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A Beginner's Guide to Scrubbing Data

Amitech Solutions

woman scrubbing data in a databaseEveryone has heard the axiom, “numbers don’t lie.” While this certainly makes sense on a hypothetical level, the truth is that sometimes statistics and data are just plain wrong. Indeed, many healthcare organizations have had to deal with incorrect, incomplete, inconsistent, duplicate, and outdated data for years. As one can imagine, “dirty data” can be extremely problematic for any business, but especially so for health payors and providers. Not only can incorrect data lead to inefficiencies and poor patient interactions, but, in some instances, it can contribute to negative patient outcomes. Thankfully, it is possible for healthcare organizations to engage in regular data cleansing to prevent any of that from happening. To that end, here is our beginner's guide checklist for scrubbing data:

1. Conduct Regular Audits

No system of collecting and processing data is ever 100% foolproof. So even if your company has recently installed new programs or new software, it’s still crucial to conduct regular data scrubbing audits. Healthcare providers and payers depend on reliable data in order to operate at peak effectiveness. And even small compromises in data integrity can cause big problems. If your organization doesn’t currently engage in regular data scrubbing, make it a point to change that policy ASAP.


2. Utilize AI & RPA Correctly

Artificial intelligence (AI) and robotic process automation (RPA) are both extremely useful tools that healthcare professionals can use to ensure the quality of their data sets. However, they should not be used to completely replace the role of humans in data scrubbing. Keep in mind that RPA tools in particular need to be monitored closely. Yes, professionals can use RPA to tackle a long list of simple, repetitive tasks, but RPA alone cannot perform proper data cleansing. The same basic principle also holds true for AI applications –– despite the fact that AI is sometimes considered more sophisticated than RPA.


3. Think About the Big Picture

Sometimes professionals think of data scrubbing solely in terms of removing inaccurate or outdated data from their files. While this is certainly a big aspect of data cleansing, it should also include finding ways to add new and relevant information as well. Additionally, quality data scrubbing should seek to identify the sources of data error. Remember, incorrect data could occur for a number of reasons, which is why it’s key to diagnose the root of the issue. Fixing a raft of internal data mistakes now only to face the same problem a few months down the line is far from ideal.

Though data scrubbing does involve the process of correcting specific information, analysts should not lose sight of the big picture either. This means that data cleansing may require any number of initiatives to put right human error and to update old systems. Lastly, data scrubbing should cover the entirety of your system and all of your programs. Isolated issues do occur, but making that assumption without checking can be very detrimental.

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Clean data forms the bedrock of a wide range of important initiatives that healthcare and medical facilities undertake every day. Keeping your data clean and up-to-date should be a top priority. The good news is that our team at Amitech can help you do just that. We are experts in data collection, analysis, and quality control, and we can assist your data scrubbing efforts to protect the performance of your organization. Contact us here to learn more or to get started today.

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