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The Chain of Custody in the Era of Modern Forensics: From the Classic Procedures for Gathering Evidence to the New Challenges Related to Digital Data.

Tommaso D'AnnaMaria PuntarelloGiovanni CannellaGiovanni ScalzoRoberto BuscemiStefania ZerboAntonella Argo
Published in: Healthcare (Basel, Switzerland) (2023)
The purpose of this work is to renew the interest and attention for the chain of custody in forensic medicine, its establishment and maintenance, protecting the integrity and validity of evidence as well as to analyze how over time the establishment of the chain of custody and the collection of evidence has evolved also in function of the advent of technology and the use of electronic devices connected to the network. The analysis of the various aspects of the chain of custody demonstrates how necessary it is for the professional figures involved in the phases of the investigation (especially those who manage the evidence and who have, therefore, designated the assignment) to know the procedures to follow, trace the movement and the handling of objects subjected to seizure, also for the purposes of toxicological and/or histological investigations. The knowledge of interferences or complications helps to reduce errors and safeguard the validity of the evidence, assuring the proceeding judicial authority that the evidence is authentic and that it is, in other words, the same evidence seized at the scene of the crime. Furthermore, the issue is particularly felt today, with the recent need to guarantee the originality of digital data. Following a careful review and analysis of the literature currently available in this regard, it is worth adding that further efforts are needed to formulate internationally validated guidelines, harmonizing the different reference criteria in forensic science and medical areas, given the current absence of good international practices valid in the field and applicable both in the case of physical evidence and in the case of seizure of digital evidence.
Keyphrases
  • healthcare
  • public health
  • mental health
  • primary care
  • machine learning
  • risk factors
  • risk assessment
  • patient safety
  • electronic health record
  • clinical practice
  • big data
  • data analysis