Artificial Intelligence
A Detection of Informal Abbreviations from Free Text Medical Notes Using Deep Learning
To parse free text medical notes into structured data such as disease names, drugs, procedures, and other important medical information first, it is necessary to detect medical entities. It is important for an Electronic Medical Record (EMR) to have structured data with semantic interoperability to serve as a seamless communication platform whenever a patient migrates from one physician to another. However, in free text notes, medical entities are often expressed using informal abbreviations. An informal abbreviation is a non-standard or undetermined abbreviation, made in diverse writing styles, which may burden the semantic interoperability between EMR systems. Therefore, a detection of informal abbreviations is required to tackle this issue.
- 1. Heryawan, Lukman, et al. "A Detection of Informal Abbreviations from Free Text Medical Notes Using Deep Learning." European Journal of Biomedical Informatics 16.1 (2020).
Human-Computer Interaction
Toward Design of an Agent-based Writing Support System for the SOAP Note: A Content Analysis of the Video-based Survey
Subjective, objective, assessment, and plan (SOAP) notes are widely used by physicians to document clinical reasoning in assessing, diagnosing, and treating patients. SOAP notes are also used in medical coding tasks for reimbursement of insurance claims. In Indonesia, medical coders who are independent from physicians assess SOAP notes to assign diagnostic codes and medical procedure codes based on the corresponding International Classification of Diseases standards. Discrepancies between physicians who write the SOAP notes and coders who assign diagnoses and treatments, may occur. These discrepancies were assessed by performing a video-based survey to understand the coder’s perspective, allowing the development of a writing support system to achieve unproblematic SOAP notes. This survey found that problematic SOAP notes were not caused by a single problem but by multiple problems. Abbreviations used by physicians are the major problem in assigning diagnostic codes, whereas incomplete data are the major problem in determining planning. This survey also showed that problematic SOAP notes may contain helpful keywords for coders that can help in determining diagnosis and treatment. The findings show that the system should be able to recognize separate sections of the SOAP note to provide writing support features and identify helpful keywords to encourage physicians to write unproblematic SOAP notes.
- 1. Heryawan, Lukman, et al. "Toward Design of an Agent-based Writing Support System for the SOAP Note: A Content Analysis of the Video-based Survey." Advanced Biomedical Engineering 9 (2020): 146-153.
Human-Computer Interaction
Agent-based Completion for Collecting Medical Note Parameters
In this paper, we present an agent-based completion that cooperates with a physician to optimize parameter-text pairs collection from a medical note. Currently, in hospitals, there is a medical coding process, which collects certain parameter-text pairs from the medical note's narrative text. The process starts with categorizing the text into certain parameter-text pairs. Then, the collected pairs are studied by the coders to produce correct medical codes. However, since the physicians may not aware of the coders' requirements, the medical coding process is quite problematic, such as lack of parameter-text pairs. To address this problem, we propose an agent-based completion, which represents the coder's view to categorize the text into certain parameter-text pairs and recommend the parameters to be filled. This paper shows a basic design of the agent and the background technologies to support the completion system.
- 1. Heryawan, Lukman, et al. "Agent-based Completion for Collecting Medical Note Parameters." Proceedings of the 7th International Conference on Human-Agent Interaction. 2019.
