Medical Technology – Separating the Wheat from the Chaff

Scientists often come up with elegant inventions that they wish to exploit commercially. However, an elegant invention may not be cost-effective. The world is experiencing a period of massive growth in investments in high-tech start-up companies.[1] Many of these achieve high values in short time periods, despite not generating any sales  – Silicon Valley is home to 142 ‘unicorns’ (unlisted start-up companies valued at more than $1 billion).[1] It is no secret that investments of this type are highly speculative, driven more by sentiment than analysis. Currently a ‘bubble’ appears to be forming.[2] A company’s market value is theoretically equal to the net present value of future cash flows. Problem – if there are no present cash flows on which to base future projections, then the company’s value is a wild guess. But is it so wild or could it be tamed?

A substantial proportion of start-ups are based on medical technology – for example, the fabled ‘unicorn’, Theranos, which makes (or rather is attempting to make) revolutionary blood spot testing equipment. Medical innovations are increasingly procured on the rational grounds that they are cost-effective.[3] [4] Health economics provides a set of techniques to calculate the cost-effectiveness of new treatments to help decide whether a treatment or diagnostic method should be supported in a service [5]; in England, the National Institute for Health and Care Excellence (NICE) makes procurement decisions on this basis. A recent article from our CLAHRC [6] provides a synopsis of techniques to inform procurement decisions by calculating cost-effectiveness of a technology when it is still at the idea or design stage.[7] [8] This, in turn, can be used to determine the optimal price.[9] Then future cash flows can be calculated taking into account repayment of the initial investment. Health economics at the supply side (i.e. to inform investment decisions) has two fundamental differences from health economics at the demand side (i.e. to inform procurement decisions):

  1. Uncertainties are greater.
  2. Uncertainties can be resolved or reduced during development of the technology. This means that the option to develop the technology can be kept alive until more information has been collected.

The corollaries of those two fundamental points are that:

  1. Parameter estimates for supply-side economic models are ‘Bayesian’ in the sense that they are prior probability estimates derived from experts (rather than observed frequencies) and;
  2. The calculations must include the present value of holding an option that may, or may not, be pursued at some future date.

Economic models cannot give a definitive answer to investment decisions. However, human judgement is clouded by all sorts of faulty heuristics (mental processes),[10] [11] and models direct decision-makers to look closely at assumptions and provide at least a partial antidote to ‘optimism bias’.[12] They are a guide for the savvy investor and should strengthen the supply side of the medical technology industry, thereby mitigating the risk of boom and bust.

— Richard Lilford, Professor of Public Health, University of Warwick


  1. The Economist. Theranos: The fable of the unicorn. The Economist. 31 October 2015. 69-70.
  2. Mahmood T. The Tech Industry is in Denial, but the Bubble is About to Burst. Tech Crunch. 26 June 2015.
  3. Cleemput I, Neyt M, Thiry N, De Laet C, Leys M. Using threshold values for cost per quality-adjusted life-year gained in healthcare decisions. Int J Technol Assess Health Care. 2011; 27(1): 71-6.
  4. Schwarzer R, Rochau U, Saverno K, et al. Systematic overview of cost-effectiveness thresholds in ten countries across four continents. J Comp Eff Res. 2015; 4(5): 485-504.
  5. Drummond MF. Methods for the economic evaluation of health care programmes. Oxford: Oxford University Press. 2005.
  6. Girling A, Young T, Chapman A, Lilford R. Economic assessment in the commercial development cycle for medical devices. Intl J Technol Assess in Health Care. 2015. [ePub].
  7. Girling A, Young T, Brown C, Lilford R. Early-Stage Valuation of Medical Devices: The Role of Developmental Uncertainty. Value Health. 2010;13(5):585-91.
  8. Vallejo-Torres L, Steuten LMG, Buxton MJ, Girling AJ, Lilford RJ, Young T. Integrating health economics modelling in the product development cycle of medical devices: A Bayesian approach. Int J Technol Assess Health Care. 2008; 24(4): 459-64.
  9. Girling A, Lilford R, Young T. Pricing of medical devices under coverage uncertainty – a modelling approach. Health Econ. 2012. 21(12): 1502-7.
  10. Kahneman D. Thinking, Fast and Slow. London: Penguin Group. 2012.
  11. Kahneman D, Slovic P, Tversky A. Judgement under Uncertainty: Heuristics and Biases. Cambridge: Cambridge University Press. 1982.
  12. Sharot T. The Optimism Bias. Curr Biol. 2011; 21(23): R941-5.

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