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

Almost all the papers in this review applied AI, machine learning, and NLP techniques to extract structured data from unstructured clinical narratives in both English data sources as well as sources in other languages. Tasks such as applying billing codes or populating cancer registries or assisting with clinical research are key roles for HIM professionals. Collectively, the set of papers show the potential for these techniques to improve the efficiency of what have been laborious manual processes.

In the future, the uses of AI and machine learning methods to mine structured, and increasingly, unstructured, data from EHRs are likely to expand. Such expansion, in addition to clinical and health services research that make use of data in EHRs, might also include risk scoring and other predictive modeling, population health management, analyses for revenue enhancement, and quality assurance activities. As the survey paper of the HIM section of the IMIA Yearbook, authored by Stanfill et al. makes clear, when the use of these methods becomes more integrated into research and clinical activities, the need to address a variety of technical and ethical issues, including those related to data quality, as well as privacy and security, will be increasingly recognized. HIM professionals can play a key role in addressing these issues, but the issues themselves are important to many professions and multiple and diverse research domains.

Given the importance of AI methods and approaches to the field of Health Information Management, it was striking that the MeSH headings of papers that represent cutting edge work in the use of AI concepts rarely included MeSH headings related to HIM, although these articles could be found with searches that included the AI concepts and EHRs. Similarly, the set of papers that included HIM-related MeSH headings did not include papers on AI methods. It is difficult to tell whether the lack of overlap of the AI literature and HIM is a result of how the article authors chose key words, how the MeSH coders assigned headings, or the fact that HIM professionals are not involved in this research and the researchers do not identify with HIM. Whatever the cause, the results of the 2018 literature search as well as the discussion in the survey paper highlight the need for HIM professionals to become more knowledgeable about these new approaches and to bring their expertise to the research applying these methods in practice.

The full article can be downloaded below.