In this series on quality measurement, we will discover and discuss measure development from definition to implementation and discover why quality measures are so impactful for clinical data registries.
Quality means that care delivered has value: It is safe, effective, focused on the patient, timely, and equitable. That’s why quality measures are so important.
Quality measurement is a major component of measurement science and healthcare quality – especially in clinical registries. These methodologies are responsible for the creation and implementation of performance metrics, development of testing methods, and other quality-promoting activities. 
In this post we will cover:
Quality measures are defined as metrics that quantify healthcare processes, outcomes, patient perceptions, and organizational systems related to high-quality healthcare.
Without quality measurement, it is difficult to ascertain whether processes and interventions intended to improve health are effective, safe, efficient, equitable, or timely.
Quality measures provide an objective way to quantify adequacy and appropriateness of care while identifying strengths and weaknesses of a healthcare system.
Increasingly, quality measures are being deployed in value-based care programs such as the CMS Quality Payment Program/Merit-based Incentive Payment System (MIPS). We also use measures not only to improve the quality of care and reduce cost, but also to minimize disparities in care.
The Centers for Medicare and Medicaid Services (CMS) has outlined a blueprint for quality measurement. This is intended to walk measure developers through the process of creating and maintaining quality measures.
While there are many different sub-types of measures, measures generally fall under three categories:
One could argue that there is a fourth major category that falls outside of the Donabedian model, which are balance measures — metrics that ensure improvements to one part of the system are not having an adverse impact in other areas. 
All are crucial for quality healthcare delivery, and all are part of today’s most impactful clinical registries.
Process measures are the most common type of quality measures. They evaluate transactions between patients and providers. Process measures represent specific steps taken to achieve a positive improvement or the reduction of a harmful outcome.
Examples of process measures include measuring the percentage of women who have had mammograms or the percentage of patients who had their hemoglobin A1C checked for diabetes control.
Structural measures assess the context in which healthcare is delivered and evaluate the quality of the healthcare setting. An example of a structural measure is whether a practice uses Certified EHR Technology or whether they use e-prescribing to send patient prescriptions to pharmacies.
While they are the hardest to evaluate, outcome measures are arguably the most valid since they assess the effectiveness of healthcare on patient populations.
Determining the root causes of an outcome in a healthcare setting can be challenging because many factors other than medical intervention can influence a particular outcome.  Hence, outcome measures should be seen as a way of indirectly assessing the quality of care and should be evaluated in the context of the processes and structure that produce the outcome.
An example of an outcome measure is the Hospital-Wide, 30-Day, All-Cause Unplanned Readmission (HWR) Rate for the Merit-Based Incentive Payment Program (MIPS) Groups. This population health outcome measure serves as the attribution metric for clinicians in MIPS and assesses the readmission rate of patients within five specialty cohorts.
While all quality measures generally have the same makeup, the data sources and administration of quality measures can differ.
Historically, all quality measures were collected in a manual form, but some modern measures can be calculated solely through the use of electronic data from the electronic health record (EHR). These are known as electronic clinical quality measures (eCQMs).
Clinical quality measures use a variety of data sources including the electronic health record, but most often include manual chart abstraction. This requires measure flows to aid an abstractor in finding data for the measure in different areas. Clinical quality measures can use a wide variety of data and sources but require the most human intervention in collection and reporting.
Electronic clinical quality measures (eCQMs) are tools that help measure and track quality-of- care services that clinicians and hospitals provide using structured data produced by a provider's EHR. Using eCQMs helps to reduce error, cost, clinician burden, and helps to promote efficient and effective care.
However, eCQMs are constrained to data stored in the EHR, which limits utility. While the number of eCQMs currently available are limited, the move to include more structured data in electronic health records and the mandate from CMS to move to digital measures will cause growth in this area. 
Quality measures are collected through many means, including administrative claims, assessments, chart abstraction, and registries.
The CMS Quality Payment Program (QPP) has identified a special registry type, known as the Qualified Clinical Data Registry (QCDR) intended to develop medical specialty-specific quality measures to evaluate value-based care. The Qualified Registry (QR) also provides a mechanism for evaluating value-based care but is limited to implementing wide-reaching MIPS measures.
As a repository for clinical data and an engine to calculate outcomes, registries are integral in the function of many quality and accountability programs including the CMS QPP and the NCQA HEDIS program.
Quality measures are the first tool in your quality improvement arsenal and the registry can provide insights on whether healthcare processes, products, treatments, therapies, and practitioners are performing well.
Quality measures are the cornerstones of many quality programs including regulatory reporting, performance improvement, and registry program management. These metrics provide objective approaches to data collection, healthcare benchmarking, and outcomes reporting.
In our next blog in this two-part series, we will dive into the creation of quality measures, pitfalls to avoid, and resources to help you be successful.
Check out the following articles for more like this.