What Can Health Care Teach Us About Data Quality?

Blog Administrator | Address Quality, Analyzing Data Quality, Data Management, Data Quality | , , , , , ,

By Elliot King

Data quality issues are more acute in health care than in perhaps any other industry sector. According to a seminal study by the Institute of Medicine, (IOM) preventable medical errors are responsible for nearly 100,000 deaths annually, making it the sixth leading cause of death in the United States. These errors cost $98 billion annually.

Of course, not all of these errors reflect problems in data, but many do. And
sometimes, data errors seems astonishing and the outcome devastating. According
to the IOM, as many as 40 wrong sites, wrong side, wrong patient procedures
occur every week. For example, a surgeon will amputate a person’s right leg
instead of the left or remove the gall bladder instead of a kidney. This is a
“data error” of the most grievous kind.

Archaic paper record keeping has long been cited as a source of cost and a cause
of medical errors. With paper records, if a patient is treated by more than one
doctor, each doctor may not have read what the other doctors are doing. And that
is not good.

But the aggressive move to electronic health records (EHR) has its own risks as
well. Mistakes in patient data can follow the patient from doctor to doctor.
Moreover, some programs allow health care providers to auto-fill fields, making
it appear that they performed a more thorough examination, let’s say, than they
actually did.

The issue of the quality of the data used in EHRs has been thrown into the
spotlight by efforts to reuse EHR information for clinical research. Within the
medical community, most practitioners agree that clinical data is not recorded
as carefully as research data. So researchers studying the potential of using
EHRs for research measure quality by the characteristics with which most data
quality professionals are familiar–completeness, correctness, timeliness, and so
on. They assess those characteristics using multiple methods including
comparisons to established standards, data element agreement, data source
agreement and others.

Unfortunately, the early results of data quality assessment for EHRs are not
very encouraging. Some studies have indicated that the introduction of EHRs does
not lead to higher quality data being gathered, but just larger quantities of
bad data. Findings like that have triggered a spirited debate in the medical
community, with some arguing that the experience with EHRs demonstrate the first
law of informatics–that data should only be used for the purpose for which is
was collected.

— For more info on data quality and the healthcare industry,
download our free
on “Data Quality Is Good Medicine for Healthcare Providers.”