Improving the quality, safety and efficiency of healthcare is a national priority. While clinical quality measures have been around for some time, they are now taking center stage through federal, state and commercial quality/pay for performance (P4P) initiatives. Regulations such as Meaningful Use (MU), Physician Quality Reporting System (PQRS), Hospital Inpatient Quality Reporting Program (HIQRP), Accountable Care and Value-Based Payment Programs are driving healthcare organizations to implement enterprise-wide strategies to meet clinical quality reporting and evidence-based clinical decision support requirements.
While most healthcare organizations support the goals of quality improvement—enhancing patient outcomes and reducing healthcare costs—meeting those objectives across the enterprise is challenging. While organizations solve commonly acknowledged problems around data capture and complex measure calculations, there are no consistent, systematic means for measuring clinical quality management.
Multiple measures for the same condition
Organizations such as the National Quality Forum (NQF), Centers for Disease Control and Prevention and the Center for Medicare and Medicaid Services (CMS) are doing significant work toward standardizing clinical measures irrespective of the quality initiative, but there is still a long way to go. The problem is that various quality improvement programs, such as PQRS or HEDIS, are developing, standardizing and adopting evidence-based clinical quality measures on their own, which results in multiple clinical measures with slight variations for a given condition or outcome. For instance, when tracking HbA1c levels in diabetic patients, performance measures include HbA1c testing, HbA1c poor control, HbA1c control (<7%) and HbA1c control (<8%) depending on the program.
Initiative-specific nuances in measure implementation
Different quality initiatives select a set of measures based on industry best practices and their endorsement by nationally recognized organizations such as NQF or CMS. This results in common measures—say, tracking HbA1c poor control in diabetes—being tracked across different quality initiatives, such as MU or PQRS. And because each quality initiative has its own nuances in the actual specification and implementation of the measures, provider organizations need to track the same measure multiple times for different initiatives.
Additionally, organizations need to be cognizant of the fact that the eligible patient population for the same measure may also vary across quality initiatives based on health plan coverage (Medicare or commercial), allowable evaluation and management (E&M) services and so forth.
Periodically changing measure definitions
Even within a quality initiative such as PQRS, HIQRP or HEDIS, the specifications for clinical quality measures change over time, driven by factors such as the addition/retirement of E&M codes, medication codes, endorsement of new evidence and changes in exclusions/exceptions.
Consequently, organizations are forced to expend significant time and effort in periodically tracking and upgrading measure calculation logic as well as associated workflows, data sources and IT systems based on the extent of change. Moreover, changes in measure calculations make it very difficult (and potentially risky) for organizations to accurately trend and benchmark the performance of clinical measures over a period of time.
Availability/applicability of performance benchmarks
The use of industry benchmarks to monitor performance is uncommon; likewise, few governing guidelines for clinical quality reporting provide them. Some organizations publish national and/or regional statistics, but there are issues in using this data. For instance, the data may not be endorsed by any quality initiative organization or does not account for contextual/regional variations (health of population being served, resource availability, environmental and seasonal variations, etc.).
The lack of industry benchmarks makes it difficult for healthcare organizations to set and track realistic goals and get a better sense of their performance. This issue is becoming more acute in the context of newer payment models such as value-based payments and shared savings programs.
Tracking care of measure exclusions/exceptions
Most quality measures are based on clinical evidence at a population level. However, during the computation of these measures, data may be excluded for many patients due to medical reasons or other significant deviations (exclusions/exceptions). Where exclusions/exceptions are provided, alternate best practice guidelines for excluded populations are not always available or tracked. If a patient is excluded from an influenza immunization measure due to contraindication, for example, it is important to follow best practices for this population as well.
In summary, the more clinical quality measures shape healthcare delivery and performance, the more pressing the need for governing agencies and healthcare organizations to address these systemic challenges and pave the way to standardization. But for now and the foreseeable future, the road remains a bumpy one.