Utilization and efficacy of DotPhrases in the electronic medical record for improving physician documentation
Abstract
Clinical documentation of patient visits has changed over the last 20 years, with the increasing use of electronic records causing a seismic shift in how notes are taken. Electronic note-taking aims at reducing the time taken to document a visit, and the introduction of dot phrases, or DotPhrases, in electronic medical records is a step toward reducing the time required to update patients' charts, which might allow doctors to spend more time with their patients. DotPhrases, abbreviated phrases used in the electronic medical record, help in the simplification of note-taking and the standardization of notes. They also allow for a more comprehensive note from physicians and ensure that no information is undocumented. On the contrary, however, excessive usage of DotPhrases can lead to an excessively long and cumbersome note. This can overwhelm physicians and lead to them missing crucial information that is buried somewhere in the notes. Although there is ample research studying the benefits of DotPhrases, adequate research must also be carried out to understand their shortcomings and disadvantages. This article aims to shed some light on use of DotPhrases and to outline their advantages and disadvantages affecting patient management and care.
Read the paper
Related studies
- Current state and future directions for deep learning based automatic seismic fault interpretation: A systematic review
- Physics of Partially Saturated Porous Media: Residual Saturation and Seismic-Wave Propagation
- Modern Imaging Using Seismic Reflection Data
- Effects of marine seismic surveys on free-ranging fauna: a systematic literature review
- A review of the recent seismic activity in the southern part of egypt (upper egypt)
- A review of research on mechanism of seismic attenuation and energy compensation
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.