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Digital Transformation of Cold Case Reviews: Digitizing Case Files

NCJ Number
Date Published
November 2022
20 pages

This in-brief is the second in a three-part series, and it focuses on the processes and resources that are available for agencies and multidisciplinary teams (MDTs) to digitize their cold case files, and how that allows for the application of AI-based text analytics.


This in-brief is the second in a three-part series that highlights the potential value, approaches, and considerations for digitization of cold case files to facilitate the review process; the authors’ specific focus was on sexual assault and violent crime cold cases, but the technology may also be applied to other types of cold case files. This paper focuses on the processes and potential impact of digitizing case files. The in-brief’s objectives are to communicate the benefits and realities of digitizing cold case files, providing example case studies; outline strategies that multidisciplinary teams (MDTs) may employ to digitize their cold case data; and to present considerations for MDTs implementing digitization technology. Key takeaways suggest that digitizing information from cold case files can streamline the case review process in many ways, enabling an MDT of allied professionals to easily and securely store, search, and access data across different cases, and share information with collaborators; fully digitizing data involves scanning documents and converting their contents to “machine-readable” text that can be searched, through the use of optical character recognition (OCR) which is currently unable to convert handwritten notes; MDTs can use various transcription or scanning methods to digitize cold case data, and the in-brief provides case study examples of successful digitization projects; the digitization process may require organizations to seek outside funding or resources; current adoption of artificial intelligence (AI)-based text analysis is limited but provides an efficient tool for gaining insights across large volumes of text-based data.

Date Published: November 1, 2022