Creation of an Accurate Algorithm to Detect Snellen Best Documented Visual Acuity from Ophthalmology Electronic Health Record Notes.
Creation of an Accurate Algorithm to Detect Snellen Best Documented Visual Acuity from Ophthalmology Electronic Health Record Notes.
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DOI：
10.2196/medinform.4732
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发表时间：
2016-05-04
影响因子：
3.2
通讯作者：
Bryar, Paul J
中科院分区：
文献类型：
--
作者：
Mbagwu, Michael;French, Dustin D;Bryar, Paul J
背景：视敏度是眼科用来衡量患者视力的主要指标。单次患者就诊时，单眼的视力可能以多种方式记录（例如，Snellen vs. Jager单位vs.字体打印大小），并记录远视或近视。在单个患者就诊中捕获每只眼睛的最佳记录视力（BDVA）是使电子眼科临床记录在研究中有用的重要步骤。目的：目前，从电子健康记录（EHR）笔记中以有效和准确的方式获取BDVA的方法有限。我们开发了一种算法，从电子眼科临床记录中的定义视场中检测左右眼的BDVA。方法：我们设计了一种算法，从295218份有视力数据的眼科临床记录中确定的视场中检测BDVA。研究人员确定了大约5668种独特的反应，并开发了一种算法，将所有独特的反应映射到一个结构化的Snellen视觉灵敏度列表。结果：在研究期间，共有295218份眼科临床记录记录了视力。该算法识别每只眼睛定义的视力部分中的所有视力，并返回每只眼睛的单个BDVA。对100例随机患者记录的临床图表回顾显示，从这些记录中检测BDVA的准确率为99%，观察到的误差为1%。结论：我们的算法成功地从眼科临床记录中捕获了记录最好的Snellen距离视力，并将各种输入转换为结构化的Snellen等效列表。据我们所知，我们的工作代表了从大量电子眼科笔记中准确捕获视力的第一次尝试。使用这种算法可以使研究小组对以患者为中心的结果评估视力感兴趣。本研究使用的所有代码目前都可获得，并将在https://phekb.org上在线提供。
BACKGROUND: Visual acuity is the primary measure used in ophthalmology to determine how well a patient can see. Visual acuity for a single eye may be recorded in multiple ways for a single patient visit (eg, Snellen vs. Jager units vs. font print size), and be recorded for either distance or near vision. Capturing the best documented visual acuity (BDVA) of each eye in an individual patient visit is an important step for making electronic ophthalmology clinical notes useful in research.OBJECTIVE: Currently, there is limited methodology for capturing BDVA in an efficient and accurate manner from electronic health record (EHR) notes. We developed an algorithm to detect BDVA for right and left eyes from defined fields within electronic ophthalmology clinical notes.METHODS: We designed an algorithm to detect the BDVA from defined fields within 295,218 ophthalmology clinical notes with visual acuity data present. About 5668 unique responses were identified and an algorithm was developed to map all of the unique responses to a structured list of Snellen visual acuities.RESULTS: Visual acuity was captured from a total of 295,218 ophthalmology clinical notes during the study dates. The algorithm identified all visual acuities in the defined visual acuity section for each eye and returned a single BDVA for each eye. A clinician chart review of 100 random patient notes showed a 99% accuracy detecting BDVA from these records and 1% observed error.CONCLUSIONS: Our algorithm successfully captures best documented Snellen distance visual acuity from ophthalmology clinical notes and transforms a variety of inputs into a structured Snellen equivalent list. Our work, to the best of our knowledge, represents the first attempt at capturing visual acuity accurately from large numbers of electronic ophthalmology notes. Use of this algorithm can benefit research groups interested in assessing visual acuity for patient centered outcome. All codes used for this study are currently available, and will be made available online at https://phekb.org.