Findings from the 2019 International Medical Informatics Association Yearbook Section on Health Information Management

• Published in: Yearbook of medical informatics Vol. 28; no. 1; pp. 065 - 068
• Main Authors: Bloomrosen, Meryl, Berner, Eta S.
• Format: Journal Article
• Language: English
• Published: Stuttgart Georg Thieme Verlag KG 01.08.2019
• Subjects: Artificial Intelligence; Clinical Coding - methods; Data Mining; Electronic Data Processing; Health Information Management; Humans; Medical Informatics; Neoplasms; Registries; Section 1: Health Information Management; Health information management; text mining; electronic health records; natural language processing; artificial intelligence; automated coding
• ISSN: 0943-4747; 2364-0502; 2364-0502
• DOI: 10.1055/s-0039-1677941

Summary Objectives : To summarize the recent literature and research and present a selection of the best papers published in 2018 in the field of Health Information Management (HIM) and Health Informatics. Methods : A systematic review of the literature was performed, with the help of a medical librarian, by the two editors of the HIM section of the International Medical Informatics Association (IMIA) Yearbook. In order to include papers that would address the special theme of the 2019 Yearbook on artificial intelligence (AI) as well as HIM, we searched bibliographic databases for HIM-related papers with an AI focus using both Medical Subject Headings (MeSH) descriptors and keywords in titles and abstracts. A shortlist of 15 candidate best papers was first selected by section editors before being peer-reviewed by independent external reviewers. Results : While there were a significant number of manuscripts that addressed issues relevant to HIM, there were virtually none with MeSH headings indicating an HIM focus. Manuscripts that were considered related to the HIM field in terms of the practice of health information management as well as the profession included those that examined using machine learning and other AI approaches to identify protected health information in clinical text to aid with de-identification, automated coding approaches to translate free-text into standardized codes, and natural language processing approaches to extract clinical data to assist with populating cancer and other registries. Conclusions : The papers discussed in the HIM section reflect the special theme of the use of AI in healthcare on issues particularly relevant to the field of HIM. This synopsis discusses these papers and recommends that HIM practitioners be more involved in research and that researchers in AI and related areas recognize the applicability and relevance of their work to the field of HIM.