Do You Collect the Right Data For Defending Your Paid Claims? | Healthcare Informatics Magazine | Health IT | Information Technology Skip to content Skip to navigation

Do You Collect the Right Data For Defending Your Paid Claims?

September 29, 2010
by Phil Hurd and Bo Martin, PH.D.
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In The Age of Healthcare Reform, Hospitals Need to go Above and Beyond Meeting Minimal Data Requirements

A familiar adage about collecting data for healthcare insurance claims says, “If you don't need it to get paid, then it won't be saved.” In the age of healthcare reform, this adage is no longer true and has even become a risk.

Phil Hurd
Phil Hurd

Providers now need to save far more data beyond what is just required for a claim to be paid. Providers must collect-and have readily available-detailed information to assist supporting the paid claims in the case of an audit. Additionally, as more and more providers are completing their own internal audits, which sometimes result in self-disclosures, key data elements are critical for completeness of their audit findings and conclusions.

Bo Martin
Bo Martin

The processes for collecting data for claims to be paid are already in place. Equally important are the priorities for collecting other data to keep the payments received, if later reviewed by an auditor who demands supporting details.


A significant portion of health reform is focused on curtailing healthcare fraud. As such, the Centers for Medicare and Medicaid Services (CMS) has reinforced numerous components of its program focused on fraud and proactive audit activity. Three types of contractors play a key role in this regard:

Zone Program Integrity Contractors (ZPICs) audit claims data across all providers to determine if, for example, Medicare and Medicaid claims filed for dual-eligible beneficiaries are not being billed for the same services twice.

The Recovery Audit Contractors (RACs) review claims from any provider who was paid by Medicare, identifies net overpayments and recoups them on an automated or complex review basis.

Comprehensive Error Rate Testing (CERT) contractors analyze paid claims and calculate an error rate. They then can assess medical records and recoup overpayments.

As these contractors-who work in tandem with the Medicare Administrative Contractors and the Office of the Inspector General-become more sophisticated, providers will have to be at least equally prepared.


In addition to the “overpayment” police, health reform is placing health information demands and options on providers in a variety of areas. These include disease management, health information technology for care coordination and home telemedicine, quality of care measures, and quality of outcome measures. These trends and the continuing migration to electronic medical records contribute to the information challenge and questions of, “How much is too much?” and “What data elements should I start collecting and storing now?” Here are a few practical examples from some recent specific client experiences:

Pre-authorizations for medical necessity of an admission-Some state medical assistance programs require pre-authorizations for certain admissions. Even though a pre-authorization number is issued, it does not preclude the payer from reviewing the documentation supporting the admission at some point in the future. The documentation in the medical record must match the information reported to the state when obtaining the pre-authorization. The state is likely to ask such questions as: Is there a link between the reason noted in the request in the pre-auth system and the medical record documentation? Does it match? Is it captured electronically to allow for validation?

Last patient to get medicine for waste billing compliance-CMS has many payment policy requirements. One potentially difficult one to track is the billing of drug waste for a multi-dose vial. Providers need to consider such questions as, “Do I track and save the number of doses for a given drug provided on a given day?” “Is this matched to the number of milligrams contained in the number of vials billed on that same day?” “Can I capture the last Medicare patient to receive the drug to allow for proper billing of the waste as allowed by the regulations?” These types of data are critical to maintain to be able to monitor and accurately assess compliant billing of multi-dose drugs.


The 72-hour rule regarding surgical cases and “exact” ICD-9 match for separate billing-Recently, RACs have been analyzing data related to the 72-hour rule. The rule states that “all diagnostic services provided three calendar days prior to the calendar day of the admission are bundled and paid as part of the admission.” The non-diagnostic pre-admission services are only bundled if they are related to the admission and “related” means an exact match of ICD-9 digits of the principal diagnosis for both the admission and the pre-admit outpatient (OP) services. The problem is that hospitals typically code an admission as one encounter and do not code the OP services separately. RACs are looking for admissions with an OP procedure and an unrelated diagnosis code. The potentially higher paying surgical diagnosis-related group (DRG) is changed to a lower paying medical DRG. As the auditors become more sophisticated, providers must become better prepared. Capturing multiple diagnoses and being able to analyze the status of the patient is becoming more and more important in data management.

Continuity of data integrity when warehousing-In the constant attempt to save space and minimize file size, data elements are often eliminated or abbreviated. Modifiers are often not saved, line item payment amounts are bundled into a total claim payment amount for OP services, denial reason codes are ignored, and other relevant data are not captured and stored. Then, when analyzing past data, the most pertinent information is sometimes missing, hindering an efficient and accurate analysis. While capturing correct data is critical, storing the complete data is now even more important.


Another area where health informatics have evolved is in the identification of risk areas for audits-either by employing methodologies that mimic those publicly announced by the auditors, or by drilling down into levels of detail that support line-items on claims known to have been targeted.

The Program for Evaluating Payment Patterns Electronic Report (PEPPER), a Microsoft Excel file containing hospital-specific data statistics for CMS target areas that are often associated with Medicare payment errors, compares a hospital's performance across a dozen or so defined target areas, relative to other hospitals in the state, Medicare Administrative Contractor (MAC) region, and the nation. The statistics from each defined target area are calculated from a specific set of claims. A hospital should be able to identify these claims in their data warehouse and conduct their own analyses on the drivers for any occurrences appearing to be an “outlier” statistic.

When the RACs and other auditors submit their demands for supporting documentation as part of a “complex” review, and ultimately demanding repayments, it's a good idea to begin and continue conducting reviews on the completeness of responses for those claims and also for claims that have similar characteristics.

Data mining also can be used for anticipating and ultimately defending the “automated” reviews of RACs and other auditors. For example, claims with an inordinate number of time-based procedure codes in a 24-hour period can be flagged for internal review. And, the implementation of claim “scrubbers” can prevent the submission of National Correct Coding Initiative coding pairs or the duplicate billing of codes for the same patient on the same date-of-service.

In conclusion, a different adage about collecting data-“What can be measured, will be audited”-for healthcare insurance claims applies better to the new era. The question is whether a hospital is saving the right data to successfully defend paid claims and avoid potential repayments. Other benefits of doing so include greater efficiency at less cost when appealing later demands for re-payment and enhancing the reporting of quality and outcome initiatives.

Phil Hurd is a director at Navigant Consulting, based in Baltimore, Md
Bo Martin, Ph.D, is an associate director and statistician at Navigant Consulting, Chicago Healthcare Informatics 2010 October;27(10):48-50

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