Quality has taken a front seat in transforming health care since the beginning of the 21st century. According to the National Academy of Medicine, 2%-4% of deaths in health care were deemed preventable due to medical errors (To Err Is Human: Building a Safer Health System. 2000). Given the evolution of quality improvement, national attention has led to pivotal advances in addressing this unmet need. Such advances, in turn, have led to increasing government-funded research in health care quality, focusing specifically on the importance of managing quality, with data metrics tied closely to value-based care models. This, among many factors, has led to increasing transparency on the public reporting of quality metrics to support patient autonomy, allowing individual choice of health care providers and institutions based on publicly available benchmarks (J Cardiothorac Vasc Anesth 2021;35:22-34).

To receive reimbursement through Medicare/Medicaid – the largest payers in health care – organizations must meet standardized quality metrics and receive certification by the Centers for Medicare & Medicaid Services (CMS) or CMS-certification authorities. Current changes in payment models, from fee-for-service to value-based payment, continue to support the shift toward increasing quality and safety (asamonitor.pub/4eITnwM).

The U.S. Department of Health and Human Services expanded the quality lens to include safety, effectiveness, patient-centeredness, timeliness, efficiency, and equity (asamonitor.pub/3S8wR6T). In addition, national quality organizations were established to provide quality metric benchmarks for different medical specialties. The Anesthesia Quality Institute (AQI) implemented the National Anesthesia Clinical Outcomes Registry (NACOR) as a source for quality benchmarking in anesthesiology (asamonitor.pub/3VZGXZR). They and other organizations have compiled quality benchmarks that anesthesiology departments can use to track quality and productivity metrics.

The Donabedian model separates quality metrics into three types: structure metrics, process metrics, and outcome metrics (asamonitor.pub/3VVGLuF; Milbank Q 2005;83:691-29). Structure metrics refer to the health system’s capacity to provide care. Structure metrics consider the system’s infrastructure, resources, policies, and tools that support health care delivery (asamonitor.pub/45ZAGkv). Process metrics assess practice alignment with published practice guidelines and maintenance of quality improvement efforts in the department. Examples of process metrics include timely antibiotic administration rates, the utilization of time-outs, and PONV prophylaxis protocols. Outcome metrics aim to measure the impact of care on the patient postoperatively. This may include rates of mortality or significant morbidity such as in unplanned intensive care admission or major organ failure.

Process metrics in anesthesiology are often difficult to track due to the field’s variable clinical practice models. Anesthesiology is a highly complex field requiring clinicians to make rapid process decisions and interventions, further complicating the accuracy and reliability of data collection and data standardization. This fact raises the bar for standardization and increases the challenges. Process metrics are also difficult to track across institutions systematically and are more often dependent upon the medical system than an individual practitioner’s efforts. Finally, the impact on patient outcomes of process metrics is not fully understood with robust research studies, muddling the progress on developing indicators for higher-quality care (BMC Anesthesiol 2023;23:256).

Limitations for outcome metrics also persist. It can be difficult to differentiate or disentangle the actions of the anesthesiologists from those of the surgeon due to our simultaneous and parallel interactions with patients intraoperatively. Also, there is a relative paucity of outcomes data research in anesthesiology, leading to fewer published outcome metrics and more unknowns on how best to advance metrics in a standardized method. Additionally, metrics focused on patient outcomes may result from their baseline health status, disease process, or postoperative interventions that are often not managed directly by the anesthesiologist.

Several studies have demonstrated that accurately tracking, collecting, and analyzing quality efforts improves adherence to quality improvement efforts and trends toward improved morbidity (Semin Cardiothorac Vasc Anesth 2022;26:173-8; PLoS One 2021;16:e0247297; Med Care 2011;49:1118-25). However, research studies have failed to consistently demonstrate reproducible improvements in perioperative mortality using the current model of quality metrics and benchmarks (PLoS One 2021;16:e0247297; Med Care 2011;49:1118-25). Notably, there is an increasing trend of public metrics reporting on the observed patient populations for both hospital systems and providers, which has unclear impacts on the quality of care (Curr Opin Anaesthesiol 2023;36:208-15).

Historically, anesthesiology reimbursement has been tied to billing using the Current Procedural Terminology (CPT) coding system, along with ASA units. These values consider the procedural complexity, patient complexity, and the amount of time spent in the care of the anesthesiologist. As the focus moves to value-based models that link pay-for-performance to quality indicators, anesthesiologists are tasked with demonstrating the quality of care provided to ensure competitive reimbursement in the future. As this transition unfurls, compensation models are changing, with anesthesia groups offering bonuses to the entire anesthesiology department or individuals when certain quality metrics are met. These targets generally align with those tracked by CMS.

Surgical Care Improvement Project (SCIP) guidelines are evidence-based process measures that aim to reduce preventable perioperative morbidity (Anesthesiology 2015;123:116-25). These measures, along with those outlined by other quality-focused entities like the National Quality Forum (NQF), may, for example, provide departments with benchmarks against which to compare anesthesia groups. Importantly, many of the benchmarks put forward by quality organizations are process measures, including antibiotic timing, temperature management, prophylaxis medication documentation (e.g., antiemetics), and utilization of multimodal pain management techniques. Other benchmarks scrutinized for bonus payment include patient satisfaction, peer evaluations, and participation in quality-improvement efforts.

In a study by Hutson and colleagues, eight institutions were incentivized to adhere to medication administration metrics through enhanced pay opportunities (Baylor University Medical Center Proceedings 2019;32:5-8). The study noted that over two years there was a statistically significant improvement in antiemetic prophylaxis medication administration in the 50,000 patients treated and a reduced need for rescue agents in the postoperative period. Although this study demonstrated positive associations with linking pay to quality metrics, more research is needed on what quality metrics to include and how these metrics affect patient outcomes. Many clinical components have been criticized for not accurately identifying high-quality anesthetic care and not significantly affecting patient outcomes (Curr Opin Anaesthesiol 2023;36:208-15; Int Anesthesiol Clin 2021;59:37-46).

“Notably, there is an increasing trend of public metrics reporting on the observed patient populations for both hospital systems and providers, which has unclear impacts on the quality of care.”

As anesthesia compensation transitions to value- and team-based models – with an increased focus on demonstrating the quality, performance, and productivity of anesthesiology groups – future research will need to elucidate effective quality indicators. These must be measured promptly, include patient-specific feedback regarding anesthesiology care, and seek clinician-focused feedback for practice improvement. Data gathering methods must incorporate new technologies – such as artificial intelligence, machine learning analysis, and automated data capture aligned with tracking metrics – alongside reliable, objective, and accurate anesthesiology-specific data. Future research should explore specific anesthesiology factors that significantly impact patient outcomes and the overall cost of care in the perioperative experience. In the future, these metrics should be used to demonstrate the value of the performance of individual practitioners and anesthesiology groups.