The nature of the Ambulatory Surgery Center (ASC) is to provide efficient, high-quality surgical care while promoting financial sustainability.
The current state of healthcare economics provides little stability from which to maintain this balance, with increasing subsidies for anesthesia services serving as a dominant disruptor. Thoughtful and creative manipulation of anesthesia staffing models can provide relief from growing costs.
Market Disruption
The present shortage of anesthesia providers is only predicted to worsen in the foreseeable future. The American Association of Medical Colleges has approximated a shortage of 12,500 physician anesthesiologists by 2033, with 56% of current practitioners over the age of 55. The current and projected numbers of Certified Registered Nurse Anesthetists (CRNAs) also fall well short of forecasted increases in service opportunities. The shortfall in supply of anesthesia providers in the face of increasing demands for service have predictably created significant inflation in MD and CRNA salaries.
As provider salaries have soared, reimbursement for anesthesia services has progressively declined. CMS has cut payments to providers over 8% from 2019 to 2024, and private insurers followed suit, with many companies even eliminating longstanding payments for modifiers regarding physical status of patients and emergency procedures.
The combination of higher salaries and diminishing reimbursement has placed financial burdens on management companies and direct employment models, creating the request for subsidies to hospitals and ASCs to cover the fiscal gap of providing anesthesia coverage.
Efficiency Modeling
Solutions to combat increasing anesthesia subsidies should begin with the promotion of efficiency in anesthesia staffing and provider models. As salaries are the principal driver of anesthesia cost, removal of any practitioner redundancy is a necessary step towards efficiency.
Anesthesia models utilizing superfluous layers of providers in supervisory roles are many times an unfortunate carryover to the ASC setting from the slow-paced, inefficient operating rooms of large hospitals and academic institutions. Not only does the medical direction criteria and staffing ratios, necessary to avoid billing fraud in this model, create a perfect storm for case delays and scheduling conflicts, but they also drive up the cost of delivery, particularly in this time of salary inflation and declining reimbursement. The price
tag attached to the redundant, “supervising” provider is then essentially passed along to the ASC in the form of an anesthesia subsidy, with no evidence of increased patient safety or decreased liability.
In a truly efficient anesthesia delivery model, all providers should participate in revenue generating roles. The workload of physician anesthesiologists and CRNAs should be analyzed to ensure that the part each play in the activities of the daily surgery schedule contribute to operative throughput and minimize delays. To ensure maximum productivity, ASCs should create or confirm space in facility bylaws and regulations to assure that CRNAs are credentialed to perform in all phases of anesthesia care (nerve blocks, preoperative assessment, PACU management, etc.). This produces a “next person up” paradigm among the anesthesia staff and should allow the ASC to generate a schedule of operational efficiency without regard to anesthesia staffing ratios or individual provider abilities.
Conclusion
Significant economic disruptions within the anesthesia staffing and delivery market have led to an increasing burden on ASCs to subsidize the necessary costs to provide service. Facilities can begin to offset these costs by employing a model of anesthesia delivery that maximizes both financial and operational efficiency. Elimination of provider redundancy and full utilization of provider skillsets are essential steps in this process. Shared values of efficiency between ASCs and anesthesia providers can work to maintain an environment of high-quality surgical care and promote financial sustainability.