As the Chief Digital Office for an integration health care system I thought it would be fun to model this real-word use case.
Amplified quantity: the number of clinicians using AI generated clinical documentation. This illustrative system-dynamics model shows how adoption can generate resources and feedback that stimulate further adoption.
R1 — Investment reinforcement: More adopters → more subscription revenue → more investment → greater reliability and workflow usefulness → faster new adoption → more adopters. Investment improves usefulness with a six-month smoothing delay.
R2 — Learning reinforcement: More adopters → more clinician feedback → greater usefulness → faster new adoption → more adopters. Feedback strength represents how effectively investment and feedback improve the product.
B1 — Market saturation: More adopters → fewer remaining potential adopters → slower new adoption. The potential population is fixed at 1,000 clinicians.
Notes on how model works:
The adoption chart and Simulation values table cover 60 months. Use the Feedback strength to compare 1 (default) with 0 (reinforcement disabled). Increase Errors or privacy incidents or Cost and budget constraints from 0 to 1 to interrupt adoption. Reset to 0, 0, and 1 for the default scenario.
ILLUSTRATIVE ASSUMPTIONS
Initial adoption: 50 clinicians; potential market: 1,000; subscription revenue: $100 per adopter per month; reinvestment: 30%; baseline usefulness: 0.10; adoption coefficient: 0.12 per month; investment delay: 6 months. Usefulness is bounded from 0 to 1. New adoption equals 0.12 × usefulness × remaining potential adopters × (1 − budget constraints). The inflow accumulates new adopters in the stock.
LEGEND
+ means an increase in the cause increases the effect, all else equal; − means the opposite. R denotes reinforcing feedback and B denotes balancing feedback.
