Health analytics is a powerful set of tools that can help employers and benefits advisors make better decisions when it comes to employee wellness and health benefits programs.
In this blog, we will explore:
We will also provide examples of how health analytics are being used today and strategies for analyzing employee health data. By the end of this blog, you should have a better understanding of how you can use health analytics to optimize your organization’s benefits strategy.
Health analytics is an increasingly important tool for employers, benefits advisors, and healthcare providers and insurers. It is an umbrella term referring to the use of data and analytics tools to understand and improve healthcare decisions and performance. Health analytics enables employers to gain insight into their employees' healthcare needs and create more effective benefits plans by collecting and analyzing data from various sources, such as health records and medical claims, including:
Today’s robust health analytics solutions should be able to accommodate hundreds of data sources. The Springbuk Health Intelligence™ platform, for example, ingests employers’ multi-sourced data from more than 400 sources, normalizes and enriches, then runs more than 180 data quality checks — equipping benefits teams with accuracy, scalability, and agility for more informed decision-making.
By leveraging predictive models and artificial intelligence (AI), health analytics can help employers forecast health costs, identify high-risk individuals, optimize coverage levels, and increase employee engagement. It also involves developing models to forecast outcomes, identify trends, and improve the quality of care.
In addition to its analytical capabilities, health analytics also encourages collaboration between organizations, including employers, their benefits advisors, point solution vendors, and other healthcare providers, that provide care services or contribute data sets.
Through this collaborative approach, employers are able to access a larger pool of resources with which they can analyze member outcomes more accurately. This helps them better understand how best to design benefits programs that meet their employees’ needs while staying within budget.
Overall, health analytics is becoming increasingly important for employers looking to reduce healthcare costs while improving the overall health of their workforce – something that has become especially pertinent in today’s uncertain economic landscape as organizations strive for financial sustainability without jeopardizing the quality of life for their employees.
The capability of health analytics to provide organizations with a comprehensive view of their employee healthcare needs is highly valuable. Employers can predict future medical costs through AI-powered predictive models and curate tailored strategies to offer better coverage at lower premiums. This insight helps employers make sound decisions and use resources more efficiently while controlling costs.
Health analytics also have the potential to increase employee engagement in their benefits programs. By understanding which types of preventive care services are most effective for certain populations or age groups, companies can develop targeted campaigns that motivate employees to take charge of their own health and well-being.
With the help of health analytics, organizations can move away from traditional fee-for-service models toward value-based plans that reward providers for keeping patients healthy over time, rather than quantity over quality treatments. The shift in focus reduces medical costs and improves member outcomes by providing personalized preventive care services customized to individual needs and preferences.
Utilizing the power of health analytics has tremendous potential for organizations when it comes to managing employee benefits programs effectively—from lowering healthcare costs to boosting employee engagement—while simultaneously improving member outcomes across the board.
Health analytics can be broken down into four main categories: cost analysis, predictive analytics, machine-learning algorithms, and third-party data sources. Each of these categories offers different benefits to employers and benefits advisors in optimizing their employee healthcare strategies.
Health analytics offers a range of practical applications for employers, allowing them to better serve their employees’ healthcare needs while still controlling costs. By leveraging data and advanced analytical techniques, including artificial intelligence (AI), employers can create more informed decisions regarding benefits plans.
Through forecasting employee health claims, employers and benefits advisors can anticipate future medical expenses or identify areas where costs can be cut. Additionally, insights from healthcare utilization and cost trends enable employers to determine the most suitable plans and pricing models for their company. Finally, predictive analytics models help identify high-risk individuals who may require lifestyle modifications or additional resources for preventive care.
All of these strategies allow employers to reduce healthcare costs while still providing quality coverage for their workers.
Employers and benefits advisors need to follow specific steps in order to ensure that their health analytics implementation is successful. These steps include:
1. Establish Goals and Objectives: Before beginning any health analytics project, employers need to decide what they are looking to achieve. This involves outlining clear goals, such as reducing healthcare costs or improving overall employee engagement with benefits plans. Benefits advisors can help employers identify the right objectives for their organization and create a plan of action that includes measurable metrics.
2. Assess Data and Identify Gaps: Once goals have been established, employers need to assess the data they have available in order to identify potential gaps in care or areas of improvement. The data should be collected and integrated from a variety of sources, including employee surveys, medical claims data, utilization trends, pricing models, and benchmark sources. Benefits advisors can then use this data to determine which types of plans offer the most value for employees and which areas require additional attention from employers.
3. Develop a Data Strategy: Once gaps are identified, employers need to develop a comprehensive strategy on how best to address them using health analytics solutions. It is important that organizations develop an efficient strategy that integrates multiple data sources while also ensuring the accuracy and reliability of results.
4. Integrate Data Sources: After developing an effective strategy for collecting data from various sources, organizations must implement the necessary technology solutions in order to integrate those sources into one platform for analysis purposes. This includes linking existing systems with new ones as well as creating a unified database that contains all collected information on employees’ healthcare needs and preferences – allowing organizations access to comprehensive insights into their workforce's health status at all times.
