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An evolutionary perspective on health innovation systems

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Abstract

This paper elaborates on the general properties of medical innovation processes. It begins with a critical review of different perspectives and methods of investigation used in various streams of research that have previously analysed technical change in the health sector. After profiling and discussing their characteristics, the paper proposes an evolutionary approach to change in medicine constructed around the notion of a ‘Health Innovation System’. Health innovation, it is argued, consists of complex bundles of new medical technologies and clinical services emerging from a highly distributed competence base. Health Innovation Systems are driven by the combination of (1) institutionally-bound interactions among agents (‘gateways’ of innovation) and (2) history-dependent trajectories of change (‘pathways’ of innovation) whose developments emerge from and feed back into the structure of the system through organised transfers of knowledge between research and clinical practice. After drawing examples from recent empirical work on clinical research in specific disease areas, the paper concludes by identifying implications for further research.

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Notes

  1. There is growing attention in the literature towards the adoption of information and communication technologies, such as computer-based hospital information systems, to support the infrastructure of health-care provision. This is relevant in relation to changing health care delivery which is no longer limited to traditional sites such as hospitals and physicians’ facilities, but embraces a variety of alternatives including hospital chains, hospital management corporations, freestanding emergency care centres, and ambulatory care centres. Though we recognize that this type of infrastructural innovation is a strong complement to advances in medical research, we will not address these issues directly in this paper. See Ash (1997).

  2. Slade and Anderson (2001), for example, assess whether national income is positively related to the rate of technology diffusion by comparing richer countries to developing ones. Likewise, Lázaro and Fitch (1995) find that aggregate measures of GDP and health-care expenditure per capita are significant determinants in a pool of OECD countries. Such studies confirm the stylised fact that higher income countries adopt new technologies earlier, though cross-country variations tend to decline over time.

  3. In this strand of works, Romeo et al. (1984) and Hirth et al. (2000) on US hospitals and Ikegami (1988) on Japanese hospitals find that low prospective payment rates generally bear a negative impact on the adoption of technologies, particularly cost-increasing ones, though the extent of this varies from case to case.

  4. Even more interestingly, perhaps, the observed influence in relatively closed communities is stronger than any kind of ‘external’ source such as, for example, the publication of laboratory results in the scientific literature.

  5. This is the rather well known case of ultrasound, laser and magnetic resonance technologies, among the many possible examples.

  6. This observation does not coincide with an endorsement of a ‘demand-pull’ perspective. The paradigm of innovation ‘on-demand’ does not reflect the long-term struggle of mankind with diseases, many of which cannot yet be solved even if the need for solutions could not be stronger. For a critique of demand-pull models in medicine, see Mokyr (1998).

  7. Classic references are, among others, Freeman (1995), Nelson and Rosenberg (1993), Lundvall (1992), and Edquist (1997). See also Cooke et al. (1997) on regional innovation systems. While a complete overview of the literary debate on the theme is beyond the scope of this paper, we will limit our discussion to works that are closely pertinent to our objective. We refer the interested reader to excellent reviews by Carlsson et al. (2002).

  8. The clinical trial effectively functions as a prototypical form of new service delivery.

  9. Dosi’s (1982) notion of technological trajectories is here clearly relevant.

  10. It is worth stressing that this exercise is not meant to be exhaustive with respect to the richness of dynamics that characterise HIS. It is rather a starting point for future analyses of the complementary dynamics of other building blocks of Health Innovation Systems.

  11. The dataset have been extracted through keyword search and subsequently implemented in the software Pajek. This is developed for network analysis and provided freely for academic use on http://vlado.fmf.uni-lj.si/pub/networks/pajek/.

  12. The first map results from a dataset of more that 11,200 papers published between 1979 and 2003. The second comprises more than 12,654 papers published between 1970 and 2003.

  13. An analysis of the organisational, as well as geographical, division of labour for interventional cardiology and glaucoma research can be found in Consoli and Ramlogan (2008). The reader will also find here in depth descriptions of the data and some useful statistics. The nodes representing organisations in the maps are colour-coded to reflect institutional types (university hospitals, general hospitals, firms, foundations).

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Acknowledgements

The authors would like to thank guest editors and referees for their many helpful suggestions. We are grateful to the organisers and participants of the Fifth EMAEE Conference (Manchester Metropolitan University, 17–19th May 2007) and also those of the SPRU 40th Anniversary Conference (University of Sussex, 11–13th September 2006), where another version of this paper was presented. Special thanks to Stan Melcalfe, Richard Nelson and Paul David, who contributed especially useful comments, and to Ronnie Ramlogan, who provided invaluable support. All remaining errors are obviously our own.

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  1. Manchester Institute of Innovation Research, Manchester Business School, University of Manchester, Harold Hankins Building, Booth Street West, M13 9QH, Manchester, UK

    Davide Consoli

  2. Centre for Business Research, University of Cambridge, Top Floor, Judge Business School Building, Trumpington Street, CB2 1AG, Cambridge, UK

    Andrea Mina

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  1. Davide Consoli
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Correspondence to Andrea Mina.

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Consoli, D., Mina, A. An evolutionary perspective on health innovation systems. J Evol Econ 19, 297–319 (2009). https://doi.org/10.1007/s00191-008-0127-3

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