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Analytic Hierarchy Process (AHP) in Dynamic Configuration as a Tool for Health Technology Assessment (HTA): The Case of Biosensing Optoelectronics in Oncology

Author

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  • Giovanni Improta

    (Department of Public Health, School of Medicine and Surgery, University of Naples “Federico II”, Via Pansini 5, 80131 Naples, Italy)

  • Giuseppe Converso

    (#x2020;Department of Chemical, Materials and Production Engineering, University of Naples “Federico II”, Piazzale V. Tecchio 80, 80125 Naples, Italy)

  • Teresa Murino

    (#x2020;Department of Chemical, Materials and Production Engineering, University of Naples “Federico II”, Piazzale V. Tecchio 80, 80125 Naples, Italy)

  • Mosè Gallo

    (#x2020;Department of Chemical, Materials and Production Engineering, University of Naples “Federico II”, Piazzale V. Tecchio 80, 80125 Naples, Italy)

  • Antonietta Perrone

    (Department of Public Health, School of Medicine and Surgery, University of Naples “Federico II”, Via Pansini 5, 80131 Naples, Italy‡Clinical Engineering, University Hospital “A.O.U. Federico II”, Via Pansini 5, 80131 Naples, Italy)

  • Maria Romano

    (#xA7;Department of Medical and Surgical Sciences, University “Magna Graecia” of Catanzaro, Campus Universitario “Salvatore Venuta”, Viale Europa, 88100 Catanzaro, Italy)

Abstract

The Analytic Hierarchy Process (AHP) is a methodology, based on both mathematical and psychological approaches, exploited to analyze and solve complex problems, in order to make the best decision. AHP is also widely employed for the evaluation of healthcare systems. It works by splitting the decision problem into a hierarchy of more easily-comprehended sub-problems, each of which can be independently analyzed. Starting from these assumptions, our work aims to implement a dynamic framework for the AHP methodology, able to overcome the static nature of the technique, taking into account the evaluations associated with criteria and sub-criteria as a function of time. In the context of this paper, the Dynamic AHP is implemented as a tool for Health Technology Assessment (HTA). Indeed, the proposed case study concerns the evaluation of a new health technology for the thyroglobulin assay in patients with differentiated thyroid cancer. To this aim, we have defined the decision problem and formalized criteria and sub-criteria hierarchically; then, in order to deal with the problem from a dynamical point of view, i.e., to take into account the time dependence of criteria and sub-criteria, we developed several System Dynamics models, one for each criterion. In this context, it is shown that the Dynamic AHP approach provides a dynamic evaluation of the system, which allows to identify the best decision by exploiting the information on the possible evolution of the problem due to its time behavior, rather than taking the decision at a fixed time point, as the Static AHP does.

Suggested Citation

  • Giovanni Improta & Giuseppe Converso & Teresa Murino & Mosè Gallo & Antonietta Perrone & Maria Romano, 2019. "Analytic Hierarchy Process (AHP) in Dynamic Configuration as a Tool for Health Technology Assessment (HTA): The Case of Biosensing Optoelectronics in Oncology," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(05), pages 1533-1550, September.
  • Handle: RePEc:wsi:ijitdm:v:18:y:2019:i:05:n:s0219622019500263
    DOI: 10.1142/S0219622019500263
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    References listed on IDEAS

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    Cited by:

    1. Emma Montella & Antonino Ferraro & Giancarlo Sperlì & Maria Triassi & Stefania Santini & Giovanni Improta, 2022. "Predictive Analysis of Healthcare-Associated Blood Stream Infections in the Neonatal Intensive Care Unit Using Artificial Intelligence: A Single Center Study," IJERPH, MDPI, vol. 19(5), pages 1-9, February.
    2. Arianna Scala & Teresa Angela Trunfio & Lucia De Coppi & Giovanni Rossi & Anna Borrelli & Maria Triassi & Giovanni Improta, 2022. "Regression Models to Study the Total LOS Related to Valvuloplasty," IJERPH, MDPI, vol. 19(5), pages 1-13, March.
    3. Carlo Ricciardi & Giovanni Dell’Aversana Orabona & Ilaria Picone & Imma Latessa & Antonella Fiorillo & Alfonso Sorrentino & Maria Triassi & Giovanni Improta, 2021. "A Health Technology Assessment in Maxillofacial Cancer Surgery by Using the Six Sigma Methodology," IJERPH, MDPI, vol. 18(18), pages 1-16, September.
    4. Giovanni Improta & Anna Borrelli & Maria Triassi, 2022. "Machine Learning and Lean Six Sigma to Assess How COVID-19 Has Changed the Patient Management of the Complex Operative Unit of Neurology and Stroke Unit: A Single Center Study," IJERPH, MDPI, vol. 19(9), pages 1-19, April.
    5. Arianna Scala & Ilaria Loperto & Maria Triassi & Giovanni Improta, 2022. "Risk Factors Analysis of Surgical Infection Using Artificial Intelligence: A Single Center Study," IJERPH, MDPI, vol. 19(16), pages 1-10, August.
    6. Alfonso Maria Ponsiglione & Francesco Amato & Santolo Cozzolino & Giuseppe Russo & Maria Romano & Giovanni Improta, 2022. "A Hybrid Analytic Hierarchy Process and Likert Scale Approach for the Quality Assessment of Medical Education Programs," Mathematics, MDPI, vol. 10(9), pages 1-20, April.

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