This is a calculation model, not a definitive answer. The purpose is to make a cost that otherwise just feels vaguely present concrete enough to talk about in the leadership team. The model is built on your own, verifiable numbers, not on assumed effects that can’t be substantiated.
Start with what can actually be observed. Which step in the method is sometimes skipped or done too late. Roughly how many cases are affected per year. How much extra time is needed when the step still has to be done afterward, or when a case needs to be extended as a result. This is fact you can pull from your own organization, not guesswork.
Then clearly separate three types of evidence:
- Observed facts – for example, how many cases per year where a certain step was skipped, and how much extra time it took to fix afterward.
- Local assumptions – reasonable estimates your organization makes itself, for example that a skipped step costs a certain amount of extra handling time on average.
- Consequences that can’t be priced with confidence – for example, any impact on the participant’s outcome. These should be described in words, not forced into a sum they can’t bear.
The basic formula:
Annual visible cost = number of deviations × extra time per deviation × internal hourly cost
Add other verifiable extra costs separately, for example extended placement time or a new placement that needs to be restarted. Avoid mixing effects on the participant’s outcome into the sum itself unless you have evidence that supports that link. Describe that kind of consequence in words alongside the calculation instead.
Calculate three scenarios, all filled in with your own numbers:
- Cautious – the lowest reasonable number of deviations and the lowest reasonable time cost.
- Likely – your best estimate based on what you actually see day to day.
- High – if the deviation is more common or more costly per occurrence than you think.
Three scenarios make the calculation more useful than a single exact figure, because it shows a reasonable range instead of a false sense of precision.
It’s important to be clear, both to yourself and to others in the leadership team, about what the calculation actually shows. It’s a decision aid and a sensitivity analysis that helps you prioritize. It isn’t proof that a particular deviation caused a particular outcome for a specific participant. The link between way of working and result may well be there, but this type of calculation measures the visible internal cost, not the causal link itself.
Many organizations sense there’s a cost, but have never put numbers on it, often because it feels too uncertain to be worth calculating. A calculation built on your own, verifiable numbers, and clearly split into facts, assumptions, and uncertain consequences, is more useful than no calculation at all, even if it’s never exact.
The same model can be used for more steps in the method, not just one at a time. Every step that’s sometimes skipped has its own cost, and those costs can be added together over a year, as long as each part rests on the same clear separation between observation, assumption, and uncertain consequence.
Redo the calculation with your own data. Which step in your method is most often skipped or done too late. Roughly how many cases are affected per year. What does it cost, in time and verifiable extra costs, when the step is missed. Calculate all three scenarios. The totals you land on, together with what you’ve chosen to describe in words rather than numbers, say something about how urgent it is to strengthen method fidelity, long before the next quarterly report is due.