No matter the industry, there will always be a need for access to good, quality data. Data and data analytics have become the driving force behind implementing new and successful changes in any business, and they are the starting point for reducing costs and improving efficiency. In the healthcare industry, in particular, having access to data is simply not enough, as the decisions made with this information can often result in the difference between life and death. In healthcare, it is essential to also have an effective data management process in place to ensure that your organizational data remains clean, accurate and useful.

So what can organizations do to keep their data clean? Luckily there are a variety of specific processes that companies can implement to keep their data as up-to-date, accurate,  actionable, and as helpful, as possible. :

• How do you know if you have dirty data?
• Best ways to keep your data clean
• Examples of how data quality makes a difference

What is Dirty Data?

How data can work against an organization

The need for good data as well as the implementation of useful analytics is well known throughout the business world, most especially in healthcare management. But not all data is the same --“dirty” or incomplete, outdated, and incorrect data, can create confusion, slow down efficiency, accumulate additional costs and lead to missed opportunities. This problem is prevalent across every industry , and it ends up costing employers millions.

Data is supposed to make things easier, saving money instead of creating more cost and more work. The first step to making data work for you is to do a data audit to uncover deficiencies, inconsistencies…..to find evidence of dirty data. Then the work begins to craft solutions that will clean it up, making it reliable and useful. If you’re wondering whether or not your organization has low-quality data, consider:

• Lack of consistency. 
• Missing information.
•  Discrepancies.‍
  

Best Practices to Clean Up Your Data

Meeting your data challenges

By simply analyzing and making adjustments to a couple of internal processes, an organization could potentially see better results and start to see that reflected in their cost savings. Data health is one major area where companies can see rapid results by making small changes. Having a plan for your data helps lay the groundwork for what you need it to do. Setting goals for your organization and understanding what data you need to meet those goals is crucial.

Make data cleaning a priority

When doing a data audit and cleanse, it is vital to get the support of all stakeholders involved in data collection, analyzation, and use. It is crucial that best practices, training, and data management are brought to the forefront of any changes that are considered and might be implemented. This ensures that everyone will be aware of any changes to the data collection process, and understands how crucial it is to maintain complete, clean records.

Establish policies

Having policies in place to streamline data management is one of the best ways to create a standardized process of documenting and tracking important data. Common terms and definitions are an easy way to ensure that information processing is done accurately and that it is accounted for. Establishing metrics to analyze data parameters or goals that work towards cost saving practices or data integrity, for example, are other ways to ensure the value of the company’s data is increasing.

Control the data

Additionally, by keeping all data within one system, companies have a better idea of how to make data work more effectively. Reports and metrics can be gleaned from data when it is sorted consistently and stored securely so all stakeholders have access to safe, reliable data.

Create a proactive approach

Data collection is ongoing, so make sure to always keep on top of processes and new ways to keep data cleansing as effective and efficient as possible. Having a well thought out strategy-- as well as a way to implement it--is critical, but it is equally important to have those who are working with the data to be fully onboard and realize that their efforts are valuable. Have a proactive approach means always engaging with your data so that you’re very familiar with it and working to continuously improve.

With the help of a data analysis firm, companies can quickly pinpoint problem areas and create customized solutions to properly store and manage data.

Examples of Data Quality and Proper Data Management

Healthcare data improvements will protect patients

As we’ve discussed, it is crucial for organizations to understand the need for good, quality data. Having clean data means your company is working diligently towards improving processes and finding ways to save money. If an organization realizes that they are working against low-quality data, it is important for them to work with a data cleansing provider who will create customized recommendations and offer advice on how to implement targeted solutions.

Data clean up can sound daunting, but it’s always helpful to see where organizations were able to implement a data cleansing process successfully.

According to the U.S. government, one of the biggest contributors to healthcare waste is patient misidentification. When patients are misidentified, it can create duplicate patient data, especially if patient data is garnered from various sources. The Department of Health and Human Services created a solution to this issue by implementing Patient Demographic Data Quality (PDDQ) which is a series of questions designed to help evaluate data quality standards and improve them. The results have shown that after implementing this process, hospitals have seen a noticeable reduction in duplicates.

Another area where data cleansing has helped improve healthcare processes and reduced cost is with prescriptions. Prescription errors have cost the healthcare system $21 billion per year, and can lead to patient death. Data analytics teams have been developing ways to work with patients’ electronic records to identify prescription errors, saving lives and reducing the chance for error.

While organizations such as OhioHealth have been collecting patient health data for years, they never consider how beneficial it would be to analyze it. Because this information was located in different silos, it was impossible to get a full picture analysis of their data. OhioHealth made the decision to store their information in a singular holding place, which would guarantee ease of access for analysis and reports. In the process of doing so, it created a data governing process to improve the quality of their data going forward.

Having access to quality data is the most effective way for organizations to ensure proper forecasting, as well as reduce overall costs. Don’t miss important opportunities for improvement and cost savings due to muddy data. If you find that your data is dirty, it’s time to clean it up so that you can start utilizing its full potential.