How more complete, accurate clinical data can improve benefit design
Last year, employers reported the largest average increase in per-employee healthcare costs since 2010, according to a survey by Mercer. The dramatic 6.3% growth, to $14,542, followed an increase of just 3.4% in 2020, giving rise to speculation that people were rescheduling care they had put off during the pandemic.
As of 2019, employer-sponsored health plans covered nearly 155 million Americans. No matter how costs trend for employers in the future, it’s clear that providing the right benefits for employees will only become more challenging.
As benefit design for enrollment planning is finalized, these statistics point to a critical need for payers to ensure that self- and fully-insured employers have accurate healthcare analytics derived from highest-quality healthcare data.
Trustworthy data gives employers a competitive advantage, because they can use it to design highly targeted benefit programs specific to their employee populations. These programs can help employers to lower claims costs, improve their employees’ health, and reduce health-related absenteeism.
“Effective population health management requires an in-depth look at the population’s current health, risk factors, patterns of care and a better understanding of the social determinants of health. Data analysis is key,” writes Dr. Fred Brodsky, vice president of population health and clinical integration at Aurora Health Care.
There’s no shortage of data in healthcare — which is great for employer groups that want to design customized healthcare benefits for their employees.
Let’s say an employer wants to support employees who suffer from diabetes and periodontal disease. By cross-checking medical and dental claims data, payers can identify instances where an employee missed a treatment or cleaning. Knowing this information would allow a case manager to follow up and encourage the employee to schedule a visit with their dentist or physician.
Typically, payers can get that data from a variety of sources:
- Prescription records, which are valuable for two important reasons:
- This information usually is reported soon after a prescription is filled, so it reflects current conditions, whereas many health data sources are dated by the time they are reported.
- People tend to adhere to their medication regimens.
- Medical and dental claims
- Demographics, such as age, gender, ethnicity, address, allergies, and general medical history
- Health screenings
- Biometric screening data, which includes weight, cholesterol levels, blood pressure, blood glucose levels, and other biological measurements
- EMR/EHR data
But, while there is plenty of data, the problem lies in its quality rather than its quantity. More than half of the benefits leaders responding to a survey by Artemis Health said they don’t trust the accuracy of the data they receive from payers or other vendors.
The trouble starts upstream when data is gathered without correct standardization before being shared and analyzed. Common problems include mismatched patients between different clinical data sources; incorrect employee names, birthdates, and Social Security numbers; fields left blank; or data that is duplicative, inaccurate, or organized with non-standard code sets. That, in turn, leads to problems with patient medical records — a phenomenon known as “garbage in, garbage out.”
Payers clearly want to do something about this faulty data. The same Artemis survey found that two-thirds of HR benefit leaders planned to increase investments in healthcare data analytics in 2021, while one in five cited the need for data enrichment to help them drill down on opportunities to reduce spending or improve employee risk scores.
Payers have several options to fix and clean the data sets, but they all cost time and money for labor and software that payers may or may not want to spend.
An easier remedy is data curation and enrichment, which doesn’t require payers to make big changes in their existing data reporting operations and which significantly improves downstream data quality for analysis and use cases.
“Data enrichments can help organizations of all sizes move from insight to action by calculating overspending, finding gaps in care or coverage, and identifying new programs to meet employee needs,” writes Neil S. Austin, benefits analytics consultant with Artemis Health.
This benefits season, give yourself a leg up on the competition with cleaner, more accurate data. Contact us today to learn how.