Clinical Informatics Solutions and Services, Philips Research North America, 2 Canal Park, Cambridge, MA, 02141, USA.
Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA.
J Digit Imaging. 2020 Feb;33(1):121-130. doi: 10.1007/s10278-019-00260-w.
Radiology reports often contain follow-up imaging recommendations. Failure to comply with these recommendations in a timely manner can lead to delayed treatment, poor patient outcomes, complications, unnecessary testing, lost revenue, and legal liability. The objective of this study was to develop a scalable approach to automatically identify the completion of a follow-up imaging study recommended by a radiologist in a preceding report. We selected imaging-reports containing 559 follow-up imaging recommendations and all subsequent reports from a multi-hospital academic practice. Three radiologists identified appropriate follow-up examinations among the subsequent reports for the same patient, if any, to establish a ground-truth dataset. We then trained an Extremely Randomized Trees that uses recommendation attributes, study meta-data and text similarity of the radiology reports to determine the most likely follow-up examination for a preceding recommendation. Pairwise inter-annotator F-score ranged from 0.853 to 0.868; the corresponding F-score of the classifier in identifying follow-up exams was 0.807. Our study describes a methodology to automatically determine the most likely follow-up exam after a follow-up imaging recommendation. The accuracy of the algorithm suggests that automated methods can be integrated into a follow-up management application to improve adherence to follow-up imaging recommendations. Radiology administrators could use such a system to monitor follow-up compliance rates and proactively send reminders to primary care providers and/or patients to improve adherence.
放射科报告通常包含随访影像建议。不及时遵守这些建议可能导致治疗延误、患者预后不良、并发症、不必要的检查、收入损失和法律责任。本研究的目的是开发一种可扩展的方法，自动识别放射科医生在前一份报告中推荐的随访影像研究的完成情况。我们选择了来自多医院学术实践的包含 559 项随访影像建议的影像报告和所有后续报告。三位放射科医生在随后的报告中为同一患者确定了合适的随访检查（如果有），以建立一个真实数据集。然后，我们训练了一个极端随机树，该树使用推荐属性、研究元数据和放射科报告的文本相似度来确定前一个推荐的最可能的随访检查。两两注释者 F 分数范围为 0.853 至 0.868；分类器识别随访检查的 F 分数为 0.807。我们的研究描述了一种自动确定随访影像建议后最可能的随访检查的方法。该算法的准确性表明，自动化方法可以集成到随访管理应用程序中，以提高对随访影像建议的遵守率。放射科管理人员可以使用这样的系统来监测随访的遵守率，并主动向初级保健提供者和/或患者发送提醒，以提高遵守率。
