Machine-guns vs. Sniper Rifles – Outcome-Driven Innovation in early-career research

With the frontier of science being so vast and broad, I’ve always wondered what drives a scientist’s curiosity and interest in pursuing a particular line of research. It can be argued that all questions in science are important, given the right context, and that the answer to each question ultimately builds an overall picture that can influence the way the world is perceived, the way technology progresses and how we as humans use that knowledge to evolve.

Though very altruistic, the reality of research today is in stark contrast to the noble aspirations of natural philosophers in the 18th century and the early investigators of the 19th and early 20th centuries. The advent of silicon electronics during the latter half of the 20th century has accelerated the pace of scientific endeavours, opened completely new fields and consequently, created an entire industry based on the progress of human knowledge. Funding of scientific research is so competitive and cut-throat that one would think that it was more like war than a noble pursuit.

Though many new discoveries have led to new inventions, some have been more endearing than others. Inventions that were so disruptive at the time that it completely changed the way that humans did things, and often, caused entire industries to be wiped out overnight. Today, the pace of disruption occurs faster than ever, and will continue to do so.

Innovation is the hot topic of many bloggers, but my focus is on a stage in a scientist’s life where choices may be plenty, and serendipity often plays a role in choosing a particular path. This post will hopefully be of most use to pre-PhD or near-completion postgrads, where there’s a bit of stumbling in the transition phase from a postgraduate student to a post-doctoral researcher (if that’s the path of choice).

Disruptive science projects can be considered to be ones that have “high impact”, and I don’t just mean in a publication sense, but impact that reaches beyond a paper being published in a top-tier journal. Impact where an end-user actually stands to benefit from the research that was previously performed, and generally in a way that is completely different to the way in which the problem is currently addressed. That is to say that the outcomes of a disruptive scientific project may actually change the way the problem is viewed or addressed. Disruptive science (as well as its cousin, disruptive technology) has the ability to shift status quo to make solving problems more efficiently, in a more cost-effective manner, or both.

By contrast, sustaining (adjective) science projects can be considered as incremental steps to building a larger picture of a particular problem.  Sustaining science projects are sometimes easy to spot; research in a particular field contributes to a body of knowledge that can be eventually used to solve well-known, well-defined problems. The phrase “fundamental work” springs to mind.

In the biomedical sciences, there are many postgraduate projects that one could consider as “sustaining projects”. Projects offered to them by a supervisor that sounded good at the time. Projects offered in a laboratory that has a good publication track record. It’s any wonder why so many postgraduate students feel disillusioned halfway through their candidature – for them to be able to see the relevance of the research on how it impacts of patient at the bedside is not only difficult, but also quite disheartening. Similarly in the clinical sciences, there are many interesting questions that can be asked that have a direct impact on patient health outcomes. With so many questions choose from, it can be quite daunting for a naive postgraduate student to pick a research topic that truly has a revolutionary impact on a patient.

In both cases, there are some inherent shortcomings in the way a project may or may not be selected by potential postgraduate student. In the case of the biomedical sciences, there are so many questions that can be asked and answered at various levels of biological abstraction (level of DNA, cell, organelle, organ or animal) that it can be difficult to see in the “noise” of potential projects which one will have a greater impact in future than others. In the clinical sciences, there can be many ways to address the research problem but without identifying the key stakeholders, these projects are also fraught with risk. So without prior knowledge of the outcome of the project, it can be almost impossible to pick a “winner”. There may be a “winner”, but the chances of picking it are slim. It also means that the student is not in a good position to capitalise on any serendipitous discoveries, as their project does not align the student’s frame of work with those possible discoveries.

