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How Bad Data Can Improve Healthcare Payor Efficiency

Aug 15, 2018

How Bad Data Can Improve Healthcare Payor Efficiency

data cleansing process

IBM finds that bad data cost the U.S. economy $3 trillion in 2016, limiting the decision making, efficiency, and ROI of organizations across industries. For healthcare payors, bad data has the potential to skew or misrepresent reports, causing members’ health milestones to drop and HEDIS scores to backslide. However, if your data cleansing process only scrubs poor quality data from your data lakes, then you are missing out on a hidden opportunity to improve data governance, quality care, and member outcomes.

Why Scrubbing Bad Data Limits Change

When dealing with raw and unstructured data, a certain amount of bad data is inevitable. Whether it’s erroneous, incomplete, or unsuitable for your purposes, poor quality data is bound to occur due to common inputting mistakes or inaccurate data rules logic. Through the sophistication of today’s data cleansing tools, administrators and users have the capability to amend or correct bad data files effortlessly. Yet using the data cleansing process in this way is often shortsighted, unless businesses take vital preliminary steps to ensure that data quality management improves over time.

The reality is that scrubbing bad data ignores the root cause behind poor data quality. If bad documentation or inaccurately coded records routinely come from a specific individual or department, deleting that information enables inaccuracies to continue unchecked. If a platform continues to funnel improper data into your reports, scrubbing fails to track down the origin of the problem within data source routines or logic. In every scenario, the data cleansing process only alters data quality on a localized level, sabotaging the systemic changes necessary for long-term, effective data governance.

How to Use Your Bad Data to Improve Data Governance

Before you can leverage bad data to your advantage, you need to identify which data sources are poor quality. The volume and variety of data contained within the average data lake is staggering, and parsing all those disparate sources without an enterprise data quality tool is impractical and time-consuming. The data profiling capable through these tools enables healthcare payors to uncover and prioritize inconsistencies within data sources through sophisticated algorithms.

Identifying poor quality data sources is only the beginning. Here are the strategies that healthcare payors can use to improve their overall data governance:

  • Increase Data Transparency – Key metrics and advanced analytics reports are more powerful when the organization operates with the support of a sound data governance operating model and associated committee of data owners, data stewards, data custodians, and other decision makers is formed. By circulating findings about bad data to this governance committee, you provide them with the insight to implement change top down and enterprise wide within your organization. In this case, data transparency is most effectively shared with individuals who can respond to reports of bad data and make changes that actually improve the way data is gathered, coding is documented, and care outcomes are achieved.
  • Pursue Continuous Improvements – Finding bad data is only the start of the process. A large part of effective data management solutions is implementing data quality efforts so that the data itself is cleaner and an extensive data cleansing process is no longer needed. As part of a data quality center of excellence (COE), this requires both creating processes to eliminate the margin of error and remaining on the lookout for other opportunities to improve data gathering practices.

Want to learn more about how to move beyond the data cleansing process and improve data gathering? We specialize in helping healthcare payors to improve data management and analytics. Contact us today!

 

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