Closing the Chasm: Understanding and Addressing the Anesthesia Workforce Supply and Demand Imbalance.

The imbalance in anesthesia workforce supply and demand has been exacerbated post-COVID due to a surge in demand for anesthesia care, especially in non-operating room anesthetizing sites, at a faster rate than the increase in anesthesia clinicians. The consequences of this imbalance or labor shortage compromise healthcare facilities, adversely affect the cost of care, worsen anesthesia workforce burnout, disrupt procedural and surgical schedules, and threaten academic missions and the ability to educate future anesthesiologists. In developing possible solutions, one must examine emerging trends that are affecting the anesthesia workforce, new technologies that will transform anesthesia care and the workforce, and financial considerations, including governmental payment policies. Possible practice solutions to this imbalance will require both short- and long-term multifactorial approaches that include increasing training positions and retention policies, improving capacity through innovations, leveraging technology, and addressing financial constraints.

Measuring and Comparing Clinical Productivity of Individual Anesthesiologists.

Measuring and comparing clinical productivity of individual anesthesiologists is confounded by anesthesiologist-independent factors, including facility-specific factors (case duration, anesthetizing site utilization, type of surgical procedure, and non-operating room locations), staffing ratio, number of calls, and percentage of clinical time providing anesthesia. Further, because anesthesia care is billed with different units than relative value units, comparing work with other types of clinical care is difficult. Finally, anesthesia staffing needs are not based on productivity measurements but primarily the number and hours of operation of anesthetizing sites. The intent of this review is to help anesthesiologists, anesthesiology leaders, and facility leaders understand the limitations of anesthesia unit productivity as a comparative metric of work, how this metric often devalues actual work, and the impact of organizational differences, staffing models and coverage requirements, and effectiveness of surgical case load management on both individual and group productivity.

Improving agreement of ASA physical status class between pre-anesthesia screening and day of surgery by adding institutional-specific and ASA-approved examples: a quality improvement project.

BACKGROUND: A successful anesthesia pre-assessment clinic needs to identify patients who need further testing, evaluation, and optimization prior to the day of surgery to avoid delays and cancelations. Although the ASA Physical Status Classification system (ASA PS) has been used widely for over 50 years, it has poor interrater agreement when only using the definitions. In 2014, ASA-approved examples for each ASA physical status class (ASA PS). In this quality improvement study, we developed and evaluated the effectiveness of institutional-specific examples on interrater reliability between anesthesia pre-anesthesia clinic (APAC) and the day of surgery evaluation (DOS). METHODS: A multi-step, multi-year quality improvement project was performed. Step 1, pre-intervention, was a retrospective review to determine the percentage agreement of ASA PS assignment between APAC and DOS for adult and pediatric patients. Step 2 was a retrospective review of the step 1 cases where the ASA PS assignment differed to determine which medical conditions were valued differently and then develop institutional-specific examples for medical conditions not addressed by ASA-approved examples. Step 3 was to educate clinicians about the newly implemented examples and how they should be used as a guide. Step 4, post-intervention, was a retrospective review to determine if the examples improved agreement between APAC and DOS ASA PS assignments. Weighted Kappa coefficient was used to measure of interrater agreement excluding chance agreement. RESULTS: Having only ASA PS definitions available, APAC and DOS agreement was only 74% for adults (n = 737) and 63% for pediatric patients (n = 216). For adults, 20 medical co-morbidity categories and, for pediatric patients, 9 medical co-morbidity categories accounted for > 90% the differences in ASA PS. After development and implementation of institutional-specific examples with ASA-approved examples, the percentage agreement increased for adult patients (n = 795) to 91% and for pediatric patients (n = 239) to 84%. Weighted Kappa coefficients increased significantly for all patients (from 0.62 to 0.85, p < .0001), adult patients (from 0.62 to 0.86, p < .0001), and pediatric patients (from 0.48 to 0.78, p < .0001). CONCLUSIONS: ASA-approved examples do not address all medical conditions that account for differences in the assignment of ASA PS between pre-anesthesia screening and day of anesthesia evaluation at our institution. The process of developing institutional-specific examples addressed the medical conditions that caused differences in assignment at one institution. The implementation of ASA PS examples improved consistency of assignment, and therefore communication of medical conditions of patients presenting for anesthesia care.

Industry-Wide Survey of Academic Anesthesiology Departments Provides Up-to-Date Benchmarking Data on Surgical Anesthesia Productivity.

