Hospital COVID-19 Performance Was Measured With 459 Different Indicators
Hospitals tracked their pandemic response with a strikingly broad mix of metrics, but the field never settled on a common language. In a scoping review of 94 studies spanning the early pandemic through 11 November 2024, investigators identified 459 distinct key performance indicators used to evaluate or optimize inpatient hospital care during COVID-19 across Europe and the UK.
The review was built to answer a practical question: which indicators were actually used when hospitals tried to measure performance under pressure, and how often did those indicators recur? The answer was fragmentation. Some KPIs focused on clinical outcomes, others on staffing, bed capacity, throughput, access time, or sojourn time. Many appeared in multiple studies, yet were defined differently enough to weaken direct comparison. Similar measures were also labeled with different terminology, creating an added layer of inconsistency for anyone trying to benchmark hospitals across countries or even across wards.
One sentence captures the core problem: hospitals were often measuring the same reality with different rulers.
Clinical Outcomes and Throughput Dominated the Metric Set
The indicators clustered into six inductively derived categories: clinical outcomes, resources and capacity, care delivery volumes, care delivery performance, access time and sojourn time, and a final catch-all group of other indicators. That structure reflects how hospitals experienced the pandemic in real time. Clinical leaders were watching mortality, complications, and other outcome measures, while administrators were trying to preserve capacity, reroute patient flow, and keep services moving despite staffing strain and surges in admissions.
The review found that KPIs were reported in many formats, including volumes, percentages, scores, scales, characteristics, and binary outcomes. This mattered because format is not just a stylistic choice. A percentage may describe occupancy, a binary outcome may capture whether a threshold was crossed, and a score may compress multiple dimensions into a single number. When those formats are mixed without shared definitions, the same operational question can produce apparently different answers depending on how the metric is written down.
A second sentence lands with the force of the data: almost none of the KPI ecosystem was designed exclusively for the pandemic.
Most indicators were not novel COVID-19 tools at all. They were routine hospital performance measures that had already existed before the pandemic and were simply repurposed under crisis conditions. That pattern is clinically revealing. It suggests hospitals leaned on familiar monitoring systems rather than building a new measurement architecture from scratch, even as the care environment changed dramatically. In practice, this likely made rapid adoption easier, but it also limited the specificity of the performance lens for a once-in-a-generation emergency.
Pre-COVID Comparisons Were the Main Use Case
The most common reason for using KPIs was to compare performance before and during COVID-19. That is understandable. The pandemic disrupted elective surgery, emergency flow, bed availability, diagnostic access, and staffing patterns, so the simplest way to measure effect was to contrast the crisis period with the pre-pandemic baseline. A substantial share of studies also used KPIs to support service optimization or to explore quality of care during the pandemic, suggesting that these indicators were not merely descriptive; they were used as operational tools.
Yet the review shows a persistent split between clinical and logistical measurement. Most studies emphasized either one or the other, with limited integration of both perspectives within the same performance assessment. That gap is more than academic. A hospital can appear efficient on throughput while failing on outcome quality, or it can deliver good clinical care while accumulating severe access delays and congestion. During a respiratory pandemic, those domains interact. Bed pressure can alter triage thresholds, prolonged waiting times can change disease severity at presentation, and staffing shortages can reshape both process and outcome measures.
The biological and operational logic is straightforward. When patient volume rises faster than capacity, the system’s delay chain lengthens: triage, imaging, treatment initiation, and discharge all slow down. In infectious disease surges, those delays do not stay neutral. They can magnify exposure risk, worsen decompensation before treatment, and force hospitals to redistribute resources in ways that affect other patients who never had COVID-19.
Definitions Varied Enough to Limit Direct Comparison
The strongest methodological message from the review is not simply that many KPIs existed. It is that identical indicators were often defined differently, while similar indicators were often grouped under different names. That variation makes it hard to know whether a difference between studies reflects true performance variation or a definitional mismatch. A metric called one thing in one hospital may not be operationally equivalent to the same label used elsewhere.
The review’s authors extracted titles, abstracts, and full texts independently with three reviewers, resolving disagreements through discussion and consultation with two additional reviewers. They also used a predefined extraction form and categorized KPIs inductively, which is appropriate for a literature base this heterogeneous. Even so, the underlying evidence remained too diverse for a clean, standardized summary. The problem was embedded in the literature itself: inconsistent KPI definitions, inconsistent reporting formats, and inconsistent emphasis across clinical and logistical domains.
When the evidence base fragments this much, reproducibility suffers first. Comparability comes next. Decision-makers are left with a large set of local metrics, but a weaker basis for shared benchmarking across hospitals, health systems, or national settings.
Standardization Will Matter in the Next Crisis
The review closes on a clear operational point: greater transparency and standardization in KPI definitions could improve comparability, reproducibility, and preparedness for future health crises. That recommendation follows directly from the data. A pandemic stresses hospitals at every level, but measurement systems need not become opaque at the same time. Shared definitions would make it easier to track whether one hospital truly outperformed another, whether a policy changed outcomes or merely changed documentation, and whether logistical improvements carried any clinical cost.
A more integrated framework would also help hospitals avoid measuring only one half of the system. Clinical outcomes without logistics can miss bottlenecks. Logistics without outcomes can miss harm. The review shows that both perspectives were used during COVID-19, but rarely in a unified way. Building a combined performance model would not eliminate crisis uncertainty, but it could give hospital leaders a more usable picture of what was happening on the wards, in the emergency department, and across inpatient flow.
The final takeaway is concrete: the pandemic produced a large KPI archive, but not a standardized one. Future emergency planning will be stronger if hospitals agree in advance on what to measure, how to define it, and how to report it when the system is under strain.