Employee Benefits’ #1 Enemy - Dirty Data
It doesn’t matter how great your company’s benefits offering is for your employees if the data shared between platforms is dirty. What is dirty data? It can be anything from special characters unable to load to various systems (think accents, non-English letters such as ñ, or even hyphens) to bad structure data. Dirty data can cause employee benefit details to be rejected causing participants’ benefits coverage to be inaccurate or nonexistent in the insurance company’s system. Dirty data is most often a human error either due to keying incorrect information into the source system or coding errors in setting up enrollment files being sent. Employers are responsible for all dirty data, regardless of utilization of brokers or vendors. A simple google search of the cost of dirty data will reveal large sums of money being lost unnecessarily, including operational costs to resolve errors. Many employers do not consider the impact dirty data has on compliance expenses. Compliance penalties and fines accumulated from various government violations can easily bankrupt a smaller company, and result in significant wasted dollars for large companies. Most importantly, the enrollment errors impacting employees, and their dependents, can cause undue stress and frustration to participants at the worst times and result in distrust of employer benefit programs and insurance companies.
So, what can you do to remove the issues caused by dirty data?
Use automated files to complete updates as much as possible. Smaller companies may be required to maintain enrollment records manually as the cost of maintaining an electronic data interchange (EDI) may outweigh the benefit to insurance carriers and/or employers. Even if automated files are in place, it may be necessary to update a record manually, on occasion, in the event a participant reports an emergency need for coverage prior to the next expected file delivery. Manual entry automatically increases the potential for errors, and may also cause issues with duplicates if the detail entered manually doesn’t exactly match the system of record. The downstream issues caused by a manual keying error can be time consuming to correct and cause issues with claim payments and provider costs billed to employees. If manual entry is required, add steps to audit the information entered.
Address error reports timely. Every vendor (insurance company) produces error reports to notify the employer and/or their third party administrator (TPA) of errors produced when the file is loaded. It is common for these errors to go unresolved for extended periods of time due to competing priorities. Identify clearly who will resolve these issues in a timely manner. Vendors and TPA’s will often put the onus on the employer since the dirty data is most often caused by human resource information systems (HRIS) of record or personnel records owned by the employer. This is commonly referred to as “garbage in, garbage out” by all parties involved which points responsibility back to the employer. Depending on the severity of the error and the vendor system requirements, bad data may prevent any records from loading which impacts the full population versus only the records reporting with errors. Vendors will often assume employers are monitoring the files loading, which if not addressed, can cause long periods of time between valid updates and most often identified after it affects an employee or their dependents.
Audit data frequently to ensure all systems are in sync. At the very minimum, data should be audited after a company’s open enrollment is reported to a vendor and in the event of a large change affecting a data element or population, such as a structure change, an acquisition, or a union negotiation affecting benefit offerings. Depending on the size of your population, quarterly or monthly audits may be most useful to ensure data integrity and can be as simple as headcount audits to verify all systems match. If you are completing monthly self billing with your vendors or completing budget reporting to your finance department, you should be integrating audits for enrollment during these processes which will act as an automatic data audit and limit time needed to identify any issues allowing them to be resolved quickly. If you are not completing data audits at any other time, it is advisable to complete them prior to any applicable reporting to government agencies (for example, in the US Patient-Centered Outcomes Research Institute (PCORI) reporting, Affordable Care Act (ACA) filings, or the San Francisco Health Care Security Ordinance (HCSO)). Brokers and insurance companies will often request data reporting in advance of renewals or strategy planning which can also provide a beneficial time for integrated auditing.
Add required field attributes to data that has specific responses. As much as possible, key indicators used to drive data such as location or department should be limited to only valid entries to avoid any data discrepancies or typos. Core eligibility data should not be open to interpretation or variant responses. Most benefit systems have specific fields that must be populated in an exact format to load correctly. Most HRIS systems already have set up key attributes (for example state abbreviations or country codes) but not all fields are assigned automatically. The best example of this currently is sex assigned at birth. Many systems will reject any data element other than “M” or “F” in the standardized EDI 834 layout used to report health care enrollment information. Though some vendors have started accepting “other” or “unspecified” (identified as a “U” in the 834 layout) as an option for gender on EDI files, it will typically default to one gender or the other, most often male. This can cause issues with provider coverage and claims processing for the individual. If a company is collecting gender identity in their HRIS data, as well, it is best to store this in a separate field. Health care providers are still required to collect sex assigned at birth and is considered secure data and should be kept confidential. Though the 834 EDI layout doesn’t currently allow gender identity, most vendors now request a participant identify this detail when they register their account on the insurance website for personalization.
Avoid complex account structures. If the benefits for your company are fairly synchronized (meaning all employees are offered the same plans), simplify the account structures mapping to benefit plans built by your vendors as much as possible. On occasion, your finance department or C-suite may request benefit budget reports based on location, department, union status, administrative vs. manufacturing, or a plethora of other breakouts necessary to appropriately account for benefits’ spend. Work closely with your finance functions to identify group breakouts needed early on and partner with vendors to identify if additional demographic information provided between systems can be leveraged to identify group breakouts needed for financial reporting. Oftentimes, demographic fields shared between systems can be used to report necessary financial detail for internal teams without adding complex account structures adding a data element that has a high risk of being incorrectly mapped on EDI files. Also avoid getting too granular as if reporting groups are too small, privacy rules may restrict the type of reporting the vendor can provide to ensure protected health information (PHI) is secure. In addition, complex account structures can cause records to report invalid data elements if an employee does not fit mapping exactly. For example, if you are mapping account structure for an individual working in an administrative role reporting to a location and in a department that doesn’t have an exact match for mapping, systems will typically default them to a “catch all” account structure or not report any structure which will cause an error. Limiting account structure will also help save programming expenses as your company grows and expands, as well as prevent account structures to become stale in the event of closures or plan changes.
Dirty data can compound fast and cause access issues for your employees and their dependents as well as wasted time for your internal teams, but can also lead to complex compliance violations. Centurion At Work is happy to offer audit services to assist in verifying the integrity of your benefits data and to help with any of your benefits needs. Contact us today to learn more!
