The roots of modern research trace back to the late 17th century with the exploration of the innate (physical) world.

Newton’s research establishing the laws of the innate physical world based on experiments and repeated measurement in the controlled setting of the laboratory. This approach is based on a number of assumptions with limitations in real world applications – firstly, to experiment in the laboratory setting removes all external context that otherwise would impact the experiment (the law of the free fall of an object holds true only in a vacuum); secondly, that one can exactly measure observations (though Gauss showed that repeated measurements always have an error that symmetrically distributes around the mean); and lastly, that repeating the same experiment at a later time in a different setting will result in exactly the same outcome.

About a century later, Goethe and Humboldt demonstrated that Newton’s laws of the innate world of physics did not apply to the animate world of living beings. To understand and predict their behavior required the simultaneous understanding of their environmental context (1). Furthermore, Pareto observed another important phaenomenon of the animate world, namely that it has a consistent distribution pattern that follows an 80/20 split – now known as the Pareto or inverse power law distribution (2). These observations marked the recognition of the interconnectedness and interdependence inherent in biological systems.

Humboldt is regarded as the founder of systems sciences – the sciences of interconnectedness and interdependence within mechanical and biological systems. In general terms, such systems consist of at least two parts where

Biological systems have the added characteristic of being adaptive, i.e., the behavior of one part can change the behavior of all other parts. Over time such changes lead to emergent – marginally stable – system states [homeokinetics (4)] which, in the medical context, we associate with particular diseases and disease severities (Figure 1 – top).

There is a basic difference between physical and biological/social research questions. Physics is concerned with explaining cause-and-effect relationships in the innate world whereas biological/social sciences focus on understanding the emergent structural and behavioral phenomena in nature. While physics rightly focuses on researching mechanisms through a reductionist research paradigm, biological/social sciences should adopt an eco-systemic approach to understand the ways living beings ‘behave’ and constantly adapt at all scales of organization within their changing environments. Biological/social sciences should not only concern themselves with the structure and dynamics of ‘biological/social systems’, but more importantly with finding meaning or making sense of those eco-systemic interactions (5, 6).

Medicine is not a science, it is a praxis (7). Clinicians use those scientific results that as good as possible apply to the individual. Given the endless biological/social variability between individuals and their highly variable living environments, they can never deliver perfectly predictable outcomes. Despite these variabilities, our interventions almost always result in one of a number of limited (i.e., not infinite) familiar patterns of outcomes.

The early successes of medicine arose mainly from the insights of reductionist research that explained the ‘simple’ cause-and-effect mechanisms of then common and life-shortening infectious diseases. However, 21st century medicine mostly struggles with chronic and complex diseases whose successful management demands a systemic understanding of the ‘complex’ interactions amongst the multiple variables from across the different scales of organization.

Put pragmatically, studying the effect of a defined antibiotic on a defined bacterium, an antihypertensive on blood pressure changes, or an antidepressant on a change in mood/anxiety scores in the laboratory would rightly be best done using the reductionist cause-and-effect research approach. However, many of these findings produced in the highly controlled laboratory environment are not reproduced in ‘real world’ clinical trial settings.

Clinically relevant questions necessitate systemic research approaches focused on patient relevant outcomes like:

In the laboratory setting, research typically focuses narrowly on one-to-one relationships in the absence of any other contextual constraints (8). What may work well in the deliberately chosen context-free laboratory setting does not necessarily also work as a clinical intervention in diverse clinical settings. By their very nature clinical events are caused by a multitude of interacting factors. Clinical interventions cause one-to-many interactions simultaneously affecting physiological, environmental as well as sense-making/coping systems. Put succinctly, one-to-many relationships cannot be studied by ‘squeezing’ them into ‘sanitized’ one-to-one methodologies (8).

The bottom section of Figure 1 - bottom provides contrasting examples of research questions that either focus on mechanistic cause-and-effect problems or seek to gain insights and understandings of the complex interconnected and interdependent one-to-many cause-and-effect dynamics impacting people’s health.

Researching a cause-and-effect problem like determing whether ‘a new antibiotic kills a bacterium in vitro’ falls within the Newtonian research paradigm. It requires repeating the same experiment to determine the reliability of observations.

In contrast research to understand observable differences, i.e., patterns, related to a particular phaenomenon [e.g., blood sugar dynamics in insulin-dependent diabetics (29), or experiencing significant diabetes symptoms despite adequate blood sugar control (30)] requires a different approach. Patterns emerge depending on the interactions and combinations of several contributing variables. Pattern analysis techniques like cluster (31) or network (32) analysis can identify which combinations and interactions lead to each of the observed outcomes of interest, and may guide further research in understanding their ‘causal pathways.’ Figure 1 contrasts the differences between the two frames, and Figure 2 illustrates how cluster analysis techniques can inform the management of coronary artery disease (33).

Research, regardless of its methodology and rigor, provides additional data rather than information or knowledge (40). Statistics indicate the probability that – at the population level – these data correlate with particular population observations. However, statistical correlation does not equate to causation. Statistical correlation can only infer a potential causal relationship with a certain probability, and only if the relationship is based on a strong pathophysiologic rationale (41, 42). Hence, it is the researcher’s responsibility to provide critical contextual interpretation of new data to justify their integration to existing understandings. As clinicians we must consider the new understandings in relation to their applicability at the individual/population level, but most importantly, in their unique contexts. And finally, research cannot relieve us from the task of making decisions and being responsible for them.

In conclusion, achieving more predictable medical interventions requires a more comprehensive understanding of which systemic variables, and which contexts, lead to the variety of our observable outcome patterns. Recent systems-focused research has demonstrated improvements in diabetes management (71), the drug treatment of hypertension (72), understanding the treatment of depression (73) and the treatment of brain tumors (74) but is, at this stage, a notable exception in clinical research. More systems-focused studies would significantly contribute to the knowledge required to define which outcome pattern a patient – and especially those with multiple morbidities – most likely will belong to. Understanding the underlying bio-medical, social, emotional and interpersonal features (75) underpinning outcome patterns would then enable us to offer the most likely treatment to remedy the issue of concern.

In summary, the reductionist medical research of the late 19th/early 20th century undoubtedly has lead to great benefits in understanding and treating the predominant infectious diseases of the time. However, it failed to achieve the same benefits in relation to the now predominant chronic and multimorbidid conditions affecting our patients. These problems are systemic in nature, i.e., they are the result of interconnected and interdependent activities spanning from the gene to the societal level. From a pragmatic perspective, we need to firstly shift our way of thinking toward an eco-systemic frame, and secondly, need to further develop the as yet embryonic eco-systemic research tools to find those solutions that allow us to offer the most likely beneficious approaches to each of our patients. And lastly, there is an urgent need to re-orientate our undergraduate medical courses to develop critical analytic thinking, and to teach our post graduate specialty trainees a wide range of research methodologies beyond the RCT.

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

JS: Writing – review & editing, Writing – original draft, Visualization, Resources, Conceptualization. JM: Writing – review & editing, Writing – original draft, Conceptualization. FT: Writing – review & editing, Writing – original draft, Conceptualization. TK: Writing – review & editing, Writing – original draft, Conceptualization.