Characteristics of Data Quality

Data precision

is the term used to describe expected data values. As part of data definition, the acceptable values or value ranges for each data element must be defined. For example, a precise data definition related to gender would include three values: male, female, and unknown. Or "Year of birth" should be recorded with a 4-digit numeric value such as "1965" rather than the shortcut "65".

Data currency/data timeliness

mean that healthcare data should be up-to-date and recorded at or near the time of the event or observation. Because care and treatment rely on accurate and current data, an essential characteristic of data quality is the timeliness of the documentation or data entry.

Data comprehensiveness

means that all the required data elements are included in the health record. In essence, comprehensiveness means that the record is complete. In both paper-based and computer-based systems, having a complete health record is critical to the organization's ability to provide excellent patient care and to meet all regulatory, legal, and reimbursement requirements.

Data comprehensiveness

means that all the required data elements are included in the health record. In essence,comprehensiveness means that the record is complete.

Data accuracy

means that data are correct. The data should represent what was intended or defined by the original source of the data. For example, the patient's emergency contact information recorded in a paper record or a database should be the same as what the patient said it was.

Data accessibility

means that the data are easily obtainable. Any organization that maintains health records for individual patients must have systems in place that identify each patient and support efficient access to information on each patient. Every health record system should allow record access 24 hours a day regardless of the format in which the record is stored.

Data consistency

means that the data are reliable. Reliable data do not change no matter how many times or in how many ways they are stored, processed, or displayed.

Data consistency

means that the data are reliable. Reliable data do not change no matter how many times or in how many ways they are stored, processed, or displayed. Data values are consistent when the value of any given data element is the same across applications and systems.

Data relevancy

means that the data in the health record are useful. The reason for collecting the data element must be clear to ensure the relevancy of the data collected.

Data granularity

requires that the attributes and values of data be defined at the correct level of detail for the intended use of the data. For example, numerical values for laboratory results should be recorded to the appropriate decimal place as required for the meaningful interpretation of test results—or in the collection of demographic data, data elements should be defined appropriately to determine the differences in outcomes of care among various populations.