Within the healthcare ecosystem, providers must navigate a complex web of regulations, processes, quality improvement (QI) initiatives, and pay-for-performance incentive programs aimed at ensuring compliance with the highest standards in patient-centered care. The majority of these programs call for health systems to track and report specific metrics referred to as clinical quality measures (CQMs).
According to the Centers for Medicare & Medicaid Services (CMS), CQMs are a way of assessing the processes, experiences, and/or outcomes of patient care as well as observations or treatment that relate to one or more quality aims for that care. Those quality aims include, for example, making sure the care is effective, safe, efficient, patient-centered, equitable, and timely.
CMS is not just an interested party weighing in with opinions on CQM goals, though. They establish the nationwide criteria for these quality measures and require eligible providers to report their metrics in order to be reimbursed for Medicare-eligible services.
What’s more, CMS is far from alone in the role of gatekeeper for pay. Commercial payers, too, require performance against their own established CQMs as part of their respective incentive programs; providers forfeit reimbursement if they don’t report or achieve CQMs to payers.
Roadblocks in monitoring CQMs
Clearly, driving clinical quality measures is a fundamental objective for healthcare organizations. It may come as no surprise, then, that the reporting requirements for these external parties - which exist alongside required reporting for the health system’s own internal QI metrics that they want to track - can create a heavy burden for the teams responsible for them, which includes IT, patient safety, and quality staff.
The combination of increased workloads, staff shortages, and ever-changing regulations - amid the backdrop of the COVID-19 pandemic - means the vast majority of healthcare systems aren’t taking advantage of the data available to them through the clinical decision support (CDS) and electronic health record (EHR) systems they’ve invested both time and resources into. Even more, poorly implemented and ineffective monitoring of CDS can lead to potential safety gaps, inadequate quality improvement, and clinician dissatisfaction.
When it comes to tracking progress on CQMs, those on the front lines addressing patient safety and quality of care sometimes lose the forest for the trees. That is, IT staff and EHR analysts are focused on the technology itself and don’t always go a step further to understand how a process or system implementation affects patient outcomes.
Informatics’ role in driving clinical quality measures
Driving CQMs takes a collaborative and systematic approach. That’s where clinical informatics can help.
Clinical informatics teams bridge the gap between technical and nontechnical teams. They often are key consumers of EHR data and are adept at translating its uses to nontechnical stakeholders. Specifically, when these teams review insights derived from EHR alerts and order sets, stakeholders across the healthcare organization can get a better understanding of what’s working - and what’s not. This gap analysis of EHR data is what enables real improvement to take place.
The data and analysis available through clinical informatics tools can help QI project teams design and deliver more impactful alerts, while also offering actionable insights that help providers drive quality improvement and better track progress towards CQMs. Not to mention, informatics tools can take some of the burden off IT teams, who would otherwise spend time and resources developing their own automated tools or generating tedious reports that require stakeholders to sift through the data.
Health systems also can track ongoing performance against their own internal CQMs through a clinical informatics platform. The insights gained from monitoring and assessing CQM goals can often prompt healthcare organizations to initiate new QI projects aimed at driving clinical quality measures and improving efficiency.
For example, a pediatric hospital located in an inner city may be focused on lowering mortality rates in the neonatal intensive care unit (NICU), whereas a hospital catering mostly to older patients may direct their efforts toward reducing length of stay for diabetic neuropathy. Based on the data uncovered through an informatics platform, providers may decide to create a new interruptive alert, revise an order set, or make a change to their intake process.
Bridging the gap
The best informatics tools link both the technical and clinical aspects of healthcare operations, providing automated data and insights that enable ongoing, easy access to CQM-related data.
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