In a white paper* “What is Outcome-Driven Innovation” published by Anthony W. Ulwick of a San Francisco-based innovation strategy consultancy Strategyn, Ulwick purports that the “ideas-first” approach is inherantly flawed, as it requires a lot of resources to ideate many ideas to find a winner. In the case of the health sciences, it means a lot of postgrad students doing work that may eventually be deemed useless to the field (at the time of the work being done). I have seen many postgrad students deny this reality in their own different ways – I have often heard “Oh, but my project’s outcomes will eventually be useful to someone, someday”. Some students are happy to “chugg” along with the fact that their science may not lead to anything they see as useful, and can be happy with that reality. But for others that hold onto the noble belief that science must, in some way, contribute to society, this reality can be emotionally draining, especially when the project requires so much of their own time and effort (aka blood, sweat and tears). The machine-gun approach is highly wasteful of resources, and doesn’t necessarily hit the desired targets.

For these students, a “needs-first approach” would be more appropriate, especially when it comes to deciding what project they wish to embark on. However, the needs-first approach is structurally flawed, in the sense that student must understand the customers’ (patient, parent, healthcare provider, healthcare subsidiser) needs. The way we currently encourage scientific endeavour does not support this approach to selecting a project. Many of the funding bodies are starting to provide funding for needs-based research, however, many scientists are still in-grained with the traditional “ideas-first” approach to scientific project ideation, meaning they fail to recognise the opportunity for funding outside of their primary approach to selecting projects. The bad habits of the ideas-first approach are being passed on from one generation of scientist to another, and it’s unsustainable. The overall way in which we fund and encourage scientific endeavours are also flawed, and are ripe for disruption as well. But that’s a topic for another blog post.

The needs-first approach to research is more evident in the physical sciences – PhD’s in these areas are often tied to the needs of industry (i.e,. the geosciences for assisting the mining industry in Australia) so there are many quality projects available, and often, not enough students to perform this work. The glut of young researchers in the biomedical sciences may be attributed to the fact that many of the projects are ideated by more senior scientists under the inherently flawed ideas-first approach.

A well-informed potential health sciences postgrad student that understands the value proposition of the project and takes the “needs-first” approach is more likely to pick a satisfying project that can lead to better self-perceived outcomes, as well as higher impact where the patient’s needs are clearly defined. In effect, using a sniper scope to pick out a potential target. This is great for the student’s morale over the candidature period. Added in with serendipity, the needs-first approach puts the student in a better position to capitalise on any new discoveries that result from the research, as the application of both the current and new discoveries are better aligned to high-impact outcomes for priority research areas. Also, a needs-first approach to picking a project minimises many risks – risks of the student not being able to gain project momentum with key stakeholders (as stakeholders are more likely to be motivated by the value proposition of a project with high-impact outcomes), risk of few publications and risk that the student eventually becomes “tired” of their own research and thus risk of the student not completing.

The main problem, as I see it, is that we don’t teach the principles of outcome-driven innovation (ODI) to undergraduates. By the time they start considering postgraduate research options, they are ill-prepared with the correct tools to pick satisfying, high-impact research projects.

I would strongly advocate for entrepreneurship to be taught at both the undergraduate and postgraduate level – many new PhD models incorporate some form of commercialisation training, but the general principles of entrepreneurship can be applied not only in a commercialisation context, but in a broader translational sense. Scientists and entrepreneurs both have their tools of the trade, and both venture into the unknown – so why don’t they share such tools?

Translational research and research translation require the scientist to not necessarily be an entrepreneur, but be able to harness the concepts of entrepreneurship to be truly successful beyond the tier-1 journal publication. We should be teaching the necessary tools and skills of the entrepreneur to young scientists to not only help them pick a good postgrad project, but also empower them to map out their career in the long-term. The “needs-first” approach to project ideation is not as obvious from the outset, but as it’s structurally flawed (in the sense that we don’t often see the pathway to choosing a “good project” unless we are taught how to “see”), structural flaws can be more easily overcome than the inherent flaws of the “ideas-first” approach.

In my next blogpost I’ll talk about some of these tools, how they can help define the scope of the Minimum Viable Solution (the thesis) and why working as a research assistant before you start a PhD gives you unconcious conciousness.

As always, comments and tweets welcome – I know you all have opinions on this topic!!!


*Strategyn now requires you to sign up to their mailing list to access their resources – it’s definitely worth it!


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