Accelerating Time-to-Insight in Claims Analytics: A Smarter Approach with Claims360

Written By - Saurav Kumar, Engagement Lead

Accelerating Time-to-Insight in Claims Analytics: A Smarter Approach with Claims360

Imagine a mid-sized pharmaceutical company in the immunology space that recently launched a new product in the psoriasis market. Their primary goal is to understand how their product is performing in the market, especially where it's working and where it’s not. To do this, they need to identify the true source of their business, analyze how their product is being utilized across lines of therapy, and understand the key healthcare providers (HCPs) driving the sales. To gather these insights, the company needs to leverage claims data that captures detailed patient treatment journeys. This will allow them to answer key questions, such as:

Mapping the Line of Therapy:

Where is their product being prescribed in the treatment sequence: first-line, second-line, or later?
What is their market share in each line of therapy?

Patient-level Deep Dive:

What therapies are patients using before they start on their product?
Are they new patients, switch-ins from other treatments, or are they adding the product onto their existing regimen?

Patient Access Barriers:

What is the current fulfillment rate, abandonment rate, and rejection rate?
What are the rejection reasons? How does the abandonment rate vary with an increase in out-of-pocket amount?

HCP Behavior:

Which healthcare professionals are consistently driving uptake of the product?
Are these early adopters, high-decile writers, or do they have specific prescribing behaviors?

By leveraging claims data analytics, the company aims to bring all this information together to deliver actionable, data-driven insights. These insights will refine their sales and marketing strategies, helping them make informed, timely decisions to maximize the impact of their new product.

However, the challenge they face is that if the claims data is not organized and handled in a structured manner, the complexity of the data and the numerous business rules involved can significantly delay the process of obtaining actionable insights. This could result in a substantial lag in brand strategic decision-making, potentially being made months after the optimal moment has passed.

Several operational challenges contribute to this lag -

Fragmented data processing
, causing inconsistencies and reconciliation delays
Lack of real-time data quality checks
, leading to rework and errors
Heavy reliance on manual application of business rules
(e.g., market baskets, patient stability rules, line-of-therapy methodologies)
Disjointed reporting platforms
, slowing down decision-making across teams
Extended validation cycles
, i.e., benchmarking against historical data before final reporting

As a result, generating actionable claims insights can take a month or more,

delaying decisions on treatment trends, marketing strategies, market access, competitive benchmarking, etc.

Introducing Claims360: Structured, Scalable, and Built for Speed

At ProcDNA, we are solving this challenge with Claims360, a structured analytics accelerator that eliminates inefficiencies and accelerates time-to-insight.

Imagine Aliza, a Commercial Analytics Director, wants to analyze year-over-year new patient starts for Biologics in the psoriasis market. Traditionally, she would need to request consulting vendors, wait for data extraction, and review reports that could take days. With the Claims360 self-serve analytics platform, she can simply type a question like: “How many new patients start do we see each year for Biologics in the psoriasis market?” Within seconds, the system provides: A data table with exact figures, a trend chart for visual analysis, and LLM-generated insights highlighting key takeaways.

This is just one example - more complex queries can now be answered in real time with intuitive access to insights, making Claims360 a powerful solution in claims analytics.

Our Claims360 solution is built on three key pillars -

1. Unified Data Framework & Business Rules Parameterization

Claims analytics require a flexible structure to adapt to evolving market needs. Claims360 centralizes fact and dimensional data while parameterizing business rules, ensuring agility without compromising consistency.

Standardized Data Integration:
Aggregates fact and dimensional data appropriately from the claims database by creating structured data modules - raw, pre-processed, and final processed layers, enabling data readiness for analytics and insights. The framework is designed for easy integration with leading cloud platforms like Databricks, Snowflake, and others.
Business Rules Parameterization:
Claims analytics process involves multiple business rules at various stages, many of which are tailored to specific analytical needs, adding complexity. These rules, ranging from patient inclusion criteria to patient journey logic, are often tailored to specific brand or indication needs, which adds complexity. Without an organized way to manage them, analytics become inefficient, error-prone, and inconsistent across use cases. Therefore, parameterizing these rules is critical to improving efficiency, reducing quality risks, and ensuring consistency across workflows.

