care, 23(1), 3-9.
Question 2: With respect to data warehousing, appraise at least 2 approaches in data warehouse design that supports quality data management from varying sources, processing/storage of data throughout the warehouse model, and meaningful output into the presentation layer. Summarize your recommendations in the executive summary.
Data warehouses are essential tools for health information management. Two approaches that support quality data management are summarized below.
Firstly, the enterprise-wide data warehouse model is an approach that supports data management from varying sources. This approach involves consolidating data from multiple source systems into a single comprehensive data warehouse that can generate meaningful reports. The enterprise-wide data warehouse model improves data quality and accuracy by providing integrated, accurate, and consistent data across the organization. It also simplifies data structure and provides a unified view of data across the organization, making it easier to query.
Secondly, the star schema model is an approach that supports meaningful output into the presentation layer. This model organizes data by dividing the database into a fact table and a set of dimension tables. The fact table contains the measurements and processes, while the dimension tables contain data about the interpretation of the measurements. The star schema model is efficient and offers fast query response times. It is also easy to maintain and improves reporting while making it easy to understand and explore data relationships.
In summary, the enterprise-wide data warehouse model consolidates data from multiple source systems, while the star schema model organizes data to provide meaningful output to the presentation layer.
Sources:
Kimball, R. (2012). The data warehouse toolkit: The complete guide to dimensional modeling. John Wiley & Sons.
Agrawal, R., & Gupta, A. (2020). Star schema. Encyclopedia of Database Systems, 3299-3303.
