Data Capture for Performance Measures: What Coders Need to Know

by Patience J. Hoag, RHIT, CHCA, CCS, CCS-P

These days, cost control, efficiency of care, and quality outcomes are hot topics in every healthcare setting. One way to achieve quality outcomes and cost-efficient healthcare is through performance measures. This article provides a brief overview of HEDIS® and other performance measures and outlines other healthcare opportunities for coders.1

Importance of Performance Measures

Performance measures are critical tools that help determine how well organized healthcare delivery systems perform patient care. Today performance measure reporting has shifted from reimbursement-driven data reporting toward quality of patient care. Improving the quality of patient care by measuring and publicly reporting data related to that care is required by various organizations.

The Joint Commission on Accreditation of Healthcare Organizations implemented ORYX, an initiative that incorporates performance measurement data and outcomes into the accreditation process. The Centers for Medicare and Medicaid Services (CMS) use core quality performance measures under the Hospital Quality Initiative. Pay-for-performance initiatives are quickly spreading across all healthcare domains. State agencies and other proprietary entities have developed their own performance measures as well.


One set of performance measures comes from the National Committee for Quality Assurance (NCQA), which manages the Health Plan Employer Data and Information Set (HEDIS). NCQA defines HEDIS as “a set of standardized performance measures designed to ensure that purchasers and consumers have the information they need to reliably compare the performance of managed health care plans.”2

When first created, the small number of HEDIS standardized performance measures was used to compare the quality of care rendered by managed care organizations (MCOs). Currently, HEDIS is considered by many to be the gold standard for assessing MCO performance, with approximately 90 percent of MCOs collecting and reporting HEDIS data.3

In addition to private MCOs, CMS requires all Medicare Advantage managed care plans report HEDIS data. The Federal Employees Health Benefits program has also recently adopted HEDIS reporting initiatives, and numerous states that offer Medicaid managed care plans are also required to report HEDIS performance measures. In 2005 some preferred provider organizations began reporting their performance on HEDIS measures.

Member-level data, from enrollment to treatment, are used to determine if a health plan’s member will be eligible for inclusion in any given measure. Standards of practice have changed over time, and the performance measures have evolved as well. For 2007, HEDIS includes 71 measures across the following eight domains of care:

  • Effectiveness of care
  • Access/availability of care
  • Satisfaction with the experience of care
  • Health plan stability
  • Use of services
  • Cost of care
  • Informed healthcare choices
  • Health plan descriptive information

Coding systems used for many measures include ICD-9-CM, CPT/HCPCS, DRGs, UB-92 revenue or type of bill codes, and CMS 1500 place of service codes. These codes help indicate whether the member would qualify in the numerator or denominator for a given measure.

The Logical Observation Identifiers, Names, and Codes (LOINC) are also included in several measures, although not many MCOs receive data in that format. Additionally, CPT category II codes, which were created for reporting performance measures, are included in the appropriate measure to which they apply. (See “HEDIS Reporting for Breast Cancer Screening” for an example of HEDIS performance measurement reporting.)

The breast cancer screening example is a simplified version of this particular measure, but it is one of the easiest to grasp from a coding perspective. Other measures are much more complex, such as the Comprehensive Diabetes Care measure, which has nine indicators:

  • Hemoglobin A1c (HbA1c) testing
  • HbA1c poor control (>9.0%)
  • HbA1c good control (<7.0%)
  • Eye exam (retinal) performed
  • Low-density lipoprotein cholesterol (LDL-C) screening performed
  • LDL-C control (<100mg/dL)
  • Medical attention for nephropathy
  • Blood pressure (BP) control (<140/90 mm Hg)
  • BP control (<130/80 mm Hg)

Codes to Identify LDL-C Screening” illustrates the inclusion of category II CPT and LOINC codes in one of these Comprehensive Diabetes Care indicators.

Coded Data Collection: Administrative versus Hybrid Data

Codes captured via claims or encounter data are referred to as administrative data. Many measures use administrative data exclusively to identify health plan members for inclusion in the eligible population (denominator) or who are found to have received the service identified in the numerator. For other measures, the hybrid method (both medical record review and administrative data) may be used to determine numerator compliance.

Using the hybrid method to collect data for HEDIS can be both expensive and time-consuming for MCOs. They need to acquire access to the records from practitioners who provide care to the member, either by obtaining copies of the records and abstracting them upon receipt or by sending abstraction teams to the provider offices. Because of the increasing use of the electronic health record, medical record review may become less labor-intensive for both providers and MCOs.

