A Six Sigma DMAIC methodology as a support tool for Health Technology Assessment of two antibiotics
Author(s) -
Alfonso Maria Ponsiglione,
Carlo Ricciardi,
Giovanni Improta,
Giovanni Dell’Aversana Orabona,
Alfonso Sorrentino,
Francesco Amato,
Maria Romano
Publication year - 2021
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2021174
Subject(s) - dmaic , six sigma , context (archaeology) , computer science , reliability (semiconductor) , health care , quality (philosophy) , control (management) , measure (data warehouse) , quality management , process (computing) , process management , reliability engineering , operations management , risk analysis (engineering) , medicine , engineering , data mining , management system , artificial intelligence , philosophy , economic growth , biology , operating system , paleontology , power (physics) , epistemology , quantum mechanics , physics , lean manufacturing , economics
Health Technology Assessment (HTA) and Six Sigma (SS) have largely proved their reliability in the healthcare context. The former focuses on the assessment of health technologies to be introduced in a healthcare system. The latter deals with the improvement of the quality of services, reducing errors and variability in the healthcare processes. Both the approaches demand a detailed analysis, evidence-based decisions, and efficient control plans. In this paper, the SS is applied as a support tool for HTA of two antibiotics with the final aim of assessing their clinical and organizational impact in terms of postoperative Length Of Stay (LOS) for patients undergoing tongue cancer surgery. More specifically, the SS has been implemented through its main tool, namely the DMAIC (Define, Measure, Analyse, Improve, Control) cycle. Moreover, within the DMAIC cycle, a modelling approach based on a multiple linear regression analysis technique is introduced, in the Control phase, to add complementary information and confirm the results obtained by the statistical analysis performed within the other phases of the SS DMAIC. The obtained results show that the proposed methodology is effective to determine the clinical and organizational impact of each of the examined antibiotics, when LOS is taken as a measure of performance, and guide the decision-making process. Furthermore, our study provides a systematic procedure which, properly combining different and well-assessed tools available in the literature, demonstrated to be a useful guidance for choosing the right treatment based on the available data in the specific circumstance.
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