Healthcare Data Quality Management: The 7 Attributes of Quality Data

Data quality management is one of the most important data-related tasks in healthcare. Second only to data security, and even then, it could be a tie, data quality is the foundation upon which solid data-driven strategy and asset management can be built. In fact, it’s the groundwork for all quality improvement initiatives. If the data within your system lacks quality, the results you obtain from that data will be incomplete at best — detrimental, at worst. However, knowing you need to conduct data quality management and knowing how to define quality data are two different things. Traditionally, there are seven attributes of data quality that are understood across all data-related activities. Consider these attributes when ranking your own data for quality and integrity.

Accuracy in Data Quality Management

data quality management: accuracy

Accuracy is vital when you’re talking about data. It’s important to vet all incoming and stored data for accuracy on a regular basis. Inaccurate data can do a great deal of damage. From skewing results by a small margin to completely invalidating an entire data set, inaccuracies are the proverbial bad apple in the bunch. Make sure your data quality management procedures check for accuracy at every stage.

Data Validity

Similar to the way peer-reviewed studies are stronger sources than those without peer review, validated data is typically of a higher quality than data that hasn’t been through any kind of validation process. Make sure the data you utilize has been through its paces as far as error-checking, peer review, and factual cross-checking is concerned. Has it been replicated? Was it conducted by a reputable source? These are all important questions when it comes to data validation.

Reliability and Consistency

data quality management and reliability

Is the data reliable? Is it consistent, or do the results change depending on who’s utilizing or populating it? Data reliability and consistency are two extremely important attributes. They could be listed separately, but they’re almost always linked. Reliable data tends to be consistent, and consistent data is usually reliable. Strive for these two attributes in the data you populate, store, and use if you’re trying to achieve high levels of data quality management.

Relevance and Timeliness

data quality management and timeliness

Data that isn’t relevant or comes at an inopportune time doesn’t do a whole lot to effectuate progress and results. If the data you’re considering and evaluating for quality isn’t relevant to the task at hand or if it lacks any relevance at all, it’s probably not of a very high quality. Furthermore, the data has to be timely. Is it outdated? Does it apply to today’s patients or the population in question? Is it full of terminology from which society has moved on? It’s important to check for all these aspects when conducting data quality management tasks and vetting a piece of data for its level of quality.

Comprehensiveness 

Is the data comprehensive? In other words, does it provide an overview of the situation or give you complete information? Or is it only a small fragment of the overall story? Does it contain biases, or does it fairly represent all applicable angles? Checking to ensure that your data is comprehensive can go a long way to ensuring its quality.

Accessibility

Perfect data that nobody can get to or use isn’t very valuable. That’s why another attribute of high-quality data is its accessibility. If your organizational data is widely accessible by all members of your team or company and everyone is data literate enough to utilize it, then you’re on your way to excellent data quality management. If it’s siloed away or hard to access, that could be an area for you to look at and improve upon.

Originality

data quality management and originality

Finally, is your data unique? While data that confirms other sources or theories is always helpful, it’s also important to make sure that the data in question isn’t simply repeating what’s already known. Did the data you’re evaluating come from original research? Is it the product of unique methodologies and innovative studies? Or is it simply a regurgitation of existing information? Unique data that meets the other standards of quality is data that will act as a catalyst for progress and change. As such, it’s important to seek uniqueness and originality in the data you’re utilizing.

Conclusion

While there are certainly other attributes of quality data, these seven attributes tend to be the most common among best practices and healthcare literature dealing with data quality management. In any case, it’s a good place to start. Whenever you’re dealing with data, it’s crucial to ensure its quality. It doesn’t matter if you’re developing software, creating a new pharmaceutical product, or writing a report on epidemiological trends. If you’re using data, it has to be high-quality, and these seven attributes can provide a good checklist. Utilize it as a starting point for ensuring data quality management and build upon the list as you discover additional attributes relevant to your particular project. It takes time and effort to vet data for quality, but the end result is a safer, more effective, and more efficient healthcare system that can offer higher quality care as a result, and that’s worth it.