Common dimension of health cares data quality according to AHIMA are:
x Data accuracy. Data that reflect correct, valid values are accurate. Example of inaccurate data: typographical errors and misspelled.
x Data accessibility. Data that are not available to the decisions maker needing them are no use.
x Data comprehensiveness. All of the data required for a particular use must be present and available to the user.
x Data consistency. Quality data are consistent. Use of an abbreviation that has two different meanings provides a good example of how lack consistence can lead to problem. Example: CPR = Cardiac Pulmonary Resuscitation or Computer-based Patient Record.
x Data currency. Many types of health care data become obsolete after a period of time. A patients admitting diagnosis is often not the same as the diagnosis recorded upon discharge.
x Data definition. Clear definitions of data elements must be provided so that both current and future data users will understand what the data mean. One way to supply clear data definitions is to use data dictionaries.
x Data granularity. Sometimes is referred as data atomicity. That means the individual data elements in the sense that they cannot be further subdivided. Granularity is related to the purpose for which the data are collected.
x Data precision. Precision denotes how close to an actual size, weight, or other standard a particular measurement is.
x Data relevancy. Data must be relevant to the purpose for which they are collected. Example: we can collect very accurate, timely data about a patient’s color preferences or choice of hairdresser, but is this relevant to the care of the patient?
x Data timeliness. Timeliness is a critical dimension in the quality of many types of health care data. For example, critical lab values must be available to the health care provider in a timely manner. Not after the patient has been discharged.