WHITE PAPER

IMPROVING LEGAL TEAM EFFECTIVENESS WITH DIGITIZED LITIGATED CLAIMS INFORMATION

Ajit NairVice President, Insurance Platform Services, EXL

lookdeeper@exlservice.com

Adolfo Canovi Vice President, Global Products, EXL

Written by

July 30, 2018

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Digitally Transforming Information

Insurers rely on documents to determine payouts and provide support for or against insurance claims.

The often-critical information in these documents is frequently extracted using tools that have changed little in the last 70 years. Some of these approaches have been digitized to enable quicker access, easy storage and quick copying. However, this approach reaches its limits when the volume, complexity or processing speed of documents needs to be scaled up.

Changing from a document-centric to information-centric approach addresses this problem by achieving seamless scalability. Leveraging digital intelligence can improve storage, handling, and analytics. Digital transformation of

document processing extracts relevant information and converts them into actionable structured repositories, such as databases, and produces these outcomes:

1. Reduced document production costs2. Consistent application of document production

guidelines3. Easy access and distribution4. Improved compliance, leading to document

disclosure in-line with trial strategy5. Analytics that identify trends, such as how are

information security requirements evolving over time, or identifying a type of claim to focus on using the Pareto principle

6. Easy integration into multiple computer systems for automated business transaction processing, decision support, agreement enforcement, compliance and other areas

Most of the value of changing information extraction processes comes from making the information more actionable. For example, finding the location of a limits of liability paragraph has low actionability, while extracting the limit value and impact on the litigated matter, or the impact on case strategy, has higher actionability. Similarly, medical records with a diagnosis have low actionability while diagnosis codes have higher actionability, and pictures of a bill have a smaller value than an itemized bill that includes the type and amount of charges.

Claim litigation depends on quickly and accurately extracting information from large volumes of documents. However, this is often done using obsolete approaches. Optimizing the extraction process and compiling information into a database can achieve significant benefits, such as information consistency, easy access, improved compliance, straightforward analytics, better reusability, and increased integration into multiple computer systems. This can be made possible using new process-specific platforms.

IMPROVING LEGAL TEAM EFFECTIVENESS WITH DIGITIZED LITIGATED CLAIMS INFORMATION

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natural language processing. In many cases, this requires some structured manual review. Each technique helps with one step of the process, building on each other to improve the process as a whole.

Information Extraction Components

Extracting actionable information requires a complex combination of tools and techniques ranging from simple optical character recognition (OCR) to deep learning and

Steps to restructure and digitize the claims litigation process

1. Streamline record inflow

2. Use technology to extract text, clean data, insert

document metadata and organize records for easy access

and quick search

3. Adapt the litigation processes to take full advantage

of digitized and structured information and their

supporting platforms

SEGREGATE CRITICAL RECORDS TO CASE STRATEGY: Deposition summaries

Client project records

Exhibits

Case law

Records evidencing event chronology

Missing records that drive discovery requests

IDENTIFY RECORD ELEMENTS WITH RELEVANT INFORMATION FOR THE LITIGATED CLAIM:

Identify all parties involved

Establish high-level record chronology

Email communication between parties involved

Contract clauses relevant to the matter

Elements evidencing breach of contract, or lack thereof

Changes to project records that highlight scope,

timeline, budget, or other changes

Gaps in information that warrant further

legal research

Missing information that drives discovery requests

Compliance or non-compliance with key statutes

and regulations

Information that establishes precedent

(case law support)

EVALUATE CASE PROGRESSION WITH CLEAR VIEW ON RESEARCH NEEDS AND CASE STRATEGY STRENGTH ACROSS ALL RECEIVED INFORMATION TO ACHIEVE THE CASE STRATEGY AND OBJECTIVES:

Litigation objectives including primary

and fallback positions

Use of experts

Identifying themes to be followed

Key exhibits

Deadlines and important dates

Witnesses and other key parties

Case law

Records strategy

4. Build a repository of digital closed cases enables running

analytics across multiple matters, helps manage trends, and

identifies opportunities for improvement

Implementing Information Extraction for Litigated Claims

• A few suggestions can help carriers on their implementation journeys.

