Health plans in the United States lose billions of dollars every year due to data quality errors. This highlights the need for health plans to detect and correct data errors before they cause downstream losses. Traditionally, health plans have used deterministic rule sets to detect data errors throughout their operations. However, this approach is now obsolete given that processes and systems have grown much more complex and data volumes have increased exponentially.
Data errors cause a myriad of problems for health plans, many of which have major financial and reputational consequences. Incorrect or missing information regarding a provider in a health plan’s directory can result in surprise billing, member abrasion, provider abrasion, and compliance fines. Similarly, data errors that result in the incorrect adjudication of a claim also has direct and significant impact. A solution that can proactively detect and correct data errors across the enterprise is critical.