Why Data Quality Matters in Healthcare

At it’s most basic, the term data quality refers to a measurement of how completely information actually meets the needs of a particular organization. In healthcare, that might meant that the information in question is complete and consistent. Most healthcare providers have specific regulatory rules that they have to adhere to, which makes the issue of data quality all the more vital.

Tracking data quality allows business organizations as well as public sector healthcare providers to see how much information they’re collecting and whether it can be used for a particular purpose. Data quality influences the relaibility of every digital workflow healthcare providers ever engage in, which make it every bit as important as it is contentious.

Insurance and care providers have to pay closer attention to data quality than almost every other kind of organization. Accuracy and consistency could both become potentially problematic areas because any mistake in a particular record could lead to a situation where someone might end up getting the wrong treatment. Integrity is also a big issue, since people who want to game the system in their favor could easily edit particular records in order to do so. The good news is that improving data quality is easier than you might think because of a few simple strategies.

Increasing The Quality Of Data

Timeliness is one of the first areas that information technology department staffers could theoretically fix with just a minimum of effort. Ensure that all members of a particular department have access to the information they need to do their jobs when they need it. Simply going through a database and being certain that everyone has the right account privileges for their particular position would go a long way toward improving this particular facet of the data quality practice.

Those who aren’t afraid to put more work into their data governance practices will want to take a look at the uniqueness of every entry in their databases. Organizations that have large numbers of unnecessary records can start to make poor decisions as a result. There’s also a number of performance-related reasons that they might want to eliminate extra ones. Deduplication can be sped up with certain utilities, though personnel should be encouraged to enter information carefully from the beginning so that this kind of thing never happens in the first place.

Validity and accessibility are the other areas where data quality suffer in perhaps the overwhelming majority of healthcare organizations. Clinical trials often suffer because the information they have on hand is seriously out of date. Researchers need access to all of the latest findings. Hospitals and laboratories that still rely on paper documents need to graduate to electronic ones as quickly as possible. They’ll also want to standardize coding and formatting guidelines as soon as they can.

While this last part might take more work than every other data-related chore, it’s the one that promises to save the greatest amount of time once done. In fact, some hospitals have found that it eliminated the need for periodic data cleaning operations since it enabled staffers to input information right from the start.