HIM 220 Module Three Worksheet Final
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Apr 3, 2024
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Module Three Worksheet
Jill Farrington
Southern New Hampshire University
HIM 220 Module Three Worksheet Review the “Real World Case” involving a data dictionary on p. 58 of Introduction to Information Systems
for Health Information Technology. Then,
answer the following:
1.
What is the purpose of the data dictionary and why is it important to create one?
A data dictionary is a set of clear and easy-to-understand descriptions of data items that everyone in the system can refer to. It helps different methods to share and transfer information and organize using standardized definitions and data mapping. This makes searching for, identifying, and retrieving patient information easier. Data dictionaries are important because they ensure that data is consistent throughout the organization, which means the data quality is top-notch. By standardizing definitions, providing constant use, and controlling data quality, data dictionaries improve the quality, integrity, documentation, and analysis of data. In short, a data dictionary is essential to prevent duplication, errors, inaccuracies, and inconsistencies in data.
2.
How will the data dictionary help improve or address data consistency, data currency, data definitions, data granularity, data precision, data relevancy, and data timeliness?
Data consistency
–Maintaining data consistency is one of the critical benefits of a data dictionary. By providing standardized definitions and data mapping, a data dictionary ensures that the same data is represented consistently across all documents and systems, reducing the risk of irregularities and duplication.
Data currency:
Data currency is an essential aspect of maintaining high-quality data. It refers to the accuracy and timeliness of the data, ensuring that it is updated and up-to-date
with the latest information available. Organizations can make informed decisions based on the most accurate and relevant data by providing data currency. This is particularly important in healthcare, finance, and government industries, where decisions can have significant consequences. Therefore, maintaining data currency is crucial to ensure that the information used to make these decisions is reliable and trustworthy.
Data definitions:
Standardizing data definitions is an important aspect of preventing confusion. By creating a consistent set of definitions for data items, a data dictionary can help ensure that users across different systems understand and communicate about data
similarly. This streamlined approach enables easy search, identification, and retrieval of the correct information, ultimately improving data quality, integrity, documentation, analysis, and reuse. Additionally, data dictionaries ensure data consistency throughout an organization, preventing duplication, errors, and inconsistencies in information.
Data granularity:
Data granularity is an important aspect of maintaining high-quality data. It ensures that data is accurately and thoroughly documented, with attributes and values defined at the correct level of detail. All relevant information is captured and recorded, providing a comprehensive data picture. Organizations can improve data quality, integrity, and analysis by ensuring data granularity, ultimately leading to better decision-making.
Data precision:
Data precision is all about ensuring that data values are the right size to support the task. You don't want to use up more space or processing power than you need, but at the same time, you don't want to cut corners and end up with inaccurate calculations or processing errors. That's why a data dictionary comes in handy - it gives you clear guidelines on how precise your data needs to be for each element so you can make sure everything is just right.
Data relevancy:
Data relevancy is crucial for maintaining high-quality data. It ensures that collected data is valuable and meaningful for its intended purpose. A data dictionary provides clear guidelines for collecting and documenting only relevant data, making informed decisions more straightforward.
Data timeliness:
A data dictionary maintains high-quality data by standardizing definitions and mapping, ensuring consistency and accuracy across all documents and systems. It also provides accurate, up-to-date, and relevant data, enabling informed decisions. By streamlining data search and retrieval, it improves data quality and integrity. Implementing a data dictionary is imperative for making informed decisions with significant consequences.
3.
Do you think it is necessary for this healthcare facility to have a data warehouse? If so, why, and how, will the laboratory IS provide useful information to the data warehouse?
The healthcare facility should have a centralized database, as it can act as a repository for
data from various sources and can be accessed by different systems. This can assist the
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facility in data sharing, clinical and managerial decision-making, streamline workflow, and provide data for analysis. The laboratory's IS can provide valuable information, such as standard lab tests and the time it takes to generate results. This information can be stored in the data warehouse, enabling healthcare providers to anticipate when to expect a
test result and communicate this to the patient.
4.
How could data mining be applied to this scenario?
Data mining is all about digging deep into a database to find hidden links that might otherwise go unnoticed. In this case, data mining can help identify mistakes or abnormalities in the facility's database and then fix them. On top of that, it's possible to establish clear definitions of terms to prevent any misunderstandings.