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.
In previous blogs, we’ve discussed Population Management concepts and given specific examples of pediatrics, ACO, and clinical populations. In this blog we turn to the Medicaid population. Medicaid has unique characteristics because of the nature of the financing and because of the social demographics of the population served. This population is going through big changes because of Medicaid expansion and the advent of programs that may resemble some of the characteristics of Medicaid, such as subsidized rates through the federal and state exchanges.
A key to analyzing this population is to create homogeneous sub-populations. There are several ways to define the sub-populations:
- Program – CHIP, TANF/AFDC, ABD (duals), Waiver programs
- Type of delivery model – Managed Care and Fee for Service
- County – Rates are typically defined at the county level for managed care and care delivery is organized around counties
- Special populations – Pregnant women, children, mentally ill
There are specific issues that drive the metrics and analysis for Medicaid recipients, including:
- High levels of emergency department (ED) use – This population has much higher ED usage rates which often reflect access and socio-demographic issues
- Maternity – This population has higher rates of maternity and has wider variability in maternity outcomes
- Behavioral Health – Managing behavioral health is a more important component of care
- Community Resource Access – Population wellness is much more dependent on additional community resources such as case workers, food banks and social workers
It’s important to focus on the key issues that drive the results for the population and to create metrics that reflect these key issues and characteristics. Below are some sample metrics that should be tracked and improvement goals developed.
In previous blog posts we have discussed the analytics needs for population health management. One significant population that has been historically underserved by analytics is the pediatrics population. Recently, there has been renewed interest in managing the care and cost of this population. Some of the interest has been generated by CMMI grants in managing pediatric care and the rapid expansion of children in the Children Health Insurance program (CHIP) and Medicaid expansion.
Defining the Pediatric Population
This is a relatively simple population to define, typically using birth date to define the population. Many define the population from birth to 18 years of age. Often the population is further sub-divided into infants (0 – 1 ages), children (2-12 years old) and adolescents (13-18 years old). Another typical method of sub-dividing the population is by payer and program, with Medicaid programs such as CHIP and TANF/AFDC and commercial payers. Other important ways of segmenting this population are by socio-economic status, geography and by disease state. Milliman has developed a tool that hierarchically assigns patients to a single pre-dominant disease (chronic condition hierarchical groups) and is currently developing a pediatric version of this analytic.
Pediatric Specific Analytics
Effective population health management requires a balanced set of metrics to profile and target improvements for the population. It requires healthcare quality, care delivery efficiency, patient safety, cost, and utilization metrics. The ability to benchmark the specific pediatrics population is also critical to be able to target, plan and measure the impact of interventions. Below is a sample of the metrics that should be available to manage pediatrics populations.
These metrics are examples of the tools that need to be available within analytic systems to be able to effectively manage pediatrics populations. It will become increasingly important to have specific measures that are relevant to the unique characteristics of populations that are being managed.