Concerned that any "land mines" missed in linear review could surprise company executives at the hearing heavily covered by the press, the General Counsel engaged H5 to urgently take another look using H5 Key Document Identification services. Read the results.
This H5 case study describes in detail the process of using advanced search and analytics to find key documents for a high profile healthcare fraud investigation.
Learn how H5 Key Document Identification helped a client who needed to quickly establish key facts and find evidence to support counterclaims in a contract breach lawsuit in this case study.
Learn how H5 helped a Fortune 500 health insurance provider on a tight deadline complete a massive review—on time and on budget for—an HSR Second Request.
Learn how H5 used Key Document Identification to help a large health insurance provider pursue an internal investigation involving potentially concealed employee conflicts of interest with external vendors.
Learn how H5 used Key Document Identification to help a Fortune 100 health insurance provider manage a large investigation that could have triggered False Claims Act violations.
With broad experience in the healthcare domain, H5 quickly finds the key documents needed for fact-finding and review for production in litigation, investigations and regulatory requests.
In a large antitrust matter, H5’s expert search team used proprietary tools and methods to surface documents that a large case team had been unable to find for months —and might never have found.
For internal or government investigations, rapidly surfacing the key facts is critical to the outcome. Learn how H5 has helped companies avoid surprises by identifying the information that can help counsel establish the evidentiary trail, assess risk, and formulate an effective response.
Keyword-based data reduction is often maligned as being imprecise and ineffective. But with experts at the helm who know how to construct keyword search that can achieve simultaneously high recall and precision (finding what’s needed without over-inclusion), data volume can be significantly reduced.