A healthcare payer cured its unstructured contracts
2,500
The company had 2,500 unstructured contracts.
8 hours
Each contract required 5-8 hours of analysis.
20k hours
The company saved an estimated 12,500-20,000 analysis hours with automation.
At a glance
Client
Healthcare claim processor
Industry
Insurance
Our role
Help process unstructured contracts
Our solution
Automated contract analysis
Automation for efficiency and revenue capture
Scenario
A healthcare claim payer needed to digitize data from thousands of unstructured contracts.
Approach
Grant Thornton converted the divergent contracts into a single source of data.
Result
The automated contract conversion saved up to 20,000 hours of analysis.
Scenario
Thousands of unstructured contracts
In healthcare, contracts and claims can be very complex. An organization that processed healthcare claims for US veterans needed to clarify this complexity. The organization received and paid claims based on 2,500 different contracts with healthcare providers nationwide.
Many of the contracts were signed decades ago, and there was no efficient way to check the amount due on a claim, or to check a contract’s compliance with current claim standards. The contract formats also varied from one provider to another, so there was no single path to convert the unstructured information into standardized data. The claim payer needed an efficient way to both check payment details and confirm compliance across thousands of unstructured contracts.
Approach
Centralized automation
The Grant Thornton team proposed an automated solution to analyze and convert the contracts. To do that, the team had to start by turning divergent contracts into a single source of data.
“One of the challenges was trying to develop models that were broad enough to capture all of the different language that people used, but that conceptually meant the same thing,” said Grant Thornton Advisory Services Senior Associate Cody Root.
To build these models, the team called upon industry expertise. “We actually had somebody on our team who had spent time managing programs for healthcare organizations prior to joining Grant Thornton,” said Grant Thornton Advisory Services Partner Tony Dinola. “We used contract lifecycle management (CLM) automation for the contract intelligence component, and we used our own table extraction capabilities that are built on AWS to automate the analysis required for reading the details within the contracts and doing compares against the healthcare program.”
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Result
Efficient standardization
The Grant Thornton team completed the automated analysis and conversion to clarify the claim payer’s contract terms with:
- Estimated 12,500-20,000 analysis hours saved, based on 5-8 hours required to analyze each of the 2,500 contracts
- Revenue leakage controlled by giving payment processors quick access to the contracted payment amounts
- Compliance assured by using an automated comparison of contract payment terms with claim requirements
- Contracts identified that were below or above the current compliance requirements
“I think a significant pain point for almost any corporation is the ability to actually make sense of unstructured data in their environment,” Dinola said.
“The value of information that is contained within legal contracts — whether that be on what we refer to as the buy-side or vendor base contracts, or on the sell-side or customer base contracts — is enormous,” Dinola said. “But, the time required to do the analysis of unstructured data and compare that to structured transactional data is largely not feasible in today's environment without the right tools and technologies.”
The healthcare claim payer now has a single source of truth to verify its claim payments and compliance, with CLM automation replacing up to 20,000 hours of contract analysis.
“Commonly the feedback we’re hearing is, one: ‘I never thought this was possible,’ and two: ‘The return on the investment is enormous — because you are giving me a cost-effective solution that’s oftentimes driving 10, 12, 15x of return,” Dinola said. Listen in as Dinola and Root recount the thinking behind our approach:
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