5. Analyze and Interpret Results: Finally, after integrating all relevant data points into one system, organizations must analyze their results accurately to make informed decisions on their benefits plans based on up-to-date information about their employees' healthcare needs. Through careful analysis, employers can monitor trends over time in order to uncover opportunities for cost savings or improved engagement levels among employees with their benefits package. Furthermore, by understanding which services are being used by certain populations or age groups, organizations can curate more tailored strategies when it comes time for open enrollment periods.
Implementing health analytics requires careful planning but can also provide significant rewards. By following these five steps - establishing clear goals and objectives; assessing available data and identifying gaps; developing an effective data strategy; integrating multiple sources into one system; analyzing and interpreting results - employers can ensure a successful implementation of health analytics solutions while maximizing its potential benefits.
This section explores advanced strategies for analyzing employee health benefits data and how employers can leverage them to make more informed decisions.
Predictive analytics is a powerful tool for predicting future costs, adverse events, or the prevalence of certain conditions in the population. Predictive models can be used to identify trends in utilization and cost data, which can help employers modify existing benefits, implement new programs, or formulate member outreach strategies. Predictive analytics can be used to identify high-cost claimants so that resources can be allocated appropriately. Machine-learning algorithms are another powerful tool for uncovering trends in employee health data. By training machines on large datasets of healthcare expenditure and utilization patterns, machine-learning algorithms can detect patterns that may not be visible to the naked eye. These insights into employee healthcare trends provide valuable information that helps employers target interventions and make more informed decisions regarding their benefits packages.
AI-driven approaches also have the potential to identify at-risk populations and pinpoint potential cost savings opportunities. For example, AI models could be used to predict which individuals are most likely to experience certain medical conditions or develop chronic diseases, allowing employers to target preventive care services or lifestyle modifications before a serious health problem arises or costly treatments become necessary.
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 client success story, learn how Springbuk empowered the consulting team and their client to:
Additionally, AI models could be used to compare different plan options and recommend those with the best cost/benefit ratio for employees’ needs.
By utilizing these advanced strategies for analyzing employee health benefits data, employers can gain valuable insights into their workforce’s healthcare needs and create more effective benefits plans that reduce costs while still providing quality coverage for their employees.
Health analytics offer employers a unique way to gain valuable insights into the healthcare needs of their employees.
Predictive models can be used to:
Through careful analysis of data, employers are able to design cost-effective plans that provide quality coverage while also controlling costs associated with employee health benefits.
Additionally, advanced analytics solutions enable employers to monitor trends in utilization and make informed decisions about plan design and administration. By leveraging these powerful tools, organizations have the potential not only to reduce healthcare costs but also to improve overall outcomes among employees through tailored strategies that meet each individual's specific needs.
Benefits advisors are in a unique position to help employers manage healthcare costs and deliver quality coverage while controlling expenses. Leveraging health analytics, they can gain valuable insights into employee utilization patterns, cost trends, and areas for improvement. By using predictive models, they can anticipate medical expenses and identify gaps in coverage that their employer clients need to address. Advanced analytics solutions enable them to monitor trends in employee benefit usage over time and make informed decisions about plan design and administration that best suit their client’s needs.
Machine-learning algorithms, paired with AI-driven approaches, enable benefits advisors to uncover potential cost savings opportunities as well as identify vulnerable population groups that require special attention. All stakeholders should have access to the same information when making decisions about employee healthcare plans through integrated enterprise systems. It is important for employers and their benefits advisors to collaborate on a strategic approach when it comes to managing healthcare costs while ensuring employees receive quality coverage from their benefit plans.
Benefits advisors who use data to drive decisions are more likely to be seen as trusted advisors, rather than relying on “service” as a differentiator, which can help them win new business and retain more clients. Health analytics empowers advisors and their clients to decrease costs, mitigate risks, and avoid future high-cost services.
Benefits analysts are pivotal in assisting employers to optimize their healthcare plans and lower costs. By leveraging health analytics, they can:
Analysis of large datasets helps spot cost-saving opportunities and potential coverage gaps. Utilization data analysis, for instance, can uncover instances where services are overused or underutilized – a telltale sign that a plan is not correctly tailored to the population it serves.
Moreover, data from different sources can be used to construct predictive models that calculate future medical expenses based on existing utilization habits. These models can then be deployed to accurately estimate the monetary implications of various plan designs or alterations in provider networks.
Machine-learning algorithms provide benefits analysts with the capacity to detect trends in employee healthcare that may not have been observed using traditional methods. With sophisticated analytic solutions, analysts can observe utilization patterns over time and discover anomalies that could indicate high-risk individuals who may require lifestyle changes or extra resources for preventive care. Through early detection of these individuals, employers are able to take measures to reduce risk while providing quality coverage at reduced premiums.
Furthermore, by looking into employee engagement metrics, such as survey responses or usage rates of particular services like telemedicine visits, benefits experts are able to gain meaningful insights into how employees interact with their plans and can therefore make informed decisions regarding plan design and administration. This assists organizations in understanding which types of preventive care services prove most successful for certain populations or age groups, so they may customize their plans specifically for their workforce’s needs.
Through careful analysis of staff health benefits data, utilizing effective tools such as predictive analytics and machine-learning algorithms, benefits analysts can aid companies in reducing costs while still offering premium coverage for employees. By effectively taking advantage of these powerful tools and implementing actionable steps toward reducing healthcare bills throughout an organization's scope, benefits leaders will have greater chances of succeeding in designing, managing, and monitoring employee benefits programs.