Algorithmic prediction of anaesthesia manpower quantity needs: A multicentre study

Area of Science:

• Health Services Research
• Anesthesiology
• Medical Workforce Planning

Background:

• Global shortage of anaesthetists impacts healthcare systems worldwide.
• Accurate forecasting of anaesthetist numbers is crucial for maintaining adequate health services.
• This study addresses the need for a predictive tool to manage anaesthesia provider allocation.

Purpose of the Study:

• To develop a predictive model for determining the appropriate number of anaesthetic providers.
• To create an algorithm for calculating anaesthesia manpower needs based on service demands.
• To provide guidance for health systems in optimizing anaesthetist allocation.

Main Methods:

• A cross-sectional study utilizing data from health service regions across Thailand.
• Development of decision-making criteria for manpower assessment.
• Calculation of personnel numbers based on total time spent by providers and service duration.
• Application of linear regression analysis to build the predictive model.

Main Results:

• A predictive model (Y) was formulated incorporating factors like hospital type, patient physical status, and anaesthetic care type.
• The model includes variables such as advanced anaesthetic medication, monitored anesthesia care, and general anesthesia.
• It accounts for pre-anaesthetic, anaesthetic, and post-anaesthetic durations to predict provider needs.
• The model equation is Y = 3.53 + [0.56 (standard centre) + 0.36 (advanced centre) + 1.03 (specialty centre)] + 0.07 (ASA IV and V) + 0.61 (advanced anaesthetic medication) + [0.61 (monitored anaesthesia care) + 0.17 (general anaesthesia)] - [0.27 (pre-anaesthetic duration within 31-60 min) + (0.61 (over 60 min)] - [0.85 (anaesthetic duration within 31-60 min) + 1.04 (within 61-120 min) + 1.32 (over 120 min)] - [0.16 (post-anaesthetic duration within 31-60 min) + 0.45 (within 61-90 min) + 0.74 (over 90 min)].

Conclusions:

• The developed anaesthesia manpower algorithm provides a method for calculating necessary anaesthetists per population.
• This tool can assist health systems in maintaining adequate anaesthesia services.
• The model aids in addressing the global shortage of anaesthetists through better workforce planning.