  Question Answering (QA) systems on patient-related data can assist both
clinicians and patients. They can, for example, assist clinicians in
decision-making and enable patients to have a better understanding of their
medical history. Significant amounts of patient data are stored in Electronic
Health Records (EHRs), making EHR QA an important research area. In EHR QA, the
answer is obtained from the medical record of the patient. Because of the
differences in data format and modality, this differs greatly from other
medical QA tasks that employ medical websites or scientific papers to retrieve
answers, making it critical to research EHR question answering. This study
aimed to provide a methodological review of existing works on QA over EHRs. We
searched for articles from January 1st, 2005 to September 30th, 2023 in four
digital sources including Google Scholar, ACL Anthology, ACM Digital Library,
and PubMed to collect relevant publications on EHR QA. 4111 papers were
identified for our study, and after screening based on our inclusion criteria,
we obtained a total of 47 papers for further study. Out of the 47 papers, 25
papers were about EHR QA datasets, and 37 papers were about EHR QA models. It
was observed that QA on EHRs is relatively new and unexplored. Most of the
works are fairly recent. Also, it was observed that emrQA is by far the most
popular EHR QA dataset, both in terms of citations and usage in other papers.
Furthermore, we identified the different models used in EHR QA along with the
evaluation metrics used for these models.
