Stocks, flows and time lag in systems: Implications for shared decision making research

System dynamics (SD) is an approach to understanding the nonlinear behaviour of complex systems over time using stocks, flows, internal feedback loops, table functions and time delays. [1]

One of the basic lessons of looking at a complex system through the lens of system dynamics is that it takes time, sometimes a surprisingly long time, to alter a component of a system that has been functioning in the same fashion for a while. In the language of system dynamics, this cumulative experience of one type of behavior is called a stock. Even with a steady inflow of new behaviors, unless there is a correspondingly large outflow of old ones, the overall behavior of the system will change only slowly.

A classic example is what happens in a bathtub that is full. Now start adding new water from a faucet and open the drain. The inflow is new water from the faucet, the outflow is the water going down the drain, and the water in the tub is the stock. Even if the faucet is running freely and the drain is open, it will take some time before all of the old water in the tub is replaced with new water.

Now imagine the tub represents the traditional way of clinical decision making, the inflow is adoption of routine shared decision making, and the outflow through the drain represents practitioners who abandon the old style to adopt the new one. From this perspective, it is easy to see that changing an established style of medical decision making could take a long time to achieve, particularly if the stock of established behaviors is slow to change, i.e., the drain is functioning slowly.

From this perspective, if you are considering conducting a clinical trial of a shared decision making intervention, or reading a report of one, you should ask yourself how long the expected change in decision making behavior is likely to take based on the estimated persistence of the existing “stock” of usual decision making practice. For the trial to be successful, it has to be open for at least this long. If not, a real but delayed effect will be missed. Similarly if you are reading the report of a clinical trial of a shared decision making intervention, a key question is, if the trial was negative, was it continued long enough to expect to see a change in the setting where it was conducted.

In the August 19, 2022 post, I discussed a negative trial reported by Isabelle Scholl and colleagues. [2] The authors concluded that their intervention failed, but the trial only lasted at most 30 months and probably less than that in many cases due to the delayed onset, stepped wedge design used for the trial. Is possible they could have seen an effect if they been able to conduct a longer trial?

I raise this possibility because I have not seen it mentioned in the literature. From this perspective, clinical trials of interventions to change clinical decision behavior, by necessity, have to take into effect the expected rate of change that is predicted by a large “stock” of customary behaviors. I am not sure if this consideration is high on the list of items commonly used in planning implementation studies. However, I think it should be.

Musings

I am in no way an expert in systems thinking or system dynamics. However, I think this description of the effects of stocks and flows on system changes is correct.

One could logically ask how could a clinical trial planner estimate the effect of a stock of customary decision making behavior? Fortunately there are a number of system dynamic software packages available ranging from simple to complex that could be used. A nice summary of the models available is on the System Dynamics Society webpage. If you are interested, here is a short YouTube video showing how these dynamic models work.

References

  1. https://en.wikipedia.org/wiki/System_dynamics

  2. Scholl, I., Hahlweg, P., Lindig, A. et al. Evaluation of a program for routine implementation of shared decision-making in cancer care: results of a stepped wedge cluster randomized trial. Implementation Sci 16, 106 (2021). https://doi.org/10.1186/s13012-021-01174-4.

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