HEDIS Reporting for Breast Cancer Screening

Eligible population: Women 42–69 years of age as of December 31 of the measurement year (two age stratifications: 42–51 and 52–69). Total rate is the sum of the two numerators divided by the sum of the two denominators.

Continuous enrollment: Members must be continuously enrolled during the measurement year and the year prior to the measurement year.

Denominator: Eligible population

Numerator: One or more mammograms during the measurement year or the year prior to the measurement year.

Exclusions: Women who have had a bilateral mastectomy by December 31 of the measurement year (looking as far back as possible in the member’s history) and for whom administrative (claims) data do not indicate a mammography was performed.

Codes to Identify Breast Cancer Screening

CPT HCPCS ICD-9-CM Procedure UB-92 Revenue

77052, 77055–77057


87.36, 87.37, V76.11, V76.12


Codes to Identify Exclusions



ICD-9-CM Procedure

Bilateral mastectomy

19303-50, 19305-50, 19306-50, 19307-50

85.42, 85.44, 85.46, 85.48

Unilateral mastectomy (members must have two separate occurrences on two different dates of service)

19303, 19305, 19306, 19307

85.41, 85.43, 85.45, 85.47

Note: Biopsies, breast ultrasounds, and diagnostic mammograms should not be counted, as they are not screening procedures.

Codes to Identify LDL-C Screening
80061, 83700, 83701, 83704, 83715, 83716, 83721 3048F, 3049F, 3050F 2089-1, 12773-8, 13457-7, 18261-8, 18262-6, 22748-8, 24331-1, 39469-2

End Results = Publicly Reportable Data

NCQA annually publishes a “State of Health Care Quality” report that summarizes the past year’s performance measurements. The report assists with monitoring performance, and identifying variations in patient care and includes recommendations for future quality patient care.

NCQA requires each MCO undergo a HEDIS audit prior to publicly reporting their data. This ensures that the data are accurate and reliable and that the data would be comparable with other MCO-audited data. These publicly reportable data can then be used by consumers and employers to assist them in determining which MCO to contract with for healthcare.

Performance Measure Proliferation

Because multiple entities have created similar performance measures, it is easy to understand how the casual observer could become confused. The smallest difference in terminology in the descriptor for a given measure could inadvertently mean incorrect inclusion or exclusion of a given population.

For example, some diabetes measures have a blood pressure management indicator. The HEDIS indicator identifies compliance for a numerator event with either a BP of <140/90mmHg or <130/80mmHg; the National Diabetes Quality Improvement Alliance has set their threshold for BP management at <140/80mmHg. As you can see, tiny differences can be hard to pick up on, unless providers and health plans are on top of the measures for which they are collecting data.

Pay-for-performance, with its built in financial incentives, will likely bring performance measures into the cross hairs of HIM data management, in both the acute care and ambulatory settings. As pay-for-performance and similar initiatives gain visibility, HIM professionals will need to keep on top of trends in data collection and should be well versed in the various versions of the performance measure domain.

Category II codes were added in the CPT book over the past year. CPT’s appendix H contains a very helpful table, which includes some performance measures and the category II codes that could be reported to capture those measure-specific data.

Future Focus on Quality versus Reimbursement

Coding professionals have expressed concern for years about the shift in their roles from data collection and data quality to bill dropping. As data collection for performance measures grows, so do opportunities for coding professionals. As experts in clinical data management, coders can be the first line of defense in collecting and extracting coded data from information systems. Our expertise is invaluable as healthcare moves into an even more data-rich environment, and the future for our profession looks very bright indeed.


  1. HEDIS is a registered trademark of the National Committee for Quality Assurance (NCQA).
  2. NCQA. “Health Plan Employer Data and Information Set (HEDIS).” Available online at
  3. NCQA. HEDIS Compliance Audit: Standards, Policies and Procedures, vol. 5. NCQA, 2006, p. 9.


National Committee for Quality Assurance. “The State of Health Care Quality 2006: Industry Trends and Analysis.” Available online at SOHC_2006.pdf.

Patience J. Hoag ( is a project leader at Health Services Advisory Group.

Article citation:
Hoag, Patience J.. "Data Capture for Performance Measures: What Coders Need to Know" Journal of AHIMA 78, no.1 (January 2007): 74-76.