BACKGROUND: Benchmarking group surgical anesthesia productivity continues to be an important but challenging goal for anesthesiology groups. Benchmarking is important because it provides objective data to evaluate staffing needs and costs, identify potential operating room management decisions that could reduce costs or improve efficiency, and support ongoing negotiations and discussions with health system leadership. Unfortunately, good and meaningful benchmarking data are not readily available. Therefore, a survey of academic anesthesiology departments was done to provide current benchmarking data. METHODS: A survey of members of the Society of Academic Associations of Anesthesiology and Perioperative Medicine (SAAAPM) was performed. The survey collected data by facility and included type of facility, number and type of staff and anesthetizing sites each weekday, and the billed American Society of Anesthesiologists (ASA) units and number of cases over 12 months. The facility types included academic medical center (AMC), community hospital (Community), children's hospital (Children), and ambulatory surgical center (ASC). All anesthesia care billed using ASA units were included, except for obstetric anesthesia. Any care not billed or billed using relative value units (RVUs) were excluded. Percentage of nonoperating room anesthetizing sites, staffing ratio, and surgical anesthesia productivity measurements "per case" and "per site" were calculated. RESULTS: Of the 135 society members, 63 submitted complete surveys for 140 facilities (69 AMC, 26 Community, 7 Children, and 38 ASC). In the survey, overall median productivity for AMC and Children was similar (12,592 and 12,364 total ASA units per anesthetizing site), while the ASC had the lowest median overall productivity (8911 total ASA units per anesthetizing site). By size of facility, in the survey, the smaller facilities (<10 sites, ASC or non-ASC) had lower median overall productivity as compared to larger facilities. For AMC and Children, >20% of anesthetizing sites were nonoperating room anesthetizing sites. Anesthesiology residents worked primarily in AMC and Children. In ASC and Community, residents worked only in 18% and 35% of facilities, respectively. More than half the AMCs reported at least 1 break certified nurse anesthetist (CRNA) each day. CONCLUSIONS: To make data-driven decisions on clinical productivity, anesthesiology leaders need to be able to make meaningful comparisons at the facility level. For a group that provides care in multiple facilities, one can make internal comparisons among facilities and follow measurements over time. It is valuable for leaders to also be compare their facilities with industry-wide measurements, in other words, benchmark their facilities. These results provide benchmarking data for academic anesthesiology departments.

Measuring Clinical Productivity of Anesthesiology Groups: Surgical Anesthesia at the Facility Level.

Benchmarking and comparing group productivity is an essential activity of data-driven management. For clinical anesthesiology, accomplishing this task is a daunting effort if meaningful conclusions are to be made. For anesthesiology groups, productivity must be done at the facility level in order to reduce some of the confounding factors. When industry or external comparisons are done, then the use of total ASA units per anesthetizing sites allows for overall productivity comparisons. Additional productivity components (total ASA units/h, h/case, h/operating room/d) allow for leaders to develop productivity dashboards. With the emergence of large groups that provide care in multiple facilities, these large groups can choose to invest more effort in collecting data and comparing facility productivity internally with group-defined measurements including total ASA units per full time equivalent.

Do Pain Medicine Fellowship Programs Provide Education in Practice Management? A Survey of Pain Medicine Fellowship Programs.

BACKGROUND: We hypothesized that there is a gap between expectations and actual training in practice management for pain medicine fellows. Our impression is that many fellowships rely on residency training to provide exposure to business education. Unfortunately, pain management and anesthesiology business education are very different, as the practice settings are largely office- versus hospital-based, respectively. OBJECTIVE: Because it is unclear whether pain management fellowships are providing practice management education and, if they do, whether the topics covered match the expectations of their fellows, we surveyed pain medicine program directors and fellows regarding their expectations and training in business management. STUDY DESIGN: A survey. SETTING: Academic pain medicine fellowship programs. METHODS: After an exemption was obtained from the University of Texas Medical Branch Institutional Review Board (#13-030), an email survey was sent to members of the Association of Pain Program Directors to be forwarded to their fellows. Directors were contacted 3 times to maximize the response rate. The anonymous survey for fellows contained 21 questions (questions are shown in the results). RESULTS: Fifty-nine of 84 program directors responded and forwarded the survey to their fellows. Sixty fellows responded, with 56 answering the survey questions. LIMITATIONS: The responder rate is a limitation, although similar rates have been reported in similar studies. CONCLUSIONS: The majority of pain medicine fellows receive some practice management training, mainly on billing documentation and preauthorization processes, while most do not receive business education (e.g., human resources, contracts, accounting/financial reports). More than 70% of fellows reported that they receive more business education from industry than from their fellowships, a result that may raise concerns about the independence of our future physicians from the industry. Our findings support the need for enhanced and structured business education during pain fellowship. KEY WORDS: Business education, practice management, fellowship training, curriculum development, knowledge gaps, private practice.

Adding Examples to the ASA-Physical Status Classification Improves Correct Assignment to Patients.

BACKGROUND: Despite its widespread use, the American Society of Anesthesiologists (ASA)-Physical Status Classification System has been shown to result in inconsistent assignments among anesthesiologists. The ASA-Physical Status Classification System is also used by nonanesthesia-trained clinicians and others. In 2014, the ASA developed and approved examples to assist clinicians in determining the correct ASA-Physical Status Classification System assignment. The effect of these examples by anesthesia-trained and nonanesthesia-trained clinicians on appropriate ASA-Physical Status Classification System assignment in hypothetical cases was examined. METHODS: Anesthesia-trained and nonanesthesia-trained clinicians were recruited via email to participate in a web-based questionnaire study. The questionnaire consisted of 10 hypothetical cases, for which respondents were first asked to assign ASA-Physical Status using only the ASA-Physical Status Classification System definitions and a second time using the newly ASA-approved examples. RESULTS: With ASA-approved examples, both anesthesia-trained and nonanesthesia-trained clinicians improved in mean number of correct answers (out of possible 10) compared to ASA-Physical Status Classification System definitions alone (P < 0.001 for all). However, with examples, nonanesthesia-trained clinicians improved more compared to anesthesia-trained clinicians. With definitions only, anesthesia-trained clinicians (5.8 ± 1.6) scored higher than nonanesthesia-trained clinicians (5.4 ± 1.7; P = 0.041). With examples, anesthesia-trained (7.7 ± 1.8) and nonanesthesia-trained (8.0 ± 1.7) groups were not significantly different (P = 0.100). CONCLUSIONS: The addition of examples to the definitions of the ASA-Physical Status Classification System increases the correct assignment of patients by anesthesia-trained and nonanesthesia-trained clinicians.