As healthcare costs continue to rise, Pharmacy Benefit Managers (PBMs) are under increasing pressure to manage their clients’ drug spend, while ensuring patients have access to the medications they need. One of the most valuable tools at their disposal is healthcare claims data, which can deliver insights into patient behavior, healthcare utilization, and healthcare costs. This data allows PBMs to identify cost savings opportunities, develop more effective formularies, and improve patient outcomes. Discover how to analyze claims data, and what it can do for your organization.
Understanding Healthcare Claims Data
Healthcare claims data is a rich source of information for providers, health systems, pharmaceutical, and employee benefits companies. Claims data can help employers understand the cost of healthcare and identify opportunities for cost savings.
1. What is a claim analysis?
A claim analysis examines and interprets healthcare claims data to identify patterns and trends that can be used for predictive analytics. The analysis typically involves cleaning and transforming the data using statistical techniques to identify patterns and relationships and visualizing the results for interpretation.
2. What does claims data consist of?
Claims data consists of records healthcare providers generate when they bill insurers for the care they provide. These records include information such as:
- Diagnosis codes
- Procedure codes
- Drug cost details
- Utilization metrics
- Prescription drug orders
- Prescriber data
- Demographic data
Analyzing claims data can help identify patient trends, understand the effectiveness of treatment, and uncover patterns that can be used for predictive analytics.
3. The value of using claims data
According to the Rural Health Information Hub, claims data is essential for understanding population health and evaluating healthcare utilization. With the help of advanced analytics tools, pharmaceutical companies can determine how to analyze claims data and make better-informed decisions about product development, marketing, and sales strategies.
The Use of Claims Data in Predictive Analytics
Healthcare claims data is valuable for predictive analytics, providing insights into patient and population health, supporting better-informed healthcare plans, and driving cost efficiencies.
What can claims data tell you?
Much of the value of claims data lies in the fact that it can help employers forecast healthcare trends, identify cost savings opportunities, and optimize healthcare delivery. For example, claims data can reveal information such as:
- Patient behavior: Claims data helps identify high-risk patients more likely to require costly healthcare services. It also monitors patient adherence to treatment plans, identifies gaps in care, and evaluates the effectiveness of different treatment approaches.
- Healthcare utilization: Claims data offers insights into how healthcare services are used, such as the frequency of doctor visits, hospitalizations, and emergency room visits. This information can highlight areas where healthcare services can be improved or made more efficient.
- Healthcare costs: Claims data delivers a detailed breakdown of healthcare costs, including the cost of specific procedures, medications, and services. This information can be used to identify cost-saving opportunities, negotiate better pricing with providers, and develop more effective formularies.
Analyzing healthcare claims data enables companies to predict patient outcomes and enhance healthcare quality. By leveraging claims data, pharmaceutical organizations can develop more effective treatments, tailor their marketing strategies to specific patient populations, and acquire valuable insights that drive business success and improve patient outcomes.
3 Steps on How to Analyze Claims Data
Analyzing claims data requires a structured approach that includes data cleaning, transformation, and visualization. Here are some tips on how to achieve this.
- Clean and transform the data: Before analyzing the data, ensure it’s clean and properly formatted. This may involve removing duplicates, correcting errors, and standardizing the data. You may also need to transform the data into a format that’s easier to work with.
- Identify patterns and trends: Once the data is clean, identify patterns and trends using statistical techniques such as regression analysis, risk adjustment, and predictive modeling. This helps identify high-risk patients, forecast healthcare trends, make better decisions, and optimize healthcare delivery.
- Visualize the results: Visualizing results makes interpreting the data and communicating the findings to others easier. Use charts, graphs, and other visualizations to illustrate the findings and highlight key insights.
Analyzing healthcare claims data can be a complex and time-consuming process. Consider working with a partner like Xevant that specializes in healthcare data analytics to provide the expertise and tools necessary to analyze claims data effectively. Contact us today to arrange a demo.