By: Carol Spencer, RHIA, CCS, CHDA and Jill Sell-Kruse, RHIA, CCS

ED. NOTE: This is the second of three series of articles designed to compare and contrast the "coding-state" coding manager's role;s and responsibilities of today in contrast to the "data-state" data manager's roles and responsibilities of tomorrow. The first article discussed integration of controls and safeguards. This article compares and contrasts coding quality measures for improper payment.

When evaluating coding, it is interesting that coding managers generally access quality based on accuracy rates whereas regulatory agencies, or those contracted by the Centers for Medicare & Medicaid Services (CMS) such as Recovery Audit Contractors (RACs), evaluate coding quality based on dollars or payment error rates.

Using payment amounts to measure coding quality may also be more in line with the chief financial officer's (CFO) language of linking coding quality to dollars.

Transitioning from Coding to Data State

In transitioning from the role of coding manager to data manager, and from a "coding state" to a "data state," it is important for health information management (HIM) professionals to unleash the bonds of the infamous 95 percent coding accuracy goal that has long plagued them and move to a financial means of accessing coding accuracy.  After all, doesn't coding quality directly impact reimbursement?

The data manager of the future, as compared to the current-day coding manager, must have a thorough understanding of how external or regulatory organizations measure coding quality, particularly in relationship to payment. Medicare contractors perform medical review to ensure that coded and billed items or services are covered and are reasonable and necessary. The comprehensive error rate testing (CERT) contractor conducts medical reviews to measure inpatient hospital payment error rates.

CMS Contractor Reports

Let's look at CMS or CMS contractor reports and the common denominator among all programs.

• CERT: A national paid claims error rate (PCER) goal for the Medicare fee-for-service program is calculated and adjusted over time. The November 2009 national PCER goal for all Medicare FFS providers was 3.7 percent of which the hospital coding component totaled 0.6 percent PCER. Overpayments, underpayments and error rates are published annually.
• RAC: The RAC Program's mission is to reduce Medicare improper payments through the efficient detection and collection of overpayments, the identification of underpayments and the implementation of actions that will prevent future improper payments.
• Medicare Administrative Contractors (MACs) and Fiscal Intermediaries (FIs): Their goals are to determine that appropriate payments are made and prevent or reduce improper payments.
• CMS dashboard: This tool provides statistical views of the inpatient prospective payment system (IPPS) data as it relates to claims payment and volume
• The Program for Evaluating Payment Patterns Electronic Report (PEPPER) provides hospital-specific data for Medicare severity diagnosis-related groups (MS-DRGs) and discharges at high risk for payment errors.

In the above, the common denominator is "payment." What is the common denominator of your coding quality program's measure?  What is the numerator? As summarized above, the focus is on "overpayments," "underpayments" or, to sum it up, on "improper payments," all of which result in a Payment Error Rate or PCER.

How does this compare to the role of the coding manager in the current "coding state" and the role of the data manager in the future "data state?" Let's take a look.

Coding State

The metric of choice typically applied by coding professionals is coding or MS-DRG accuracy. For coding accuracy, the denominator is total number of codes while for MS-DRG accuracy it's the total number of charts. Where some coding managers evaluate all aspects of coding, such as the capture rate of the CC and secondary diagnosis, others may evaluate only those items that affect DRG assignment.  In either case, the numerator, the total number of correct codes, results in an accuracy score. The goal typically is 95 percent accuracy and the goal has remained static over the years. See the case example below:

• 72.1 percent MS-DRG accuracy = 31 MS-DRGs correctly assigned/43 total cases
• 76.7 percent principal diagnosis accuracy = 33 principal diagnosis codes correctly assigned/43 total principal diagnosis codes
• 76.2 percent MCC accuracy = 32 MCCs correctly assigned/42 total MCCs
• 89 percent CC accuracy = 73 CCs correctly assigned/82 total CCs
• 90.0 percent overall coding accuracy = 289 correct diagnosis codes/321 total diagnosis codes
• 93.8 percent procedure coding accuracy = 15 correctly assigned/16 total procedure codes

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