The rising cost of healthcare is a perennial challenge for employers and health plans. The average cost of employer-sponsored healthcare is projected to rise 9% in 2025, higher than the 6.4% increase from the previous year and the highest increase in a decade. This means CFOs and benefits leaders will be under even more financial pressure as they plan budgets and healthcare benefits for the coming year. 

Striking the right balance between controlling expenses and providing robust employee support requires more than guesswork; it demands actionable insights. Healthcare data analytics offers a powerful solution, equipping decision-makers in risk bearing organizations with tools to predict healthcare needs and design benefits plans that optimize costs while enhancing employee well-being. 

How healthcare data analytics can help employers combat rising healthcare costs  

Rising healthcare costs place immense pressure on employer benefits budgets, often leading to tough decisions about where to allocate resources. Employers who lack visibility into potential high-cost cases may find themselves unprepared, leading to budget overruns and strained resources. 

Knowing that healthcare costs will increase every year forces CFOs and benefits leaders who still want to provide comprehensive care to employees to get creative. The rising use of specialty drugs like biologics and GLP-1 medications is a key factor in these rising costs: while they represent only 2% of total claims, they make up half of overall spending on drugs for most employers. 

One potential solution is to identify where there is disproportionately high spending and allocate more resources there, as well as to cut back on areas where there is less usage. This can help reduce the overall spending associated with managing a patient’s health across all aspects of care (total cost of care) including preventive services, chronic disease management, acute care, post-acute care, and any ancillary services.   

Advanced healthcare data analytics can help employers anticipate high-cost care episodes by leveraging predictive models. These models analyze past claims data, risk scores, and demographic information to identify employees at higher risk for costly health events.  

Armed with this knowledge, employers can proactively allocate resources, budget for anticipated expenses, and implement interventions to mitigate financial surprises. For instance, identifying at-risk employees early allows organizations to offer tailored health management programs, reducing the likelihood of costly acute care episodes. 

Another potential approach is to cut back on areas of coverage that are used less often and to promote preventive care. As one example, dental health is often a predictor of more serious issues, so increasing benefits for those services may head off higher healthcare costs down the line.  

Budget proactively for high-cost care 

Major surgeries, flare-ups of chronic disease, and other health episodes that require significant medical intervention can result in unexpected expenses, leading to budget overruns and financial concerns. Without data-driven insights, employers can only guess at these annual costs.  

Certain conditions, such as cancer and heart disease, can be correlated with demographic factors like age and gender. By leveraging predictive models enriched with Census data and other Social and Behavioral Determinants of Health (SBDoH), alongside claims data and risk scores, healthcare data analytics can provide employers with deeper visibility into the likelihood of major health incidents. This enhanced understanding, which also accounts for the rise of chronic diseases, allows businesses to plan budgets more effectively and mitigate the risk of unforeseen expenses.  

Addressing chronic conditions that drive total cost of care 

According to the CDC, 90% of healthcare spending is on care for people with chronic and mental health conditions. Diabetes, heart disease, asthma, and other chronic conditions often result in frequent visits to the doctor, regular medication, and even hospitalization—all of which can drive up the total cost of care. 

Targeted interventions and preventive care can help employees control these conditions and reduce the need for more serious and costly interventions. Employers can leverage insights from healthcare data analytics to zero in on how prevalent these conditions are among the workforce and implement appropriate programming.  

This might include wellness programs for smoking cessation and exercise or employee education on health management. Employers might also promote or subsidize equipment like blood pressure monitors and pulse oximeters to help employees manage their chronic conditions. 

Using data analytics to customize benefits 

Every workforce is different, with variations in age, lifestyle, ethnic and racial backgrounds, sexual orientation, and more. That means a one-size-fits-all healthcare benefits plan may not align with their specific risks, preferences, or requirements. This can also result in wasted resources on unused benefits or unmet healthcare needs that drive up costs in the long run. 

Using healthcare analytics to explore how employees use healthcare services and their potential future needs helps benefits leaders customize more appropriate benefits packages.  

A workforce that skews older may want more screenings and medication coverage, while a younger one may need more maternity and pediatric care. By aligning benefits offerings with employee preferences and risks, organizations can optimize spending and enhance employee satisfaction. Customized benefits plans strike a balance between cost efficiency and employee support, ensuring resources are directed where they are needed most. 

Boosting employee engagement with preventive care 

Preventive care is critical for identifying and addressing health issues before they escalate into costly treatments. One in four employed adults in the U.S. skips regular checkups. Less than 10 percent of Americans undergo routine screenings. By not taking advantage of preventive care, employees put themselves at risk for late-stage health issues and more intensive and costly treatments—which drive up the total cost of care for employers. 

Data analytics sheds light on patterns of preventive care utilization within the workforce. Employers can analyze patterns in healthcare usage to pinpoint underused preventive services, then use the findings to develop incentives and educational campaigns that encourage employees to take advantage of them.  

These initiatives can help employees stay healthy and catch potential issues earlier, ultimately reducing the need for more intensive treatments. They can also lower absenteeism due to health issues—which costs employers almost $35 billion a year. 

Enhancing transparency and accountability in benefits spending 

Employers often struggle to gain a clear understanding of how healthcare dollars are spent across various services and providers. Without visibility into how employees are spending their healthcare dollars across services and providers, it can be difficult for decision-makers to make a business case for including various benefits and expenditures. At the same time, it is challenging for organizational leaders to hold those decision-makers accountable for benefits planning. 

Healthcare data analytics offers detailed insights into benefits spending, breaking down costs by service category, provider, and employee demographics. By identifying areas of waste and inefficiency as well as those that drive value, these granular insights provide CFOs and benefits leaders with concrete information to make decisions regarding coverage and packages that can help cut costs. 

Leveraging healthcare data to continuously improve benefits planning  

Workforces are constantly changing. People move on and retire, while new individuals are hired. Employees may work remotely and need coverage in different regions or states. That means that the group’s healthcare needs may shift as well. Without continuous improvement, organizations may miss opportunities to optimize benefits programs and reduce costs. 

Healthcare data analytics supports continuous refinement of benefits plans by providing real-time insights into employee health trends and feedback. 

When benefits plans remain the same year after year, they may no longer meet workforce requirements and preferences for coverage, services, and costs. Employers must evaluate their plans regularly to support network optimization and make sure services and providers are still aligned with employee needs.  

Analyzing data related to claims, demographics, and employee feedback as well as external trends related to healthcare and population health can help benefits planners ensure packages remain relevant, useful, and cost-effective. 

Healthcare data analytics translate to more effective benefits 

Given the complexities of healthcare and its economics, planning employee benefits without the insights provided by data is a guessing game—one that can quickly become expensive when guesses are off-base. 

Healthcare data analytics offers a transformative approach to benefits planning, enabling employers to balance cost management with comprehensive employee support.  

By embracing a data-driven approach, employers can meet the dual challenge of controlling expenses and fostering employee well-being, creating a sustainable path forward in benefits planning. Data-driven benefits plans allow employers to optimize spending, tailor offerings to diverse employee needs, and achieve long-term cost savings.