Health metrics such as CMS Star Ratings, HRRP, Leapfrog, and VBP are not standalone metrics but rather, aggregations of underlying patient-level events. These events include readmissions, mortalities, infections, safety incidents, and satisfaction scores. For predictive strategies to be truly effective, hospital quality teams need to delve deeper than these overarching programs—they must predict the very events that construct these scores. Indeed, predictions at an event level serve as the operational fulcrum connecting hospital-wide objectives to actionable, frontline clinical processes.
Hospitals can thus optimize resource allocation, encourage engagement with clinical teams using more relatable metrics, and ultimately drive improvements in both patient outcomes and program performance. Connecting program-level performance metrics to patient-level actions forms a critical aspect of this predictive strategy. Healthcare executives are held accountable for metrics reflecting overall quality such as CMS Star Ratings, HRRP penalties, Leapfrog grades, and VBP payouts. These scores hinge heavily on distinct patient events.
Thus, highlighting patients with a potential to influence these outcomes empowers frontline teams with actionable insights. For instance, flagging a patient likely to be readmitted allows a quality team to strategically align bedside care with larger system objectives, linking both patient and organization outcomes with influential metrics like HRRP penalties and Star Ratings. Event-level predictions also play a crucial role in judiciously directing scarce resources where they have the most impact.
Given the limited bandwidth, case managers, infection preventionists, rounding teams, and quality staff need to prioritize patients and groups with the potential to drive negative quality outcomes or financial penalties. Rather than uniformly intervening across all discharges or infections, hospitals can prioritize efforts, using data-driven approaches to ensure precision resource allocation.
Understanding how to use event-level predictions to actively preempt financial risks is another key benefit. Up to 6% of Medicare revenue and 1.5% of inpatient revenue are hinged on quality metrics. Not being able to predict individual patient risks can lead to avoidable penalties impacting the bottom line in programs like HRRP, HACRP, and VBP. Thus, proactive predictions give hospitals the edge to preempt and mitigate potential financial risks by concentrating resources where they’re most effective.
Lastly, event-level predictions help engender clinical teams’ engagement with more real-world, actionable insights. Providers, for instance, can directly apply insights from a current patient’s elevated risk for sepsis, mortality, or dissatisfaction. This tangible tie between quality and patient care also creates an inter-departmental understanding that quality goes beyond just compliance and forms an intrinsic part of clinical decisions and team workflows. Event-level predictions span domains like readmissions, mortalities, patient safety indicators (PSIs), healthcare-associated infections (HAIs), patient experiences, CMS TEAM Episodes, bundled payments, and maternity outcomes.
Each domain represents a type of risk that, if predicted early enough, can drive significant improvements in hospital quality performance. Event-level prediction is more than a tool—it’s a foundational element for a predictive quality infrastructure. By embedding predictions into everyday workflows, quality teams transition from being mere data stewards to enablers of action, fostering prospective interventions rather than retrospective corrections, and effectively tying patient-level care to program-level performance.
Source: https://dexur.com/a/why-event-level-predictions-hospital-quality/1725/