The Basics of Clinical Quality Measures (CQMs) for Registries
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. [1]
In this post we will cover:
- What Is a Clinical Quality Measure (CQM)?
- Why Quality Measurement?
- Types of Quality Measures
- The Evolution of Quality Measures
- What Role Do Registries Play in Quality Measurement?
What Is a Clinical Quality Measure (CQM)?
Quality measures are defined as metrics that quantify healthcare processes, outcomes, patient perceptions, and organizational systems related to high-quality healthcare.
The structure for measurement was created in what is known as the Donabedian model, a conceptual model that provides the framework for evaluating quality in the healthcare setting. [2]
To achieve the ends of quality improvement, quality measures must help evaluate whether a metric meets certain healthcare quality criteria defined in the Institute of Medicine’s six domains [3]:
- Effectiveness: Relates to providing care processes and achieving outcomes as supported by scientific evidence.
- Efficiency: Relates to maximizing the quality of a comparable unit of health care delivered or unit of health benefit achieved for a given unit of health care resources used.
- Equity: Relates to providing health care of equal quality to those who may differ in personal characteristics other than their clinical condition or preferences for care.
- Patient centeredness: Relates to meeting patients’ needs and preferences and providing education and support.
- Safety: Relates to actual or potential bodily harm.
- Timeliness: Relates to obtaining needed care while minimizing delays.
Why Quality Measurement?
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.
We also use measures not only to improve the quality of care and reduce cost, but also to minimize disparities in care.
Types of Quality Measures
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:
- Process measures
- Structural measures
- Outcome measures
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
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
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.
Outcome Measures
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. [2] 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.
The Evolution of Quality Measures
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 (CQMs)
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)
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. [4]
What Role Do Registries Play in Quality Measurement?
Quality measures are collected through many means, including administrative claims, assessments, chart abstraction, and registries.
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.
The Value of Registries in Post-Market Surveillance
Clinical data registries are useful across many parts of healthcare. One of their most valuable purposes is post-market surveillance.
Registries are designed to collect, analyze, and interpret large amounts of real-world data setting the foundation for successful post-market surveillance. When registries are built well, the result is a vast data resource that provides real-time, ongoing data collection, and is a central source of truth to answer key questions like:
- How is a device or drug actually used in real-world settings?
- What does the typical target patient look like?
- How does a product truly perform for the target patient population versus sub-populations that differ by age, gender, race/ ethnicity, geography and combination of comorbidities?
Let’s dig into how registries are used in post-market surveillance, starting with some key definitions.
In this post we will cover:
- What Is Post-Market Surveillance?
- What Are the Goals of Post-Market Surveillance?
- What Is the Value of Post-Market Surveillance?
- How Do You Conduct Post-Market Surveillance?
- How Do Clinical Registries Drive Post-Market Surveillance?
What Is Post-Market Surveillance?
Post-market surveillance (also called post-marketing surveillance) demonstrates the real-world safety and effectiveness of medical devices and pharmaceuticals.
The U.S. Food and Drug Administration (U.S. FDA) defines it as “the active, systematic, scientifically valid collection, analysis, and interpretation of data or other information about a marketed device.” [1]
This definition underscores that post-market surveillance is an active, ongoing process – not a one-off. It also emphasizes data science as a critical part.
The European Medical Device Regulation (E.U. MDR) defines it as “all activities carried out by manufacturers in cooperation with other economic operators to institute and keep up to date a systematic procedure to proactively collect and review experience gained from devices they place on the market, make available on the market or put into service for the purpose of identifying any need to immediately apply any necessary corrective or preventive actions.”
Similar to the U.S. FDA, the EU MDR also highlights the need for a post-market surveillance system that provides up-to-date insights. But it goes a step further to call out the importance of using those insights to make adjustments and improvements.
What Are the Goals of Post-Market Surveillance?
Pharma and medical device companies rely on the real-world insights they gain through post-market surveillance activities to:
- Detect adverse events or risks as they arise during real-world usage of a device or drug.
- Compare new products or treatments with existing options and the standard of care.
- Update clinical guidelines as certain populations or groups find more benefit than others.
- Comply with regulatory requirements.
There are many different aspects of a device or product that are assessed in post-market surveillance. A few examples include:
- Clinical effectiveness: Use data from real-world clinical settings to examine the relative effectiveness of a device or drug in a large, diverse patient group to compare that product to the standard of care or competition.
