Data Versus Information

Within the healthcare industry, much activity surrounds data collection; yet, most of it fails to yield good information. Nurses collect data every day and sometimes hourly. Examples of data include vital signs, weight, and relevant assessment parameters. Information or knowledge, however, provides answers to questions that guide clinicians to change their practices. For example, the trending of vital signs over time provide a pattern that may lead to certain clinical decisions. Many times healthcare professionals get caught up in the data gathering process rather than focusing on generating information that can be used to improve care (13). Keeping the goal/s of outcomes measurement as well as the quality of the data in mind helps to alleviate this common trap.

Data Integrity

The concept of "dirty" data is important to consider when implementing outcomes measurement. Data elements that are missing or incomplete, transcribed incorrectly, or inaccurately collected are considered dirty and lead to credibility problems. In essence, data are worthless unless clinicians believe they are accurate. Outcomes measurement is so important to healthcare decision making that ensuring data integrity must remain a top priority and everyone's responsibility.

Methods for ensuring data integrity include:

1. creating a flow chart to depict the flow of data from pre to post analysis (Figure 1)



2. reviewing data collection forms prior to data entry
3. assuming that data entry is error prone
4. providing a written data dictionary with specific operational definitions
5. implementing a thorough orientation program for data collectors
6. following a predetermined data audit and data cleaning process (inspect for outliers, missing
7. values; perform accuracy checks including ranges and transposition of digits)
8. completing descriptive analysis and checking for frequency distributions
9. assessing the data for systematic bias; evaluating the need for recoding
10. comparing results to historical data and clinical experience

When reporting the data it is important to state the exact mechanisms for ensuring data integrity so that readers are assured of the objectivity, validity and reliability of the data. Clinicians who can understand and trust the data are more prone to use the data in decision making!