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Measuring the maturity of business intelligence in healthcare: Supporting the development of a roadmap toward precision medicine within ISMETT hospital

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  • Gastaldi, Luca
  • Pietrosi, Astrid
  • Lessanibahri, Sina
  • Paparella, Marco
  • Scaccianoce, Antonio
  • Provenzale, Giuseppe
  • Corso, Mariano
  • Gridelli, Bruno

Abstract

Business Intelligence (BI) has the potential to disrupt the processes through which healthcare services are offered. Despite this key role, most healthcare organizations fail in implementing or extending BI suites from the pilot niches in which these solutions are usually developed and tested to larger domains. In fact, healthcare practitioners lack comprehensive models that suggest the priorities to be followed for progressively developing a BI solution. This paper aims to start filling these gaps by developing a model through which: (i) to measure and increase the maturity of a BI solution within a healthcare organization; (ii) to enable extensive processes of benchmarking and continuous improvement.

Suggested Citation

  • Gastaldi, Luca & Pietrosi, Astrid & Lessanibahri, Sina & Paparella, Marco & Scaccianoce, Antonio & Provenzale, Giuseppe & Corso, Mariano & Gridelli, Bruno, 2018. "Measuring the maturity of business intelligence in healthcare: Supporting the development of a roadmap toward precision medicine within ISMETT hospital," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 84-103.
    • Handle: RePEc:eee:tefoso:v:128:y:2018:i:c:p:84-103
    DOI: 10.1016/j.techfore.2017.10.023
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    References listed on IDEAS

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    Cited by:

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    2. Nazarenko, Anastasia & Vishnevskiy, Konstantin & Meissner, Dirk & Daim, Tugrul, 2022. "Applying digital technologies in technology roadmapping to overcome individual biased assessments," Technovation, Elsevier, vol. 110(C).
    3. Denicolai, Stefano & Previtali, Pietro, 2020. "Precision Medicine: Implications for value chains and business models in life sciences," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    4. Maria Guadalupe Medina Barrera & Argelia B. Urbina Najera, 2021. "Medicion de Madurez en la Implementacion de Inteligencia de Negocios en PYMEs de TI(Measuring Business Intelligence Implementation Maturity in IT SMEs)," Revista Internacional de Gestión del Conocimiento y la Tecnología (GECONTEC), Revista Internacional de Gestión del Conocimiento y la Tecnología (GECONTEC), vol. 9(1), pages 61-79, May.
    5. Rafael, Lizarralde Dorronsoro & Jaione, Ganzarain Epelde & Cristina, López & Ibon, Serrano Lasa, 2020. "An Industry 4.0 maturity model for machine tool companies," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    6. Xing, Fei & Peng, Guochao & Zhang, Bingqian & Li, Shuyang & Liang, Xinting, 2021. "Socio-technical barriers affecting large-scale deployment of AI-enabled wearable medical devices among the ageing population in China," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    7. Zhang, Hao & Daim, Tugrul & Zhang, Yunqiu (Peggy), 2021. "Integrating patent analysis into technology roadmapping: A latent dirichlet allocation based technology assessment and roadmapping in the field of Blockchain," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    8. Vincenzo Vignieri & Carmine Bianchi & Astrid Pietrosi & Giuseppe Provenzale, 2019. "Il contributo delle reti sanitarie "spontanee" al miglioramento degli outcome: opportunit? e sfide nella prospettiva della progettazione dei sistemi di programmazione e controllo," MECOSAN, FrancoAngeli Editore, vol. 2019(109), pages 83-104.

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