Data preparation

To get the best mapping results, it's important to prepare your data prior to beginning the mapping process.

Data in the real world is rarely clean and tidy.

Data may have:

• an underlying data structure that could be represented with indents

• white space (leading, trailing)

• truncated text

• data types

• headers and footers

• non-text characters (? # / , - + * @ =)

• misspelling

• abbreviations

Even when data is coded, the data may not be as clean and tidy as expected.

Data could be:

• used out of context (repurposed fields)

• used as proxy – best/easiest closest thing

• underlying coding is often organic and uncontrolled:

Other data quality checks include:

• are all the terms uniquely identified?

• are there any duplicates?

• are there any null values?

• is there any meaningful metadata that needs to be accounted for?

All of these things should be considered, and rules should be developed and documented for how they will be handled to ensure consistency throughout the process and among personnel. Sometimes these decisions require expertise in workflow within the implementation and not just clinical expertise. For example: