Descriptive & Diagnostic Healthcare Analytics: Unlocking Insights for Better Employee Health

Elevate Your Benefits Management Through Analytics

In the ever-evolving landscape of employee health management, healthcare analytic tools have become indispensable for HR benefits leaders and their advisors. 

In this blog, we'll explore two fundamental healthcare data analytics types: Descriptive and Diagnostic. These are essential components in transforming raw data into actionable insights, ultimately leading to improved health outcomes and more efficient benefits management.

Understanding Descriptive & Diagnostic Healthcare Analytics

Descriptive Healthcare Analytics focus on summarizing historical data to provide a clear picture of what has happened in the past. 

Key Features Of Descriptive Healthcare Analytics

In employee health, this type of analytics help benefits leaders understand trends, patterns, and key metrics related to healthcare utilization, costs, and outcomes:

‍Data aggregation and summarization

1. Collecting data from multiple sources (e.g., claims, member surveys)
2. Cleaning and standardizing data for consistency
3. Creating aggregated views of patient populations
   

Visualization of historical trends

1. Generating time-series charts for key metrics
2. Developing interactive dashboards for exploring trends
3. Utilizing data visualization tools (e.g., Tableau, Power BI)

Reporting on key performance indicators (KPIs)

3. Defining relevant healthcare KPIs (e.g., readmission rates, patient satisfaction)
4. Automating regular KPI reports
5. Tracking progress toward organizational goals
6. Highlighting areas needing improvement
   

Benchmarking against industry standards

3. Comparing performance to national or regional averages
4. Identifying best practices from top-performing organizations
5. Assessing compliance with regulatory standards
6. Setting realistic targets based on industry benchmarks

^{See It in Action
‍}For example, Springbuk uses a multi-faceted approach in providing benchmark comparisons:

• We provide external benchmarks from leading vendors Optum and IBM Watson Health, each of whom have rich national normative datasets
• By leveraging both of these assets, our clients get best-in-class national benchmarks that help them understand how their key performance indicators (including cost, quality, utilization, prevalence) stack up against national norms, as well as various segments (such as industry and geography)
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Diagnostic Healthcare Analytics: building upon descriptive analytics, diagnostic analytics dive deeper into understanding why certain events or trends occurred. 

Key features Of Diagnostic Healthcare Analytics

Diagnostic healthcare analytics help benefits leaders identify the root causes of health issues, cost drivers, and factors influencing employee wellness:

Data drill-down capabilities

1. Enabling users to navigate from high-level summaries to granular details
2. Providing interactive filters to explore specific patient cohorts
3. Offering multi-dimensional analysis (e.g., by demographics, diagnosis, treatment)
4. Supporting ad-hoc queries for deeper investigation

^{See It In Action}
‍‍For example, Springbuk Analytics Navigator handles complex data processing and visualization, enabling in-depth exploration without advanced technical skills:

• Explore multi-level analyses through interactive drill-down capabilities
• Access intuitive guidance to analyze drivers of spend, trends, and utilization
• Effortlessly find answers and export views for stakeholder presentations

Learn More

Correlation analysis

2. Identifying relationships between different healthcare variables
3. Visualizing correlations through scatter plots or heat maps
4. Exploring potential risk factors for specific health outcomes
   

Anomaly detection

2. Implementing algorithms to identify outliers in healthcare data
3. Flagging unusual patterns in patient care or hospital operations
4. Detecting potential fraud or billing errors
   

Causal factor identification

2. Applying statistical techniques to determine potential cause-and-effect relationships
3. Utilizing machine learning models for predictive analysis
4. Conducting root cause analysis for adverse events or poor outcomes
5. Identifying modifiable factors that influence patient health or operational efficiency

Benefits of Descriptive & Diagnostic Healthcare Analytics

1. Comprehensive Understanding of Employee Health Trends: some text
   1. By leveraging descriptive analytics, HR benefits leaders can gain a holistic view of their workforce's health status 
   2. For example, they can track changes in chronic condition prevalence, healthcare utilization rates, and wellness program participation year-over-year

^{See It in Action
‍}‍^‍Springbuk's Employee Health Trends Report provides HR and benefits leaders with actionable insights into workforce health trends, benefits utilization, and cost drivers. This data-driven report enables organizations to:

• Align benefits offerings with employee needs
• Create targeted wellness initiatives
• Optimize healthcare spending

By leveraging these actionable insights, companies can develop more effective, employee-centric health strategies that improve workforce well-being while managing costs.

