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Garas G, Cingolani I, Panzarasa P, et al. Network analysis of surgical innovation: Measuring value and the virality of diffusion in robotic surgery. PLoS One. 2017;12(8):e0183332doi: 10.1371/journal.pone.0183332.
Garas, G., Cingolani, I., Panzarasa, P., Darzi, A., & Athanasiou, T. (2017). Network analysis of surgical innovation: Measuring value and the virality of diffusion in robotic surgery. PloS one, 12(8), e0183332. https://doi.org/10.1371/journal.pone.0183332
Garas, George, et al. "Network analysis of surgical innovation: Measuring value and the virality of diffusion in robotic surgery." PloS one vol. 12,8 (2017): e0183332. doi: https://doi.org/10.1371/journal.pone.0183332
Garas G, Cingolani I, Panzarasa P, Darzi A, Athanasiou T. Network analysis of surgical innovation: Measuring value and the virality of diffusion in robotic surgery. PLoS One. 2017 Aug 25;12(8):e0183332. doi: 10.1371/journal.pone.0183332. eCollection 2017. PMID: 28841648; PMCID: PMC5571947.
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PLoS One. 2017 Aug 25;12(8):e0183332. doi: 10.1371/journal.pone.0183332. eCollection 2017.

Network analysis of surgical innovation: Measuring value and the virality of diffusion in robotic surgery.

PloS one

George Garas, Isabella Cingolani, Pietro Panzarasa, Ara Darzi, Thanos Athanasiou

Affiliations

  1. Surgical Innovation Center, Department of Surgery and Cancer, Imperial College London, St. Mary's Hospital, London, United Kingdom.
  2. Department of Surgical Research and Innovation, The Royal College of Surgeons of England, London, United Kingdom.
  3. Big Data and Analytical Unit, Imperial College London, St. Mary's Hospital, London, United Kingdom.
  4. School of Business and Management, Queen Mary University of London, London, United Kingdom.

PMID: 28841648 PMCID: PMC5571947 DOI: 10.1371/journal.pone.0183332

Abstract

BACKGROUND: Existing surgical innovation frameworks suffer from a unifying limitation, their qualitative nature. A rigorous approach to measuring surgical innovation is needed that extends beyond detecting simply publication, citation, and patent counts and instead uncovers an implementation-based value from the structure of the entire adoption cascades produced over time by diffusion processes. Based on the principles of evidence-based medicine and existing surgical regulatory frameworks, the surgical innovation funnel is described. This illustrates the different stages through which innovation in surgery typically progresses. The aim is to propose a novel and quantitative network-based framework that will permit modeling and visualizing innovation diffusion cascades in surgery and measuring virality and value of innovations.

MATERIALS AND METHODS: Network analysis of constructed citation networks of all articles concerned with robotic surgery (n = 13,240, Scopus®) was performed (1974-2014). The virality of each cascade was measured as was innovation value (measured by the innovation index) derived from the evidence-based stage occupied by the corresponding seed article in the surgical innovation funnel. The network-based surgical innovation metrics were also validated against real world big data (National Inpatient Sample-NIS®).

RESULTS: Rankings of surgical innovation across specialties by cascade size and structural virality (structural depth and width) were found to correlate closely with the ranking by innovation value (Spearman's rank correlation coefficient = 0.758 (p = 0.01), 0.782 (p = 0.008), 0.624 (p = 0.05), respectively) which in turn matches the ranking based on real world big data from the NIS® (Spearman's coefficient = 0.673;p = 0.033).

CONCLUSION: Network analysis offers unique new opportunities for understanding, modeling and measuring surgical innovation, and ultimately for assessing and comparing generative value between different specialties. The novel surgical innovation metrics developed may prove valuable especially in guiding policy makers, funding bodies, surgeons, and healthcare providers in the current climate of competing national priorities for investment.

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