Tuesday, December 5, 2023

The Hidden Danger in Dirty Data

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 when it comes to the healthcare plans of the self insured. For Plan Sponsors, in particular, simply having access to data is not enough, the data must be cleaned, accurate, purged of error and subjected to meaningful analysis. Decisions made without this information can result in the implementation of health care plans that drain valuable financial resources in addition to not meeting the needs of specific employer populations and their families. However, with the right data and the right analytics tools, these critical insurance decisions can also result in intelligent plan design, cost savings and valuable opportunities to improve population health.


  • Do you have dirty data?
  • Best ways to keep your data clean.
  • How data quality makes a BIG difference.

Incomplete or incorrect data can skew processes and end up costing companies more money.

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 health plan management and utilization. But not all data is the same --“dirty” or incomplete, outdated, duplicate and incorrect data, creates confusion, slows efficiency, accumulates costs and leads to missed opportunities. This problem is prevalent across every industry , and it ends up costing employers millions.

In a perfect world, data should make things easier, saving money and streamlining processes. The first step to making data work for you is a thorough data audit to uncover deficiencies, duplicates and inconsistencies….. essentially, to find evidence of dirty data. Once clear errors have been found the work begins to identify solutions that will aid in the clean up, finally making the data itself reliable and useful. If you’re wondering whether or not your organization has low-quality data, consider:

  • Lack of consistency
  • Data silos
  • Missing information
  • Discrepancies
  • Duplicate/Disparate Entries

A data audit and cleanse is crucial to ensuring that your data is working as hard as it can for your organization. Image courtesy of Hippopx.


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. Useful tools, like Innovu’s Member Matching, ensure that data is sorted and managed effectively, removing costly duplicates due to spelling errors and/or siloed entry requirements.  

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. Developing a well thought out data management strategy-- as well as a way to implement it--is critical. Having a proactive approach means always engaging with your data so that you and your partners are 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

Having clean data is the starting block for companies looking to work diligently towards improving processes and find ways to save money. It is important for them to work with a data analytics provider that has the ability to cleanse, store and study the data. And will work with organizations and Plan Sponsors to 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.

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