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.