In Claims360, we built a structured framework that modularizes business rules based on user inputs, allowing dynamic rule adjustments without disrupting the workflow. Below are some key business rules we have parameterized to enhance robustness and efficiency:

ICD Code & Market Basket Drug Rules to identify patients relevant to the indication of interest. For example, the ICD codes for your indication of interest might not change, but the market basket may change with the launch of new products. Therefore, with a parameterized input option, you can update the market basket in real-time
Patient Stability Criteria to ensure patients meet diagnosis/treatment thresholds before inclusion. For example, in metastatic breast cancer (mBC) analytics, a single diagnosis may indicate misclassification. However, a patient with two or more diagnoses reduces the risk of misdiagnosis. If you're tracking multiple indications where the patient stability rule differs, this parameterized input ensures each variation is handled accurately and efficiently
Line of Therapy (LoT) Rules such as - Grace periods for market basket drugs to ensure accurate treatment tracking, Product episode creation rules to define treatment phases, Line advancement logic (e.g., an addition or substitution of a drug is a line change, whereas discontinuation is not), Regimen creation rules to structure therapy pathways effectively.
..and many others, such as exclusions based on comorbid conditions

This parameterized approach allows our pharma clients to modify business rules dynamically based on real-time market insights, ensuring more accurate analytics and faster decision-making.

2. Automated Quality Controls for Accuracy & Efficiency

Ensuring data integrity while maintaining speed is always a challenge. Claims360 embeds automated quality checks at every stage of the workflow:

Input Data Validation

Error Detection & Anomaly Checks
– Our standard Data Quality Management (DQM) framework helps maintain completeness, consistency, and accuracy by applying expected benchmarks. For example: Checking that patient ID and claim ID are not null; if they are, flag and quantify the impact, running outlier detection on key attributes like Days of Supply (DOS), and similarly applying additional quality checks across other attributes
Data Restatement Checks
– Before starting the data processing, compare incoming data feeds against previous ones to make sure there are no unexpected discrepancies that could skew the analytics

Data Processing Checks

Specific quality checks are built into the business rules implementation phase to ensure the logic is being applied correctly. For example, in Line of Therapy, we apply checks for various edge cases to confirm that line advancement and regimen naming are correctly implemented in the patient journey
Further, automated checks ensure patient and HCP demographics, control totals, and other key metrics are aligned with expected ranges

Report Benchmarking Against Historical Trends

Automated comparison of the latest reports against the previous version to track percentage changes and spot any data anomalies. This helps flag potential inconsistencies early and ensures accuracy before the reports get published for end users

By catching issues upfront, Claims360 makes it easier for teams to trust their data, leading to more reliable insights and better decision-making.

3. Large Language Model (LLM) based, Self-Serve Insights for Faster Decisions

Traditional claims analytics often involve multiple handoffs between analysts, vendors, and commercial teams, leading to delays. Claims360 solves these challenges through LLM-based, self-serve analytics, enabling teams to get the insights they need in real time.

Natural Language Processing (NLP) for Instant Insights
: With Claims360’s intuitive interface, business users can explore data instantly, without relying on technical teams
Automated Visual Dashboards:
Insights are presented in intuitive graphical formats with key takeaways, enabling commercial and access teams to make informed decisions quickly
Eliminating Stakeholder Dependencies:
Insights that once took days can now be generated in seconds

By integrating LLM-powered, self-serve tools with claims analytics, Claims360 empowers business users to independently access critical insights in seconds, eliminating delays, reducing dependencies, and dramatically accelerating decision-making across commercial teams.

ProcDNA

, we know that claims data is the key to commercial success by adjusting the brand strategies in real-time. Want to see how Claims360 can help your team move at the speed of the market? Let’s connect and explore a tailored solution for your business.