Data analytics has revolutionized how we approach understanding employee health needs and benefit plan impact. By combining and integrating data from various sources (such as Rx, Medical, and Eligibility claims), data analytics allows us to gain valuable insights into employee health and benefits usage, helping employers and benefits advisors to make smarter decisions and create more effective strategies to maximize employee satisfaction and contain costs.
By leveraging data analytics, employers can gain a better understanding of employee benefits utilization and design plans to provide the most value to their workforce.
Data analytics is the process of analyzing, interpreting, and transforming raw data into useful insights that can be used to make better decisions.
It involves using algorithms, statistical methods, and artificial intelligence (AI) to identify patterns and trends in large data sets. Data analytics is used in various industries, including finance, insurance, marketing, retail, and healthcare.
Did you know that approximately 2.5 quintillion bytes of data are created daily? That's a staggering amount of data, and it's growing exponentially every year. Data analytics is becoming increasingly important as organizations try to make sense of this vast information.
In healthcare, data analytics is used to identify trends in member outcomes, disease prevalence, and treatment effectiveness. It is also used to analyze healthcare costs and identify ways to reduce expenses while improving quality of care.
In employee benefits, data analytics is used to analyze claims data to identify trends in employee health and usage of benefits. Employers and benefits advisors use this data to create more effective benefits strategies and to forecast future health and benefits costs.
Various types of data analytics are used depending on the objective of the analysis. Here are some of the common types of data analytics:
Descriptive Analytics: Descriptive analytics is the most basic form of analytics, which provides an overview of past events and summarizes historical data
Diagnostic Analytics: Diagnostic analytics involves identifying the cause of a particular event or outcome
Predictive Analytics: Predictive analytics uses statistical models and machine learning algorithms to predict future outcomes
Prescriptive Analytics: Prescriptive analytics is the most advanced type of analytics, which recommends a course of action based on the anticipated outcomes of predictive analytics
Various professionals use different types of data analytics for different reasons:
Data analytics can also help employers identify trends in employee healthcare utilization and implement strategies to contain employee benefit costs.
Using data analytics, organizations can gain valuable insights to inform their decisions and improve health and benefits management.
Data analytics has emerged as a valuable tool in healthcare management over the last few years. With the increasing use of electronic health records and advancements in technology, healthcare analytics is being used to interpret member data more meaningfully.
Health analytics, a subset of data analytics, uses statistical techniques and machine learning algorithms to transform complex healthcare data into actionable insights. This technology enables healthcare and benefits providers to track member health patterns, optimize clinical outcomes, and improve quality of care.
One example of health analytics being used in healthcare is predictive modeling through artificial intelligence. Predictive modeling involves analyzing historical data to identify patterns and predict future events. In healthcare, predictive modeling can be used to predict which members are at high risk for developing a particular disease or condition. This information can then be used to develop preventative interventions, improve health outcomes, and reduce healthcare costs.
Another example of health analytics is real-time data monitoring. Real-time data monitoring involves collecting data from multiple sources, such as wearable health trackers, electronic health records, and medical devices, to provide a real-time picture of member health. This technology can help healthcare providers monitor members in real time, detect early signs of health problems, and intervene before the problem escalates.
Health data analytics can be used to identify areas of improvement in employee benefits plans and contain costs. By analyzing healthcare claims data, companies can identify gaps in care and develop strategies to improve employee health outcomes.
Additionally, health analytics can be used to forecast employee health, allowing employers to proactively address potential health concerns and tailor their benefits plans accordingly.
For example, one Springbuk client used health data analytics to identify that their employees were overusing emergency room services for non-emergency situations.
Overall, the use of data analytics in employee benefits is a valuable tool for companies looking to improve their employees’ health outcomes and contain healthcare costs.
By identifying gaps in care, forecasting employee health, and tailoring benefits plans accordingly, employers can develop more effective strategies for managing their employee benefits programs.
Employers can use health data analytics in employee benefits strategies to better understand their employees' needs and improve their benefits plans' effectiveness. By analyzing employee health data, employers can identify areas of concern and tailor their benefits plans accordingly.
Another way employers can use data analytics is to identify trends in employee healthcare usage and cost. By analyzing healthcare claims data, employers can identify areas where costs are high and develop strategies to reduce those costs.
Ultimately, by using data analytics, employers can develop employee benefits plans that better meet their employees' needs, improve their workforce's health, and control costs.
Health data analytics allows employers to make data-driven decisions about their employee benefits, which can lead to more effective benefits plans and a happier, healthier workforce.
Benefits advisors play a vital role in ensuring that employee benefits plans are optimized for maximum value and employee satisfaction. They can use data analytics to inform their decision-making and tailor employee benefits plans to meet each client's unique needs.
For instance, benefits advisors can analyze data on employee health patterns and trends to develop more targeted health benefits programs.
Benefits advisors can also use data analytics to assess the effectiveness of employee benefits plans by analyzing data on utilization rates, employee satisfaction, and health outcomes. This allows them to fine-tune benefits programs and optimize costs to meet the changing needs of employees.