- Adverse events and side effects: Leverage real-world evidence to identify risks or adverse reactions that might have been missed in the initial clinical trial for a device or drug.
- Utilization: Examine how a product is actually used in the real world, which can be different than what is approved or marketed.
What Is the Value of Post-Market Surveillance?
Real-world evidence delivered through post-market surveillance offers differing importance for different stakeholders.
For medical device companies and pharmaceutical companies, post-market surveillance can provide additional information on the natural history of disease and the relative performance of their product to the disease, as well as other marketed products. The veracity of evidence acquired through an appropriately designed registry or post-market surveillance program enables the sponsoring company to then tweak clinical, marketing and pricing strategies accordingly to develop new and improved patient-centric products.
For clinicians and provider organizations, post-market surveillance can provide information on how products perform relative to others in the context of real-world patient populations, which are often materially different from clinical trial populations. This can then inform treatment decisions, including off-label use of products.
For payers and regulatory agencies, post-market surveillance can shed light on which products and drugs are the safest and most cost-effective. This can then inform coverage and reimbursement decisions.
How Do You Conduct Post-Market Surveillance?
Let’s break down the U.S. FDA’s definition of post-market surveillance into three components:
- Data Collection
- Data Analysis
- Data Interpretation
These are the steps to conducting post-market surveillance for medical devices and drugs.
Step 1: Data Collection for Post-Marketing Surveillance
You need the right data sources and the right technology to collect real-world data efficiently, securely, and accurately for post-market surveillance activities.
These real-world data can come from several sources, such as:
- Clinical data from electronic health records (EHRs) and case report forms (eCRFs)
- Patient-generated data from patient-reported outcome (PRO) surveys
- Cost and utilization data from claims and public datasets
- Public health data from various government data sources
These real-world data are collected in two primary ways:
- Machine-to-machine data connection allows you to securely and efficiently collect real-time data from any number of systems, including electronic health records and administrative claims.
- Human-to-machine data collection allows you to capture essential care details and specialized information with electronic case report forms. It also allows you to collect patient-reported outcomes (PROs) or gain insights directly from patients, clinicians, and others through web-based surveys.
The technology you use to get this data matters. You need an efficient and secure way to collect large amounts of real-world data and prepare it for analysis.
Step 2: Data Analysis for Post-Marketing Surveillance
Once you collect data for a given device or product, the next step is transforming it into real-world evidence. This involves combining, blending, validating, and analyzing various data sources. These analyses can be risk- and reliability-adjusted according to the data ingested, and then presented to stakeholders in an interactive and engaging way.
Step 3: Data Interpretation for Post-Marketing Surveillance
Once you are finished collecting and analyzing data, you need a way to interpret the data easily and readily. Here are a few things to keep in mind:
- To ensure timely and accurate interpretation of data, you need statistically-adjusted reports that are available in real-time. This helps you quickly and easily measure and understand clinical outcomes, determine patient quality of life, and understand the total financial impact of the intervention.
- Statistically-adjusted reports should be built using key risk and reliability adjustment methodologies that are essential to success in maximizing data value.
How Do Clinical Registries Drive and Use Post-Market Surveillance?
Clinical data registries are uniquely useful in their ability to facilitate post-market surveillance for three reasons.
- They advance post-market surveillance activities to help us better understand the real-world safety and effectiveness of procedures, treatments, and devices.
- They solve complex problems for device and pharma companies. These organizations in general are faced with complex barriers when seeking real-world data for purposes of research and development, quality improvement, or regulatory requirements.
- They deliver the comprehensive technology needed to build a registry that collects real-world data, transforms it into real-world evidence, and makes it accessible and useful for decision making.
There are a few ways medical device and pharmaceutical companies are using registries to support their post-market surveillance programs.
- Accessing data from an existing registry.
- Partnering with an existing registry that is running registry-based clinical trials.
- Leveraging registry technology to support a registry-based study.
- Creating new registries and using existing registry data to support a full post-market surveillance program.
Industry-Leading Registry Technology for Post-Market Surveillance
As medical technology and research continues to advance, the importance of tracking and reacting to real-world evidence through post-market surveillance will only continue to grow. Registries will play an integral part in this process, and their analytic potential will serve as an essential asset for patients, providers, and industry partners alike.
ArborMetrix offers an industry-leading, proven registry solution for post-market surveillance that meets the needs of medical device manufacturers, pharmaceutical companies, medical specialty societies, and clinicians.