The Use of Data Analytics to Reduce Compliance Risk and Enhance Revenue
Hospitals are being subjected to increasing audits and reviews by Medicare and contractors, as well as other payers and regulatory agencies. The advantage those reviewers have is the power of data analytics, or ‘data mining’, which translates into inconsistencies, the ability to use a hospital’s claims data to identify potential vulnerabilities and compliance problems.
By using claims data analysis these reviews identify errors, trends, and other issues readily identified, thereby allowing for prospective resolution before any significant repayments or even identifying trends of concern. Most hospitals and other health care providers don’t have the tools or dedicated resources to do comparable analysis and get ahead of the Government and other auditors. As a result, the errors are only identified once the audits are done and repayments made. As the hospital focuses on fixing those issues, the auditors have moved onto others.
In addition, the inability to conduct data analysis can cause lost revenue when billing opportunities are missed and money is left on the table. With the challenging regulations and intense audit environment, health care providers absolutely need to create a level playing field and know their own data.
The Solution
Kohler Healthcare Consulting utilizes a data analytics tool that can identify regulatory risks lurking in your claims data, and can also identify potential lost revenue. This tool, referred to as the Balanced Analytic Revenue and Compliance Reporter (BARCR), allows us to analyze the risks for lost income and compliance concerns present in your 837 claims files. This tool, combines with edits from all MACs, RACs, CCI, clinical indicators and OIG focus issues, and MEDPAR data to analyze individual data fields and the claims in total (services and diagnoses). It includes comparison of your data to the data of peer organizations to identify outliers and trends. It helps point to the direction of actions needed in a time of limited resources.
