2025-03-16-兰德-使用系统动力学对医生劳动力供应_需求和需求进行建模-模型结构和假设（英）_39页_1mb

Modeling Physician Workforce Supply, Demand, and Need Using System Dynamics

This report summarizes the development and structure of a system dynamics model byRAND Corporation for the Association of American Medical Colleges (AAMC) to project U.S. physician and advanced practice provider (APP) workforce dynamics. The model employs system dynamics to capture causal feedback loops, delays, and nonlinear behaviors, offering tools for policy analysis and scenario simulation.

Purpose and Background

• Healthcare Workforce Challenges: Addresses ongoing shortages in the U.S. healthcare system due to factors like aging population, increased demand, and burnout, with implications for care quality and public health responses.
• Model Motivation: The AAMC commissioned the model to create more accurate workforce projections by including causal interactions and system dynamics, moving beyond traditional clinician-to-population ratios. It aims to map supply, demand, clinical need, and utilization to understand policy impacts comprehensively.
• Stakeholder Expectations: Models are now expected to differentiate between need, demand, utilization, account for delays in workforce training, and involve transparent, iterative stakeholder engagement.

Key Features of the RAND-AAMC Model

• Components: Includes modules for supply (physicians, APPs like nurse practitioners and physician assistants), demand (desired care by patients), clinical need (required for optimal health), perceived need (patient and clinician beliefs), and utilization (care provided).
• Structure and Tools: Utilizes causal loop diagrams (CLDs) to show feedback loops (e.g., reinforcing and balancing) and stock and flow diagrams (SFDs) to model accumulations and rates. Key features include endogenous elements (e.g., U.S. population, clinical need) and exogenous factors (e.g., birth rates, scope of practice limits).
• Timeframe: Simulates data from 2002 to 2052, allowing verification with historical trends and projections under various policies, including sensitivity to changes in training slots and workload.
• Methods: Based on expert stakeholder input, empirical data from sources like the AAMC and Medical Expenditure Panel Survey (MEPS), and standard system dynamics practices including validation and scenario testing.
• Assumptions and Limitations: - National-level aggregation without geographic sensitivity; factors like race/ethnicity of clinicians and financial elements (e.g., costs) are excluded or indirectly represented. The model focuses on physicians and APPs, excluding other provider groups.

Benefits and Applications

• Provides policymakers with tools to forecast future scenarios, evaluate policy impacts (e.g., expanding residency slots or task shifting to APPs), and identify leverage points for interventions.
• Calculates twelve imbalances (e.g., between supply and demand) to analyze shortages and surpluses across systems; outcomes include unit metrics (e.g., hours of care) or ratios (e.g., providers per 1,000 population).
• Facilitates real-time exploration through a user interface, enabling dynamic insights into interconnected system elements and unintended policy consequences.

Future Directions

• Plans include incorporating geographic mismatches, addressing scope of practice expansions, studying payment influences, and validating data for conditions like burnout and ACA impacts. Future versions aim to support state-level and specialty-specific applications.