Using Anesthesia Time Units as a Productivity Metric 

In healthcare, clinical productivity is often measured to evaluate staffing needs, analyze costs, and design behavior modification or incentive systems.^1,2 In the United States, physician productivity is typically tracked using work relative value units (wRVUs), a metric established by the Centers for Medicare and Medicaid Services (CMS) to capture the “time and intensity” of clinical services for billing and benchmarking.² Within the US healthcare system, anesthesiology is unique, as it uses anesthesia time units as the basis of billing and insurance claims, whereas other services are typically considered in terms of the specific procedure or exam provided. This difference has generated efforts to use anesthesia time units as a metric for the productivity of anesthesia providers and groups. However, the metric is less representative of productivity than it may initially appear to be. 

When attempting to apply standard productivity frameworks to anesthesiology, healthcare leaders may default to using total anesthesia time units per full-time equivalent (FTE) to benchmark individual anesthesiologists. Yet, relying on billed anesthesia units as a direct measure of an individual clinician’s productivity is a flawed representation of actual clinical workload.²  

Anesthesia units differ fundamentally from the wRVUs used by other medical specialties. Anesthesia billing is constructed using a two-part formula: a combination of base units—which reflect the complexity of the surgical procedure—and variable time units, typically billed in 15-minute increments. For example, a 90-minute laparoscopic cholecystectomy with a base value of 7 units would generate 6 time units, resulting in 13 total ASA units. Unlike wRVUs, which cleanly segregate work, liability, and practice expense components, anesthesia units bundle these elements together. This makes it impossible to directly equate them to the standard productivity metrics used by surgeons or internal medicine physicians. Because time units remain constant regardless of the case, the total units generated per hour are heavily dictated by the duration of a procedure rather than the intensity of the clinical care provided.² As a result, using anesthesia units per FTE as the sole metric does not necessarily represent the full complexity and intensity of a case.  

Consider the contrasting workflows of two providers within the same system: Doctor A is assigned to a high-turnover ambulatory center with no night or weekend call and can medically direct multiple rooms simultaneously. By churning through short, simple procedures, they rapidly “stack” the fixed base units awarded at the start of every case. On paper, they appear to be the department’s top producer. Conversely, Doctor B specializes in high-acuity pediatric cardiac cases and takes trauma shifts. Because these critical cases require intense, 1:1 direct care with zero concurrency, Doctor B must rely on the slow accumulation of time units over lengthy surgeries that only yield a single set of base units. Therefore, arguably, relying strictly on total anesthesia units per full-time equivalent creates an imbalanced metric of productivity. It financially and operationally rewards favorable scheduling and high-volume throughput while penalizing high-acuity and direct care.² 

Additionally, recent research that used EHR data to estimate clinical workload suggests that billed anesthesia units overvalue procedural complexity while undervaluing patient complexity. In fact, according to an analysis by Lou et al., a patient’s health complexity (measured via the ASA physical status score) contributes over 50% more to actual EHR-derived clinical workload than it does to billing-derived workload.³ 

The rapid growth of Non-Operating Room Anesthesia (NORA) further challenges attempts to quantify productivity in anesthesia. NORA services—encompassing care in gastrointestinal endoscopy suites, interventional radiology, electrophysiology labs, and advanced imaging centers—are expected to represent up to 50% of all anesthetics within the next decade.^4,5 The relative newness of these anesthesia modalities and their unique patient demographics requires billing and coding, workflow, and administrative protocols to adapt. 

Finally, beyond the flaws of using anesthesia time units as a productivity metric, a broader question remains regarding the purpose of measuring productivity. In a field already grappling with burnout and a workforce crisis, efforts to increase productivity at the expense of clinician health and longevity are likely to be met with resistance. Consequently, it is important to continue centering conversations about administrative policies around what improves patient care. 

References 

1. Abouleish AE, Hudson ME, Levy RS, Whitten CW. Industry-Wide Survey of Academic Anesthesiology Departments Provides Up-to-Date Benchmarking Data on Surgical Anesthesia Productivity. Anesth Analg. 2020 Sep;131(3):885-892. doi: 10.1213/ANE.0000000000004934. PMID: 32541253. 

2. Abouleish AE, Whitten CW, Hudson ME. Measuring and Comparing Clinical Productivity of Individual Anesthesiologists. Anesthesiology. 2023 Nov 1;139(5):684-696. doi: 10.1097/ALN.0000000000004722. PMID: 37815474. 

3. Lou SS, Baratta LR, Lew D, Harford D, Avidan MS, Kannampallil T. Anesthesia Clinical Workload Estimated From Electronic Health Record Documentation vs Billed Relative Value Units. JAMA Netw Open. 2023 Aug 1;6(8):e2328514. doi: 10.1001/jamanetworkopen.2023.28514. PMID: 37566415; PMCID: PMC10422189. 

4. Mendelev E, Urman RD. Improving efficiency and workflows in the nonoperating room anesthesia setting. Curr Opin Anaesthesiol. 2026 Apr 27. doi: 10.1097/ACO.0000000000001663. Epub ahead of print. PMID: 42054149. 

5. Primm A, Anca D. Updates in Non-Operating Room Anesthesia. Curr Opin Anaesthesiol. 2025 Jun 1;38(3):297-302. doi: 10.1097/ACO.0000000000001472. Epub 2025 Mar 5. PMID: 40072000.