Six Reasons to Apply System Dynamics Modeling in Medical Research

By Kenneth G. Cooper

(Moderator’s Note: After several recent conversations with Ken about different dimensions of SD in medical research, and as a follow-up to his recent post, I asked him to provide a short list of the top reasons why we should pursue more SD work in this area. The list below, shared with his permission, is his response. M.Nelson)

One. The prevalence of feedback-intensive systems in the body, and their complex interaction with systemic diseases and disorders. System dynamics modeling is distinctly well suited to study those feedback-intensive processes.

Two. The need for a systems view that integrates multiple areas of focused research, so as to understand better the system-wide consequences of diseases and interventions.

Three. The need to analyze the behavior of the systems of the body over long periods of time—perhaps decades of a chronic disorder—and to do so rapidly, as simulation provides.

Four. The value of being able to test different factors in ”what if” simulations, to examine how they affect the workings of the system—by how much, where, why, and over what time frame. These simulation tests could be used to identify the impact of different potential treatment options.

Five. Importantly, the need to test combinations of conditions and possible interventions, rather than just a “single magic bullet” approach. It seems likely that some systemic disorders may respond best to multiple points of intervention; those “combination cures” might never be found through conventional lab and clinical testing alone.

Six. The need to examine alternative hypotheses of how human systems and diseases work; there is much we simply don’t know about the workings of human systems. It would be valuable to be able to test in silico alternate hypotheses about how those systems work. (This also may lead to identifying important commonalities among “different” diseases.) Indeed, it seems likely that understanding human system dynamics better would help inform how to influence the body’s own control mechanisms and systems to help slow, halt, or cure diseases.

There is a potentially huge impact from combining the work of those who practice system dynamics modeling and those who are engaged in systemic disease research. Such a convergence could have immense societal and economic benefits.

4 thoughts on “Six Reasons to Apply System Dynamics Modeling in Medical Research

  1. Good stuff. I think there are more reasons if you broaden the definition of ‘medical’ too:

    7. Dose titration can be dynamically complex and subject to misperceptions of feedback; models make it easy.

    8. Chronic autoimmune and mental health problems are embedded in a nest of feedback between the disease and the person’s environment. https://metasd.com/2019/05/biological-dynamics-stress-outer-loops/

    9. ERs, hospitals and other delivery systems are loaded with delays, feedback and nonlinearity.

    10. Smoking, diet, exercise, and other big health drivers are social phenomena.

    11. Diet and exercise are entangled with other systems, like urban design and energy efficiency.

    12. The health insurance system, especially in the US (’nuff said).

    Liked by 1 person

  2. Good things to remember. Perhaps another point would be in determining gaps in our knowledge. This may be more prevalent in population health or public policy where much of what drives dynamics goes unmeasured, but I imagine this is the case in the under-the-skin medical world, too.

    Liked by 1 person

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

This site uses Akismet to reduce spam. Learn how your comment data is processed.