Developing healthcare quality metrics based on administrative claims data has become increasingly common over the past several years. The National Committee for Quality Assurance’s Healthcare Effectiveness Data and Information Set (HEDIS) measures have been a standard for health plan quality reporting for over two decades, and more recently, newer programs such as the Centers for Medicare & Medicaid Services Pioneer ACO (accountable care organization) program and Oregon Coordinated Care Organization program have included claims based quality measures as requirements for program participation.
Most claims-based measures are process based, evaluating if appropriate services are provided for specified groups of patients, or identifying potential over-utilization of services, but claims data are not the sole source of quality measurement. Survey data are often used for patient satisfaction and operational measures, and there is increasing use of lab results and electronic medical record (EMR) data to expand the clinical components of quality that can be measured – a topic for another posting.
Despite the expansion of claims-based quality measures, some still question the merit of these measures. Those citing concerns point out known limitations associated with analyzing claims data including:
• Potential errors or inconsistencies in coding.
• Availability of required data sources may be constrained if components of benefits are administered by multiple sources.
• Lack of complete clinical information.
• No diagnostic coding for blood pressure, laboratory results or pathology results.
• Clinical information is limited to conditions for which the patient was treated and submitted a claim. A noncompliant diabetic may have no claim history of the disease.
• Timeliness of data is impacted by claim lag.
However, the advantages of analyzing claims data greatly outweigh the limitations noted above. The advantages include:
• Data are commonly available and relatively inexpensive to analyze.
• Data are available for very large populations, allowing for more robust sample sizes.
• Coding accuracy has improved dramatically over the past 20 years, and
• For some types of measures, claims may produce a more accurate picture than even chart reviews.
An example of this last point would be measures focusing on patient compliance with medications. A physician may regularly write refill prescriptions for a patient’s hypertension medication, and those refills may be well documented in the patient’s chart, but those data provide no real evidence that the patient filled those prescriptions. Tracking actual claims for prescription refills is a much better measure. Granted, submitting a claim for a hypertension medication does not prove that the patient actually took the medication at the appropriate frequency, but a regular, on-going refill pattern is a better proxy of medication adherence than chart review information.
Days supplied is commonly available on claims data making it easy to calculate “possession ratios” to monitor patient compliance from pharmacy claims. A simplistic way (additional conditions can be added to the calculation) to measure possession ratios is demonstrated in table 1. For patients continuously enrolled during a 180 day period and previously diagnosed with hypertension, the possession ratio for each patient is the sum of all days supplied on their prescriptions during the study period, divided by 180 days.
Although claims data are not perfect for clinical reporting, they will continue to be a valuable and important source of data for quality reporting for a selected set of metrics.
This article first appeared at Milliman MedInsight.