Why Provider Data Quality Matters

Poor quality business data can often lead to suboptimal decision-making and inefficient processes, and could be costing the U.S. economy up to $3.1 trillion each year, according to an IBM study. On the other hand, data sets that contain accurate, complete and consistent data can have a major positive impact in the decision-making process. An MIT study found that companies who engage in data-driven decisioning improve outputs and productivity by five to six percent more than those who do not. In the healthcare industry, medical provider information is one type of data that can cause a big impact on claims organizations based on its accuracy. Provider data quality has been garnering a significant amount of attention lately, as the industry is spending $2.1 billion annually to maintain provider databases, according to CAHQ. Today, most Property & Casualty claims organizations’ provider databases contain inaccurate and incorrect information, causing both operational and financial impacts. Being able to monitor and maintain quality, error-free provider data can support claims organizations to see the full picture on a macro level to make informed decisions.

Provider Data Quality in the P&C Industry

While auto casualty and workers’ compensation claims organizations have relied on medical provider data to process claims for many years, the industry has only recently started to focus in on the quality of that data. For many claims organizations, it has started to become more apparent that provider databases are inundated with inaccurate and duplicate information, causing issues in their claims processes that impact loss and loss adjustment expenses. Some examples of provider data quality issues include:

• Duplicate Provider Records: If a provider presents himself on one bill as Joe Smith and as Joe A. Smith on another, some provider databases may record those as two separate provider records.
• Inaccurate Provider Demographic, Facility or Organization Information: Oftentimes, provider databases include incorrect demographic, facility and organization information in such data elements as first and last name, provider specialty, license number, National Provider Identifier (NPI), Taxpayer Identification Number (TIN) or address. This information could be captured incorrectly when entered into the claims system or could have been rendered inaccurate from their origination point.

Problems Poor Provider Data Quality Can Cause in the Claims Process

Poor quality provider data in the form of duplicate records or inaccurate and incorrect demographic and facility information can lead to operational inefficiencies and suboptimal claim outcomes. Here are a few examples of issues that occur as a result of poor provider data quality:

Inaccurate Provider Payments and Inefficient Processes

Poor provider data quality can lead to reimbursement losses, payments being sent to the wrong provider or in some instances, payments being sent to the same provider more than once. This can lead to a claims organization paying the same bill multiple times, which is very cost inefficient. To address this issue today, many claims organizations are setting up additional QA steps to ensure that there are no duplicate payments being made. These processes are often manual, costly and ineffective.

Uninformed and Inaccurate Decision Making

Inaccurate provider specialty information creates inaccurate adjuster decision making on bills. For example, this type of inaccurate information could lead to the wrong treatment cutoff point to trigger an IME.

Fragmented View of Provider Activity

Duplicate records and other inaccurate provider information can lead to a provider’s activity in a claim exposure being fragmented across multiple instances, making it difficult to track activity holistically. This can cause significant bottlenecking in carrier-provider interactions, for example, when providers call carriers to check in on their bill status, and uninformed suboptimal bill- and claim-level decision making. A fragmented view of provider activity can also obscure potential provider abuse and fraud, which according to the Coalition Against Insurance Fraud, costs the P&C industry about $34 billion in losses and loss adjustments each year. Without accurate provider data, it can be very difficult to identify patterns and outliers that can trigger fraud investigations to prevent this type of cost leakage.

Lack of Provider Analytics

Many claims organizations view provider analytics such as anomalous provider behavior detection as a vital tool to identify potential provider abuse and fraud. Due to poor quality provider data, however, most companies don’t have access to these types of analytics, which would give them more insight into provider activity to help them detect fraud and make better claims decisions.

Improving Provider Data Quality

Improving provider data quality can help claims organizations address these issues proactively and create a more efficient claims process that drives better outcomes. In the next article in the Provider Data Quality series, we will explain what claims organizations can do to start making provider data quality improvements.