Quality measures in healthcare

Quantify the success of healthcare processes, outcomes, patient satisfaction and more

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What are quality measures in healthcare?

Creating quality measures in health care is the practice of using data to assess hospital and health systems performance based on various quality standards. Quality standards are benchmarks that help healthcare organizations measure resource utilization to support improvement and collaborative efforts with objective, fact-based information. There are many types of quality measures that evaluate care for everything from inpatient facilities to imaging facilities to outpatient clinics to entire hospital systems.

Quality measures can be established by a particular healthcare institution or they can can rely on measure specifications or accreditations developed by government agencies, private nonprofits as well as some for-profit companies. Once a healthcare organization sets its quality goals for patient care and other processes, it then develops strategies to meet or exceed those goals. Quality measures can include the organization’s ability to provide effective, safe, timely, patient-focused and equitable care.

While healthcare organizations may have quality measures specific to their institution, outcome measures are generally established by large third-party organizations, such the Centers for Medicare & Medicaid Services (CMS), the National Association for Healthcare Quality (NAHQ) or the National Quality Forum (NQF).

Healthcare institutions may decide to exceed mandated quality measures to not only benefit patients’ quality of care and healthcare delivery, but to help with an institution’s marketing and contracting efforts.


Why are quality measures important?

Because healthcare costs have risen dramatically in the past 50 years —outstripping general cost-of-living increases —payers like individuals, governments and health plans want verifiable quality measures that provide evidence that these increases are justified.

Measuring the quality of healthcare can:

  • Guard against abuses and misuse of healthcare services
  • Ensure patient safety
  • Reveal areas where interventions might improve care
  • Identify how and where performance improvements should be made
  • Help patients make more informed choices about their health status and the care they receive
  • Provide objective data for policy discussions about healthcare programs and investments
  • Act as a test to see what healthcare processes, clinical practices and techniques work the best to drive constant improvements
  • Keep healthcare providers, insurance plans and other stakeholders accountable for higher standards of care
  • Shed light on inequalities in levels of service from different providers and different geographic regions

Using software for quality measures in healthcare

Measuring quality means collecting and analyzing data. But an exponentially expanding volume of available health information makes data collection —let alone analysis —challenging. Additionally, the digitization of healthcare, accelerated by the deployment and adoption of electronic health records (EHRs), is now being exploited and augmented with technologies like mobile, social, cloud computing and analytics.

Because of the dramatic increase of available data sources, metrics and their complexity, healthcare organizations are looking to software to make it easier sift through this information. These organizations are looking for a better way to analyze their true costs, quality of the service, risk models and segmentation of their populations as well as improve relationships with the people they serve. Software tools specifically designed for the healthcare field can analyze and glean the relevant information from this ocean of data and help practitioners, researchers and caregivers discover, analyze and act on performance measurement information in innovative ways.

Some software packages incorporate artificial intelligence (AI) capabilities that fundamentally change the way humans and computers interact. With the "learning" abilities of AI, the software can accelerate work in clinical research, genomic studies, personalized medicine as well as assist with the transition to more person-centered care. These cognitive capabilities can significantly extend insight and knowledge by providing expert assistance in a clinician’s or caregiver's workflow, which can help organizations dramatically change how and where care is delivered.


Key features of effective quality measures

"The Agency for Healthcare Research and Quality (AHRQ) has outlined the three types of quality measures using the Donabedian classification model 1. Measures are classified as structure, process or outcome measures.

Structure measures: Structural measures are the health care provider’s overall ability to provide high quality care: the institution’s capacity, systems and processes. These measures may include whether the organization uses more modern electronic medical records and medication order entry systems. Measures could also include how many board-certified physicians are on staff or the ratio of providers to patients.

Process measures: Process measures are what the institution does to maintain or improve health. These measures can reflect both preventative measures or those already experiencing health issues. These measures should evaluate how the institution is meeting generally accepted standards of practice. Most health care quality measures in public reporting are process measures.

Outcome Measures: Outcome measures reflect the impact the institution’s patient interventions and how they improved the health of patients. Examples of outcome measures include rates for surgical mortality, patient readmission, surgical complications and hospital-acquired infections.

Though a focus on quality measures and quality assurance has the potential to help improve healthcare, some healthcare professionals 2 have warned that there's only limited evidence that many quality measures or performance measures result in improved health outcomes —including those tied to incentives by insurers or governments. Despite this limitation, quality measures and comparative quality ratings continue to increase in popularity because the measures are based on intermediate endpoints, such as risk-factor control or care processes that are vulnerable to gaming, over-testing and over-treatment. Instead, quality measures should be based on patient-centered outcomes with individualized approaches to clinical complexity.


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