Identification of Cost Drivers

2. Diagnostic analytics can help pinpoint specific factors contributing to rising healthcare costs
3. For instance, it might reveal that a particular department has a higher rate of stress-related claims, allowing for targeted interventions
   

Improved Benefits Design

2. By understanding historical trends and their underlying causes, organizations can tailor their benefits packages to better meet employee needs
3. This data-driven approach can lead to higher employee satisfaction and more effective use of resources

Enhanced Wellness Program Effectiveness

4. Descriptive analytics can show participation rates and outcomes of wellness initiatives
5. While diagnostic analytics can help identify barriers to engagement, allowing for program refinement
   

Better Resource Allocation

4. With a clear understanding of past trends and their causes, benefits leaders can more effectively allocate resources to address the most pressing health issues within their workforce

Implementing Descriptive & Diagnostic Healthcare Analytics

Data Integration

1. To harness the full power of these analytics tools, organizations need to integrate data from multiple sources, including health plan data, claims information, and employee surveys

^{See It in Action}
Today's robust health analytics solutions should accommodate hundreds of data sources.

For example, the Springbuk Health Intelligence® platform ingests employers' multi-sourced data from more than 500 sources, normalizes and enriches, and then runs thousands of data quality checks — equipping benefits teams with accuracy, scalability, and agility for more informed decision-making.

User-Friendly Dashboards

2. Look for analytics tools that offer customizable, intuitive dashboards
3. These should allow benefits leaders to easily visualize trends, drill down into specific metrics, and share insights with stakeholders
   

Automated Reporting

2. Implement tools to generate regular reports on key metrics, saving time and ensuring consistent monitoring of important health and benefits trends
3. Intuitively reporting features like Springbuk Report Builder allow users to created tailored reports using informative cards from across the Springbuk platform, highlighting what's most important to clients and their stakeholders
   

Training and Support

2. Ensure that your team is properly trained to use and interpret the analytics tools
3. Many vendors, including Springbuk, offer comprehensive training and ongoing support to maximize the value of their platforms

Use Case: Leveraging Descriptive and Diagnostic Healthcare Analytics for Better Outcomes

For example, a mid-sized manufacturing company may look to implement advanced healthcare analytics tools. Using descriptive analytics, the company could identify a trend of increasing musculoskeletal claims over three years. Diagnostic analytics may then reveal that these claims were primarily from one production facility and often related to repetitive strain injuries.

Armed with this insight, the benefits team might decide to:

• Implement ergonomic training and equipment upgrades at the affected facility
• Introduce a targeted physical therapy program for at-risk employees
• Adjust their benefits package to better cover preventive care for musculoskeletal issues

The result: Within 12 months, the company could see a sizeable reduction in musculoskeletal claims and a significant improvement in employee satisfaction with their health benefits.

Maximize Health Benefits Plans with Descriptive & Diagnostic Analytics

Descriptive and Diagnostic Healthcare Analytics are powerful tools in the arsenal of HR benefits leaders and advisors. By providing both a broad overview of health trends and deep insights into their underlying causes, these analytics enable data-driven decision-making that can significantly improve employee health outcomes and optimize benefits management.

As you consider implementing or upgrading your healthcare analytics tools, look for solutions offering robust descriptive and diagnostic capabilities, user-friendly interfaces, and strong data integration features. 

Platforms like Springbuk offer comprehensive analytics solutions that can help you unlock the full potential of your employee health data.

Ready to take your benefits strategy to the next level with advanced healthcare analytics? Learn how Springbuk can empower your organization with descriptive and diagnostic insights that drive meaningful improvements in employee health and wellness.

Ready for more? Check out these blogs:

• What are Healthcare Analytics Tools? 
• What is Health Intelligence?
• What is Health Analytics?