Data Confidence for ACOs

All healthcare data has data quality challenges. However, as Accountable Care Organizations (ACOs) have taken on more risk and are working on improving care processes data quality has become a more important issue. Below are some common data quality issues that ACOs face and some of the solutions ACOs can use to build confidence in their data.

Incomplete Provider Data

Provider level analysis is extremely important for ACOs. ACOs need to know which providers both within and outside the ACO network are providing services to patients attributed to the ACO. This is important not only to work towards bringing a larger percentage of services in-network (leakage management), but also for quality and efficiency improvement. There are multiple issues with provider data from payer data sources that can make it difficult to correctly identify in and out of network providers. These include:

• Claims missing complete provider information. Medical claims need to have both the billing and servicing/rendering providers listed, and pharmacy claims need to have both the prescriber and pharmacy listed. It is critical that ACOs work with their data suppliers to ensure that these multiple provider fields are complete on claims.
• Custom provider identifiers. Some data sources use custom provider identifiers, instead of National Provider Identifiers (NPIs). To perform analysis across data sources from different suppliers, any custom identifiers need to be cross-walked and mapped to a consistent standard such as NPI. For facilities or large practices, which generally have multiple NPIs or may use alternative identifiers, it is important to roll up the identifiers present in the data for analytic purposes.

Incomplete Financial Fields

Data suppliers often remove or mask financial data to ensure that provider reimbursement terms for providers outside of the ACO network remain confidential. Financial values are useful in ensuring that the data is complete, and are necessary to determine the magnitude of differences in resource cost between different services. A variety of tools, including MedInsight Global RVUs, . The conversion factor can be derived using benchmarks or by dividing the total cost of the contract by total RVUs. While this does not replicate actual unit cost it can provide reasonable approximations ACOs can use to make decisions.

Incomplete Diagnosis Coding

Many ACO contracts include financial parameters that are risk adjusted and it is important to have all diagnosis codes available for analysis, as these diagnoses drive risk scores. To test for the quality of the diagnosis coding in a given data source, users can audit both the number of codes per claims and the ACOs can use benchmarks to ensure that their claims have reasonable population of diagnosis codes and use other tools to review the consistency of diagnosis coding over time.

Completeness of Electronic Medical Record (EMR) Data

More analysis is being done using EMR data and combined EMR/claims data. In order to appropriately incorporate EMR data, the ACO needs to ascertain how much of a patient’s clinical care was delivered by providers using that EMR system, as clinical data from providers using alternative EMR systems would not be included in the data. It’s also important to gauge the relative quality of the EMR fields. An example is to measure the completeness of fields in the encounter file.

These are a few examples of the importance of ACO data quality and how ACOs can use analytics tools to improve data quality. As healthcare analytics continue to play an increasingly important role in decision-making, utilization and cost, ACOs will need to work closely with their data suppliers to continue to improve data quality.