Why big data analytics will limit standard employee benefits

Imagine this — employers with access to predictive analytics to better administer health and benefit plans. Sound good? We not only now know the answer is a resounding “yes,” we are able to “predict” the positive impact analytics will have on both cost control and employee satisfaction. We believe that’s good news for HR executives nationwide.

Health care is behind when it comes to incorporating analytics into their care management approach. But employers, used to the advantages of business intelligence in other functions, are ahead of the game in terms of understanding the need and the power of BI. It’s time to drop the one-size-fits-all approach to benefits. Employees want personalized health care benefits that cater to themselves, their families and their unique health concerns delivered in conjunction with local providers.

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Lowering cost

Analytics offer the opportunity to identify savings opportunities that don’t require cutting back on benefits. This is great news for both employees and employers alike. Implementation of a successful analytics program provides transparency in regard to the health of an employee population, allows employers to identify disease trends, and drives focus in regard to implementation of health, wellness or care management initiatives. Prospectively, analytics supports more accurate budgeting and forecasting, eliminating surprises.

Health plans have been incorporating analytics successfully for years. As self-insured organizations, many employers are looking for the same insight as health care costs continue to rise at an accelerating rate. Data analysis should support a continuous flow of real-time actionable information base on rapid data integration capabilities and a robust and customized reporting platform. Access to a robust reporting platform supports quality assurance, benchmarking, ad-hoc analysis and customizable reporting capabilities.

Improving satisfaction

But what about analytics improving the experience for the employee/patient? Believe it or not, benefit programs designed to cut cost and improve outcomes can also enhance employee satisfaction. That’s the “Triple Aim” in action and it is possible using targeted analytics to deliver the right care opportunities to the right employee at the right time. With an analytics baseline, programs can target employees based on need, like second opinion services for high cost medial issues that improve quality and reduce cost. Generic messaging and offers that don’t apply create at best more garbage and at worst frustration. With busy lives, employees need access to services when they need them in conjunction with a provider they trust.

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Big data

The race to wrangle “big data” and then generate meaningful insights has become the holy grail of population health, yet deriving benefit from the information has remained elusive for a variety of reasons. Big data requires the combination of claims, EMR and patient feedback to create a 360 on patient health requirements.

There is no shortage of big data, and for employers the challenge lies in effectively analyzing the data to better understand the cycle of care, implement the necessary improvements and ultimately improve employee health outcomes while reducing costs.

Predictive analytics can be defined as machine learning technology that can analyze and learn from data to predict and improve the outcome or behavior of a patient. Predictive analytics combines employer level data sources with other market data delivering important insights, such as identifying the outbreaks of flu, or creating personalized health care experience, such as using historic and personal data to help people deal with depression and chronic deceases. Predictive analytics can also create recommendations or predictions of patient outcomes. For example, Amazon's recommendation algorithm that analyzes millions of records and your shopping habits to suggest relevant content that can empower and facilitate decision-making. Big data already helps retailers understand shoppers better by utilizing mobile and social data that’s available to them. In the same manner, employers can benefit greatly from a predictive data-modeling engine.

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In our experiences at Best Doctors and Rise Health, there are four key steps to successfully applying analytics to an employee benefits program:

1. Design a proactive and reactive approach: Along with historic analysis, consider utilizing analytics to get ahead of healthcare trends that drive higher costs

2. Organize your analytics around the employee: Analytics by themselves don’t create action. Organizing analytics around the needs of employees and aligning services appropriately is critical.

3. Evolve to a community model: Benefits that feel customized to the employee and create better-informed patients and that connect to the local primary care community create a community health model that can lowers costs, improves quality and enhances satisfaction.

4. Provide multiple access points: Annual benefit enrollment can’t be the only time that employees receive communication regarding benefits. Access should be driven by need in order for benefits to be utilized to their maximum capacity.

At the end of the day, population health management is only successful when it results in personalized care delivery. Successful programs will be defined by employer/provider partnerships and the right benefits to help knit them together.

Dr. Mark Crockett is the chief medical officer of Rise Health, a Best Doctors Company.