The 5 Most Common Data Cleansing Mistakes
It’s very difficult to create a system of data entry and collection that is entirely mistake-free. When healthcare companies log thousands of patients and monitor hundreds of different metrics, errors in data sets are virtually inevitable. Thankfully, the process of data cleansing allows companies to identify, remove and/or amend inaccurate, incorrect, outdated or irrelevant pieces of data. While this sounds simple in theory, it can get complicated in practice. With that in mind, today we’ll examine some of the biggest data cleansing mistakes that healthcare payors and providers make –– so that you can avoid them at your organization:
Failing to Analyze the Data System
Before a professional can adequately solve a problem, they must first understand the source. As such, make it a point to analyze your current data collection methods and identify the causes of incorrect data. Determine the nature of these mistakes, whether they’re made by human entries or automatic processes, for instance. Trying to clean data without analyzing a system is akin to mopping a floor without fixing a leak in the ceiling above it.
Focusing on a Small Subset of Data
It’s typically not a good idea to try and clean just one subset of data. That’s because the problems that led to inaccurate data in one subset could very well have affected other areas of your data system. Don’t assume that amending one subset of data will completely clean your system.
Overlooking Duplicate Data
Removing duplicate data may seem like a simple fix, but it can prove quite monotonous and difficult if the data files are big enough. Consider investing in automated tools that will allow you to scrub duplicates from your files quickly and efficiently.
Cleaning Without Backing Up Data
As with any other digital process, it’s always recommended to save and preserve your original data sets before you begin to alter them in significant ways. The last thing any medical professional wants to do is restart the entire data cleansing process because of an avoidable mistake.
Many people may consider data cleansing to be a reactive step. However, professionals should use the information gleaned through the process to be proactive and to set up new measures to prevent inaccurate data in the first place. One of the easiest ways to do this is to communicate new standards with your team. Conversely, not communicating with your staff will allow the same mistakes and errors to occur again and again, which means your data won’t stay clean for long. Remember, maintaining good data hygiene is a continuous action.
Benefits of Data Cleansing
To close, it’s important to remember just how crucial data cleansing can be for healthcare payors and providers. Not only does data cleansing improve internal efficiency, help facilities cut costs, and save time, but cleaning data sets will help medical professionals provide better service to patients. Simply put, data cleansing allows doctors and other professionals to act with confidence to assist patients.
At Amitech, we have years of experience working with companies in the healthcare sector. And because of this, we understand the unique data collection challenges these organizations face. Thanks to our resources and hard-earned knowledge, we can consistently deliver data management solutions in this field. Contact us here for more information.