Massachusetts businesses are experiencing financial strain due to the high cost of employer-sponsored health insurance. Rising premiums are outpacing wage growth, leading to increased costs for businesses and potential financial hardship for employees.
Americans are often frustrated with their employer-sponsored health care in terms of the necessary approvals, exclusions, deductibles, and denials. The average person rarely looks at their policy until they have a potential claim … which is when they discover the full extent of the problem.
Japan used Total Quality Management and continuous improvement techniques to build some of the highest-quality cars in the world. There is an opportunity to use continuous improvement techniques such as Six Sigma, Statistical Process Control (SPC), and Lean techniques, among others, to improve healthcare quality and reduce healthcare costs by analyzing health insurance claims data. The problem is most employers never do, and it is employees who pay the cost.
Employers — often in collaboration with independent health benefits advisors and third-party administrators — are adapting continuous improvement methodologies to lower healthcare costs for employees. While these techniques have been used historically in worker’s compensation and manufacturing, they are increasingly being applied to health insurance premiums and overall healthcare spending.
For example, Motorola, an early pioneer of Six Sigma, applied these methods to healthcare vendor selection, claims data analysis, and wellness program optimization. These efforts resulted in a 25% reduction in hospital readmissions among targeted employee populations, saving millions on health plans.
Similarly, Boeing implemented Lean and Six Sigma principles through its “Boeing Intensive Outpatient Care Program,” which focused on reducing hospital admissions and costs for high-risk employees. By leveraging SPC tools, Boeing identified ER usage patterns and addressed them with telehealth and care navigation, reducing ER visits by 15% and saving $5 million annually.
General Electric (GE) used its Six Sigma expertise to analyze healthcare claims data, pinpointing high-cost conditions like diabetes and implementing chronic disease management programs. This approach reduced preventable hospitalizations by 20% and improved employee health outcomes. Toyota similarly applied Lean principles to wellness programs and care coordination, reducing high-cost claims by addressing chronic disease risks, saving an estimated $10 million annually.
Despite these successes, many employers struggle to adopt continuous improvement techniques due to several challenges:
HR and benefits teams often lack the specialized skills, such as Six Sigma training, needed to implement these tools. Without adequate expertise or resources, employers may find it difficult to get started or sustain such initiatives.
Continuous improvement relies on accurate, timely data, but health plans frequently provide incomplete or siloed claims information. Disparate systems for claims, pharmacy, and wellness data impede analysis. Quarterly or monthly reporting cycles also limit the ability to conduct rapid improvement cycles like Plan-Do-Check-Act.
Continuous improvement demands a culture of process excellence and long-term focus. Employers without such a mindset often lack the patience or resources to sustain these efforts, even when the potential cost savings are substantial.
Many traditional brokers don’t have the expertise, or incentive to learn how to create or manage innovative, or custom programs or help employers get access to their data. Employers trust brokers and advisors to bring them the best options on the market. If the broker is not willing to learn there is a better way (whether it is profit driven motive or pure ignorance), that is a massive barrier for employers.
There are also some brokers who are rejecting this movement towards transparency and actively swaying employers against it, citing “risk” and “work load” or “employee dissatisfaction.”
Disease-Specific Claims Management
When employers begin analyzing claims data with a focus on reducing healthcare costs, they often uncover insights about claims by disease category. This approach — termed Disease-Specific Claims Management, Targeted Claims Management, or Population Health Management or Condition-Based Claims Optimization — enables employers to tailor strategies to specific conditions like type 2 diabetes or hypertension.
For each disease, multiple vendor programs are available, typically incorporating the following components:
Identifying high-cost populations and risk factors;
Connecting patients with care managers to ensure adherence to treatment plans;
Using tools like apps, coaching, and incentives to encourage healthy behaviors;
Offering screenings, telehealth, and remote patient monitoring to prevent costly complications;
Addressing mental health issues that often accompany chronic diseases.
While these programs offer significant cost savings, comparing vendors can be challenging because they use different metrics to prove their effectiveness. This lack of standardization makes an apples-to-apples comparison difficult.
Third-Party Expertise
Employers often feel unequipped to handle such complex projects, foregoing opportunities to save costs. This is where third-party consultants can assist. These experts help employers assess vendors, validate cost-saving claims, and develop a strategy tailored to their workforce. Most smaller employers do not have the time, resources or expertise to apply Six Sigma to their health care spend.
Most employers want to focus on their core business and do not want to be in the health insurance business. The best first step for all employers is to request an independent review of their health care spend by a fee-based health benefit advisor certified by the Validation Institute or Health Rosetta to determine a plan that works for their employee and dependent base and their geographic location.
Ed Gaskin is Executive Director of Greater Grove Hall Main Streets and founder of Sunday Celebrations
