Data quality is a big deal for healthcare organizations. It is not just about FDA compliance or avoiding fines. The quality of data has direct and indirect impacts on running the business, whether it's revenue cycle management, patient satisfaction, physician referral/referral-to-payment splits, fraud detection using claims data, etc. Healthcare organizations are increasingly investing in technology to improve the quality of data in their systems. In this article, we debunk some common myths and misconceptions about healthcare data quality and offer practical solutions for improving it.
From an industry standpoint, data quality is a top priority. The healthcare world has been incredibly impacted by the addition of EHRs and other types of electronic health records (EHR), but as we've learned over the last decade or so since their implementation, not all is perfect in this brave new digital world. One major challenge that the industry has faced is the significant amount of failure that data quality processes have met. In fact, many organizations spend a lot of time and money automating their data quality processes only to find out that they don't work in real-life circumstances.
Don't get me wrong, automation should absolutely be on the list when it comes to implementing data quality processes, but it isn't the only thing that needs to be on the list. Humans are incredibly complex creatures, and we have millions of years of evolution working against us when it comes to understanding people. In many ways, computers are much better suited for tasks like data collection and analysis because they don't get tired, distracted, or bored with the process, and they don't make mistakes due to emotion or tone like we humans do.
If you spend any time at all at conferences like HIMSS, HIX, or some of the other regional data quality-related events, then you've probably heard many vendors talking about how their big data systems can improve the quality of your data. The problem with this approach is that it tends to work well for already large healthcare organizations with mountains of patient data but can be a little tougher for smaller organizations. It's not that this expensive technology doesn't work, it's just outside of the budget for many healthcare organizations, especially those with less than 50 employees or so.
In addition to the cost issue, there is also a need for customization to meet the specific needs of individual organizations. A tool that does everything an organization needs is rare, and the companies that make them are quite expensive. For small or medium-size organizations, it's important to choose a data quality technology solution that fits their current budget but also has some room for expansion as they grow. It's much better than choosing a system today with plans to upgrade later when the budget can fit it.
Many people think that data quality is all about perfection — and if it isn't perfect, then there's no point in doing anything. While I will concede that near perfection can be achieved with a fully automated approach, the reality is that most organizations simply can't afford to do that. In my experience, even approaching 90% accuracy is a huge win for most healthcare organizations due to the sheer volume of data they're managing and their staff sizes (many organizations still have less than 100 employees).
In many ways, this is like the previous myth. Many organizations believe that data quality doesn't matter if they don't need it, but that couldn't be further from the truth. When you have bad data in your systems, you can make poor business decisions based on these inaccurate results. For example, let's say your patient has a common name, but another patient with a similar name has recently been convicted of fraud. With bad data quality processes in place, your organization might incorrectly assume that they are the same person and see their claims as fraudulent. This is a huge problem because the claims used to support the patient's continued healthcare coverage could be denied even though it was an honest mistake!
This myth couldn't be further from the truth as most organizations have found out over time. In fact, data quality issues and delays cost non-IT departments billions of dollars every single year across most industries. Data quality problems are no longer just limited to business-to-business interactions; there are numerous examples where data quality issues and delays have caused huge problems for patients, including:
As you can see from these examples, poor data quality has the potential to cost your organization a hefty sum — and in some cases, even patient lives. While healthcare is still an inherently decentralized industry with many different stakeholders involved (and that won't change anytime soon), this doesn't mean that individual departments should turn a blind eye to data quality issues. The good thing is that people have become much more aware of the risks associated with poor data quality, and this has led to increased initiatives like patient-centered care, which puts a large emphasis on improving data quality processes.
While there is a direct correlation between data quality and both revenue growth and lower costs, many people don't see the value in improving their data quality processes. I've heard several times that it takes too much time or money to fix these problems, so organizations choose to ignore them. Naturally, this can have disastrous consequences for organizations that depend on accurate data to make strategic business decisions. Data quality issues also come with other costs such as decreased customer satisfaction and increased insurance premiums.
However, there are many benefits of improving data quality aside from the obvious cost-savings; here's a list of just a few of the many benefits that I've seen over my career:
The next logical question is how do you determine where to start with your improved data quality processes? Before making any changes, I'd recommend putting together a data quality analysis to gain a better understanding of the situation. The goal here is to figure out exactly what the biggest issues are and where you should focus your attention.
While there's no way to eliminate all data quality problems, it's important that your organization starts taking these issues more seriously before there's a serious problem. It takes time to improve data quality and as with any project, you need to be prepared for the tough times along the way; however, it will all be worth it in the long run.
I hope I've been able to shed some light on a few of the most common myths that have prevented many organizations from improving their data quality processes. It's time to get over these hurdles and start looking forward to more accurate reporting, reduced administrative costs, and higher customer satisfaction.