For example, a school district and their new advisory firm began working with Springbuk and was curious if they could uncover any cost-saving opportunities the previous advisor hadn’t surfaced. Within minutes of logging into Springbuk, the firm’s analytics team identified a variance in MRI/CT scans’ cost based on Outpatient vs. Office setting. Learn how the firm worked with actionable direction from Springbuk to:
Overall, benefits advisors who leverage data analytics to design employee benefits plans can provide better coverage and outcomes for employees while keeping costs in check. As data analytics advances, benefits advisors can expect to see even more sophisticated and tailored employee benefits plans emerge.
Data analytics can play a critical role in improving health and benefits management. By analyzing large data sets, health data analytics can identify trends and patterns, provide insights into employee behavior, and help to optimize employee benefits plans.
One way that data analytics can be used to improve health and benefits management is by identifying and addressing gaps in care. Gaps in care refer to instances where employees may not receive appropriate medical attention, resulting in poor health outcomes and increased healthcare costs. Health data analytics can help to identify these gaps in care, enabling employers and benefits advisors to intervene with appropriate resources and support. By proactively addressing these care gaps, employee health can be improved, and healthcare costs can be contained.
For example, when analyzing an employer's aggregate data in the Springbuk health analytics application, a consulting team noticed a large compliance gap in mammogram screenings – only 66% of the population had completed this screening. In this case study, learn how Springbuk empowered the consulting team and their client to:
Overall, data analytics represents a powerful tool for improving health and benefits management. By analyzing employee data and identifying gaps in care, employers and benefits advisors can optimize employee benefits plans, contain costs, and ultimately improve employee health outcomes.
Health data analytics can be an extremely valuable tool for employers looking to contain employee benefits costs. By analyzing data on employee healthcare usage, employers can identify patterns and trends that can inform benefit design and management decisions.
One key way health analytics can help contain costs is by identifying high-risk employees who may require more expensive treatments or ongoing healthcare management. By identifying these individuals early on, employers can intervene with targeted programs and resources to help manage their conditions and prevent costly complications.
For example, one use case for health data analytics might involve identifying employees with chronic conditions like diabetes or heart disease.
In one instance, a consulting group partnered with Springbuk and uncovered the most significant opportunity in their client's population was linked to opioid usage and mental health.
In this case study, learn how Springbuk empowered the consulting group and their client to identify care efficiency opportunities, engage at-risk cohorts through holistic lifestyle modifications, and improve their population’s health outcomes.
By providing this type of proactive care management, employers can help prevent expensive hospitalizations or other healthcare interventions, ultimately leading to cost savings for both the employer and the employee.
One of the most promising applications of health analytics is the ability to forecast employee health. By analyzing a range of data points related to an individual's health status, such as their medical history, lifestyle habits, and demographic information, health data analytics can identify potential health risks and predict the likelihood of certain conditions developing in the future.
One use case of health data analytics for forecasting employee health is in the development of personalized wellness plans. For example, suppose an employee is identified as being at high risk for developing type 2 diabetes. In that case, data analytics can be used to develop a personalized plan that includes targeted interventions such as nutritional counseling, exercise programs, and regular health screenings.
By intervening early and proactively, employers can reduce the incidence of chronic health conditions, resulting in healthier, more productive employees and lower healthcare costs over time.
In addition, health data analytics can also be used to identify trends and patterns in employee health across a population, which can inform broader health and benefits management strategies. For example, if a large number of employees are identified as being at risk for cardiovascular disease, an employer might implement a workplace wellness program focused on heart health, which could include activities such as blood pressure screenings, smoking cessation support, and healthy eating campaigns.
1. Containing Costs: One way health data analytics can be used in employee benefits strategies is by analyzing employee health data to identify high-cost claimants.
This data can be used to develop targeted programs and interventions that help these employees better manage their health, which in turn can reduce healthcare costs. By tracking the outcomes of these interventions, employers can determine which programs are most effective at reducing costs and improving employee health outcomes.
2. Gaps in Care: Health data analytics can also be used to identify gaps in care, or situations where employees are not receiving the care they need to manage their health.
For example, an analysis of claims data might reveal that a certain percentage of employees with diabetes are not getting their recommended A1c tests. Employers can then use this information to develop programs that encourage employees to get the care they need, such as offering free screenings or wellness incentives for completing recommended tests.
3. Forecasting Employee Health: Finally, health data analytics can be used to forecast employee health and predict future healthcare costs. By analyzing claims data and other health data, employers can identify trends and risk factors that are likely to result in increased healthcare costs. This information can then be used to develop employee benefits plans that are designed to minimize costs and improve health outcomes.
For example, if data shows that a large number of employees have high blood pressure, employers might develop a wellness program that focuses on blood pressure management and prevention. By proactively addressing these health issues, employers can avoid costly healthcare claims and improve employee health and wellness.
Overall, health data analytics has the potential to revolutionize how employers approach employee health and benefits management, providing insights and actionable data that can improve health outcomes, reduce costs, and drive more effective employee engagement.