Healthcare Data Quality

The challenge of healthcare organizations today is to implement information technology solutions that work to improve the quality of their care data. It is critical that the data and information be of high quality, the most sophisticated of information systems cannot overcome the inherent problems associated with poor-quality source data and data collection or entry errors. The data characteristics and frameworks presented here can be useful tools in the establishment of mechanisms for ensuring the quality of healthcare data.

Data Versus Information

What is the difference between data and information? The simple answer is that information is processed data. Therefore, we can say that healthcare information is processed healthcare data. Healthcare data are raw healthcare facts, generally stored as characters, words, symbols, measurements, or statistics. Data are the beginnings of information, we cannot create information without data (Lee, 2002). Healthcare data may describe a particular event, but alone and unprocessed they are not particularly helpful. Information is an extremely valuable asset at all levels of the healthcare organization. Healthcare managers, clinical staff, and others rely on information to get their jobs accomplished.

Ensuring Data and Information Quality

Healthcare decision makers rely on high-quality information. The issue is not whether the quality information is important but rather how it can be achieved. Before an organization can measure the quality of the information it produces and uses, it must establish data standards. That is, data can be identified as high quality only when they conform to a recognized standard. Ensuring this conformance is not as easy as it might seem because, unfortunately, there is one reason for this is that the quality of the data needed in any situation is driven by the use to which the data or the information that comes from the data will be put.

AHIMA Data Quality Management Model

The American Health Information Management Association (AHIMA) has published a generic data quality management model and an accompanying set of general data characteristics. Data quality management model consists of:

 Application. The purpose for which the data are collected.

 Collection. The processes by which data elements are accumulated.

 Warehousing. Processes and systems used to archive data and data journals.

 Analysis. The process of translating data into information utilized for an application.

The AHIMA data characteristics are:

 Data accuracy. Data that reflect correct, valid values are accurate.

 Data accessibility. Data that are not available to the decision makers needing them are of no use.

 Data comprehensiveness. Data required for a particular use must be present and available to the user.

 Data consistency. Quality data are consistent.

 Data currency. Many types of healthcare data become obsolete after a period of time.

 Data definition. Clear definitions of the data element must be provided so that both current and future data users will understand what the data mean.

 Data granularity. Individual data elements cannot be subdivided.

 Data precision. Precision denoted how close to an actual size, weight, or another standard a particular measurement is.

 Data relevancy. Data must be relevant to the purpose for which they are collected.

 Data timeliness. Producing data without timely manner may be of little or no value.

Information technology has tremendous potential as a tool for improving healthcare data quality. Clearly, electronic medical records (EMRs) improve legibility and accessibility of healthcare data and information. In systems requiring structured data input, data comprehensiveness, relevance, and consistency can be improved. Data precision and accuracy are improved when these systems also incorporate error checking.