• As mentioned before, adding a structured manual review or manual extraction can still provide significant benefits.

• In order to take maximum advantage of the information contained within claim litigation records, the complete cycle from intake to case closure needs to be digitized. The following steps provide high level guidance on approaching this exercise.

• There are separate tools that can support each step, but a multi-tool workflow presents compatibility and maintenance challenges. An integrated solution spanning the lifecycle of claims litigation records is much more desirable.

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The business benefits of this kind of approach are transformational. In cases of litigated claims, extracting specific information allows in-house counsel to save time and draft a litigation strategy much sooner, including but not limited to identifying key parties, confirming claims scope against coverage provided, flagging potential fraud, and assessing settlement or mediation options. Enhancing the speed of assessment and spending more time on case strategy can improve outcomes. In instances where caseload constraints lead to carriers outsourcing this work to law firms, such specialized data extraction allows carriers to concentrate more cases in-house, which directly reduces legal spend per claim.

Over the last 10 years, techniques emerged that add value by automatically converting text into information elements. These techniques are mostly based on neural networks or probabilistic approaches like Bayesian inference. Today they are called deep learning or AI. There is no doubt that these techniques will continue to evolve and improve their accuracy over the next few years.

As an example of how these techniques can solve accuracy problems while vastly reducing work eff ort, consider the task of extracting comparatively static fields such as an individual’s date of birth, SSN, or patient ID from a large volume of medical records. This could involve natural language processing algorithms confronting type I errors such as false positives (or not finding the date), and also negative type II errors (including finding a date when there was no date). This oft en happens due to the noise in the inputted text that oft en comes from OCR/ICR tools applied to non-standard medical records.

To be more specific, the text could already contain an error where the date is incorrect, blank, or unreadable, while a readable date is recognized instead. Another possible problem is misaligned text lines that do not preserve the text’s intended sequence. All these factors make extracting an individual’s date of birth, SSN, or other piece of information from a medical record challenging. Naturally, extracting the potential impacts on a ligated matter from a limits of liability section of a contract is much more complicated.

These complicated cases benefit from using multiple overlapping techniques and adding structured manual review or manual extraction. This may only be necessary initially, as the improved accuracy will drive higher quality learning inputs with machine learning.

Optimal Solution: A full Platform Supporting Claims Litigation

• Platform specialized in claims litigation record handling throughout the litigation lifecycle

• Streamlined upload of batches of records received through discovery waves

• Incorporates low-cost solutions for organizing documents and identifying their metadata

• Organizes documents in folders as a way to work on multiple tasks in parallel

• Ability to review record content and identify elements that have significant impact on case outcomes

• Identifies research and tracks its completion

• Assesses case progress with dashboards showing strengths, weaknesses, and records counts as they are received, organized, and read

• Management reporting to identify trends and assess productivity of vendors and team members

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should include support for claims litigation combining purpose-built technology with domain expertise to unlock latent data and allow information processing in claims litigation to move at an industry-leading pace, while providing content analytics that provide decision-support on crucial process steps.

Implementing information extraction for litigated claims

While generic document management platforms can be deployed and used broadly, they require significant customization to support the complex workflows underlying claims litigation processing. Even after extensive modification, generic platforms mainly yield a limited perspective and fail to provide the information-centric view that’s required to derive insights that drive specific actions impacting claims litigation spend. As an example, the e-discovery space is crowded with firms offering data extraction services. However, the narrow focus they place on forensics and document production makes customizing these platforms for alternate, lower-volume activities an economically unviable option.

Carriers should consider an integrated solution or platform and a careful holistic implementation approach that is summarized in this paper’s appendix. Such a platform can provide a straightforward path to implementing information extraction for litigated claims, significantly reducing the lead time spent by legal professionals in delivering early case assessment. This

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