Using Data Analytics to Drive Success in Employee Benefits: Part 2

A healthy workforce is vital to an organization’s bottom line. Time lost to employee illness not only hurts productivity and drives up payroll costs but also increases how much an organization spends on its health plan and prescription drugs. Businesses that collect comprehensive data that leverages risk indexes and use analytics to determine which treatments and preventative measures are both needed and efficient will deliver the best outcomes and be able to achieve sustainable results. A healthier, engaged workforce is a critical effectiveness factor that will set businesses on the path to success.

Data analytics provides actionable intelligence so employers can make informed decisions about the health and wellness programs they offer employees. Access to comprehensive medical and pharmacy claims data can provide insight into compliance of care and direction of care but adding values such as biometric data allows employers to understand if strategies are improving chronic condition control. Using comprehensive population health data, the employer can make strategic decisions based on facts instead of being influenced by the “new shiny objects” being sold. They can run their health plans like they do the rest of their business.

Medical Norm Compliance

It’s unlikely that Americans have ever been more tuned in to discussions on what it means to make data-driven, medically sound healthcare decisions. According to a recent report, providers are still lagging in using the best evidence-based practices when it comes to delivering health care. “While evidence-based medicine (EBM) is the gold standard taught to all clinicians and is widely accepted as important to drive superior outcomes, day-to-day decision-making in health care is slow to evolve from ‘opinion based’ or ‘experience-based’ to those based on sound scientific evidence,” the report says in its introduction.

There are four types of healthcare analytics that can be applied based on the goals and needs of healthcare professionals and institutions. Each type is crucial for making the most of healthcare data, depending on the situation.

1. Descriptive analytics: This provides a historical view of data that allows healthcare providers and management to determine if current practices are efficient and make recommendations, if necessary.
2. Predictive analytics: This uses modeling and forecasting to determine what is likely to happen next. It helps healthcare providers to calculate risk scores for each patient and identify which patients may require additional attention. However, predictive analytics does not indicate preventing adverse events such as hospitalization.
3. Diagnostic analytics: This helps us understand why something happened so that actions can be taken to address the problem. It is useful in figuring out what events and factors led to a specific outcome. Just like descriptive analytics, diagnostic analytics also involves an investigation of historical data.
4. Prescriptive analytics: This type of analytics allows us to understand what actions are needed to change the prediction. It uses advanced algorithms to help determine the effects of specific actions and provide solutions by applying historical data to get a specific result. It is the most useful and powerful type of analytics, providing guided recommendations to healthcare professionals.

Applying data analytics and visualization in the right way can lead to an increase in patient access to services, resulting in lower costs, more revenue, and improved patient satisfaction.

Utilization of Data for Savings

The use of data analytics is becoming more common in healthcare for many reasons. Data analytics methods include analyzing raw data and making conclusions about that information. These methods are very beneficial in population health. Data can help identify populations in need of care, measure the care that is provided, and deliver care to the correct people.

Data is becoming more available and is collected on a regular basis. Data comes from a variety of sources, such as patient demographic data like age and gender, financial data such as insurance information, and clinical data such as medical history and lab results. Collecting data and analyzing it can determine the big picture for the patient and the population. Big data is the process of creating value from data collected, and it is gaining popularity in population health.

Big data can be gathered from various sources and various properties that all contain information about a population. When the information is gathered from the various properties, it is considered a big data chain. In order to get usable information from the big data chain, the organization must manage, process, and analyze big data. For example, big data from the healthcare industry would include financial data, clinical data, biometric data, social media data, data from research activities, and patient satisfaction data. Population health would use this data to determine many factors, including:

• Understanding environmental factors that influence a person’s overall health.
• Considering physical and social determinants of health.
• Shifting thinking from one-size-fits-all to a Value-Based care method.
• Increasing quality and accessibility of care for all populations
• Reducing rising healthcare costs.

These factors combined with a team of professionals that focuses on increasing quality of care and population health management are needed to help with the evolving healthcare landscape.

Data Analytics

Population health has been around for some time, but since the COVID-19 pandemic, its efforts have soared. Population health has been defined in many ways, but the common theme of all the definitions is to achieve positive health outcomes across all populations. To do this, data is needed to bridge the gap. Population health relies on information technology to manage and analyze enormous amounts of data to provide actionable reports. With the endeavors of data analytics and population health, populations across all spectrums have been positively impacted.

Data analytics tools are particularly useful for population health. As healthcare has moved to Value-Based models of care, organizations have placed priority on identifying interventions that can improve the overall health of the population. Data is vital for measuring successful interventions that can improve health outcomes. Data analytic tools and techniques are particularly useful for population health in measuring and predicting disease outbreaks.

With IMA People Analytics, we package together many of these data analytics techniques to understand and address each of our client’s specific needs. Customized benchmarking compares cost and utilization not only to a book of business, but tailors those benchmarks to a client’s specific geographic, demographic, or risk situation to drive better decision-making. Predictive analytics such as rising risk identification or risk scoring, help implement programs to address costs before they happen. Understanding that not all cost is related to claims helps clients develop benefits programs that their employees will value, helping with employee attraction and retention, and reducing absenteeism and presenteeism.

IMA will continue to monitor regulator guidance and offer meaningful, practical, timely information. This material should not be considered as a substitute for legal, tax and/or actuarial advice. Contact the appropriate professional counsel for such matters. These materials are not exhaustive and are subject to possible changes in applicable laws, rules, and regulations and their interpretations.

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