Abstract and Introduction
Abstract
Anesthesia services in the United States are undergoing rapid transformation, pointing to substantial changes in anesthesia delivery and need for anesthesia services. Understanding the demand and supply of anesthesia services is necessary to predict and plan for adequate patient care services. In an anesthesia workforce model, a current 10.7% excess demand is estimated. The excess demand will decline to 3.6% by 2027. Swiftest reductions in excess demand are achieved by increasing new entrants and shifting delivery models toward providers acting independently.
Introduction
Anesthesia services in the United States are undergoing rapid transformation due to an aging population, greater practice consolidations, decisions to support value-based care (Provident Healthcare Partners, n.d.), and trends toward more anesthesia services in outpatient settings (Osman & Shapiro, 2019). These factors point to substantial changes in anesthesia delivery and the need for anesthesia services. Future demand and supply of anesthesia services are assessed in this analysis.
Healthcare workforce projections inform workforce development and training programs, promote care in underserved areas, and guide regulatory policy related to scope of practice (SOP) and reimbursement (Wing et al., 2016). Current and projected future market for anesthesia services were analyzed. Relationships between demand and supply and their determinants, such as demographic mix, interactions between anesthesia providers under the various delivery models, and other specific aspects related to anesthesia delivery, were measured. Population needs are at the center of the demand side of workforce models and projections. An economic approach to demand is used, in which projected demand is based on both patient needs and the ability to finance them through public and private insurance, and other sources of financing. A workforce model was constructed based on these estimated relationships, using a method that allows for possible shortages or surpluses in the baseline year. This model was used to forecast the state of the anesthesia market in the future under a baseline scenario and several hypothetical scenarios.
