Development of a digital research assistant for the management of patients’ enrollment in oncology clinical trials within a research hospital

Gianluca Franceschini, Filippo Lococo, Emilio Bria, Giampaolo Tortora, Riccardo Masetti, Giovanni Scambia, Alfredo Cesario, Ida Paris, Luca Boldrini, Armando Orlandi, Stefano Magno, Andrea Damiani, Gennaro Daniele, Guido Rasi

Risultato della ricerca: Contributo in rivistaArticolo in rivista

Abstract

Clinical trials in cancer treatment are imperative in enhancing patients’ survival and quality of life outcomes. The lack of communication among professionals may produce a non-optimization of patients’ accrual in clinical trials. We developed a specific platform, called “Digital Research Assistant” (DRA), to report real-time every available clinical trial and support clinician. Healthcare professionals involved in breast cancer working group agreed nine minimal fields of interest to preliminarily classify the characteristics of patients’ records (including omic data, such as genomic mutations). A progressive web app (PWA) was developed to implement a cross-platform software that was scalable on several electronic devices to share the patients’ records and clinical trials. A specialist is able to use and populate the platform. An AI algorithm helps in the matchmaking between patient’s data and clinical trial’s inclusion criteria to personalize patient enrollment. At the same time, an easy configuration allows the application of the DRA in different oncology working groups (from breast cancer to lung cancer). The DRA might represent a valid research tool supporting clinicians and scientists, in order to optimize the enrollment of patients in clinical trials. User Experience and Technology The acceptance of participants using the DRA is topic of a future analysis.
Lingua originaleEnglish
pagine (da-a)244-N/A
RivistaJournal of Personalized Medicine
Volume11
DOI
Stato di pubblicazionePubblicato - 2021

Keywords

  • Artificial intelligence
  • Breast cancer
  • Clinical trial
  • Lung cancer
  • Machine learning
  • Oncology
  • Patient enrollment
  • Personalized medicine
  • Web app

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