The strongest practices use both together, forecast the baseline, project growth initiatives, and adjust quickly when reality diverges.
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A 2024 Deloitte survey found that 65% of private practices using accurate forecasting to ground their financial planning improved resource allocation accuracy by 20%. As Alan Kay said: the best way to predict the future is to invent it. But to invent it responsibly, you need to understand the difference between data-driven prediction and assumption-based scenario planning.
Forecasting draws from what has actually happened. It uses your practice's own historical data, appointment volumes, revenue patterns, seasonal trends, staff productivity rates; to estimate where you're likely to land if current conditions continue. Brandon's example: if your practice historically sees a 10% appointment drop every January, you can forecast lower January revenue, plan your supply orders accordingly, and encourage staff to use PTO during that window. Forecasting is your primary tool for setting accurate budgets and managing cash flow with confidence.
Projections enter the picture when you're considering something genuinely new, a second location, a new service line, dropping an insurance contract, adding telehealth. You don't have historical data for these scenarios, so you build assumptions from market research, competitor analysis, and demographic data. Brandon's framework: always project both the best case and worst case. Understanding the full range of potential outcomes is what separates confident strategic decision-making from reckless optimism. His 2025 caution: be especially careful with pure private-pay projections in the current economic environment, for practices with multiple full-time staff, insurance-based revenue provides a stability floor that private pay cannot guarantee.
Brandon's practical integration: use forecasting to set your baseline budget, then layer projections to plan growth initiatives. Regularly compare actual performance to both models. When reality diverges significantly from either, that's a signal, either your assumptions were wrong or conditions have shifted. Either way, update your models and communicate the variance to your stakeholders before it becomes a crisis.
