by Sarah Mason
For any data project, collecting the needed facts requires research and placing into a file. Most times, this means either a spreadsheet or plain text file. Basic projects, such as recording sales inventory by transaction, are many lines, but small in size due to associated fields. building datasets for more complex problem solving often becomes large fast with many lines, or rows, and many fields, or columns.
A better way to handle working with these datasets is reducing the set or extracting the portion required for the specific task. Taking the Minimum Necessary does several helpful things:
- Makes the dataset take less space
- Takes less time to perform an analysis
- Controls what data is accessed
- Reduces distraction and instils goal-focus
- Maintains data-privacy and data-integrity
In Practice
Taking the subset, or only needed data, for the data project has many benefits. In practice, it may take time in the beginning to create a smaller set, but is a better choice. The original is preserved as a part of the data lineage. The dataset used is specific for each project and only what is needed.
Minimum Necessary
From HIPAA legislation, Minimum Necissary, is a feature of the act. The provision is to safeguard health information for all involved.
The minimum necessary standard, a key protection of the HIPAA Privacy Rule, is derived from confidentiality codes and practices in common use today. It is based on sound current practice that protected health information should not be used or disclosed when it is not necessary to satisfy a particular purpose or carry out a function. The minimum necessary standard requires covered entities to evaluate their practices and enhance safeguards as needed to limit unnecessary or inappropriate access to and disclosure of protected health information. The Privacy Rule’s requirements for minimum necessary are designed to be sufficiently flexible to accommodate the various circumstances of any covered entity.
Conclusion
Minimum Necessary is not just for healthcare. It applies to best methods for Data Practice. Better focus, quicker processing, less space, more safeguards for integrity and privacy are benefits for using subsets and extracts for data analysis.
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Sarah Mason is a Healthcare Data Analyst and Founder Sarah Mason Consulting LLC.
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