Sheng-Feng Sung1, Kuanchin Chen2, Darren Philbert Wu3, Ling-Chien Hung3, Yu-Hsiang Su3, Ya-Han Hu4. 1. Division of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi City, Taiwan; Department of Information Management and Institute of Healthcare Information Management, National Chung Cheng University, Chiayi County, Taiwan; Department of Nursing, Min-Hwei Junior College of Health Care Management, Tainan, Taiwan. 2. Department of Business Information Systems, Western Michigan University, Kalamazoo, MI, United States. 3. Division of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi City, Taiwan. 4. Department of Information Management and Institute of Healthcare Information Management, National Chung Cheng University, Chiayi County, Taiwan. Electronic address: yahan.hu@mis.ccu.edu.tw.
Abstract
OBJECTIVE: To reduce errors in determining eligibility for intravenous thrombolytic therapy (IVT) in stroke patients through use of an enhanced task-specific electronic medical record (EMR) interface powered by natural language processing (NLP) techniques. MATERIALS AND METHODS: The information processing algorithm utilized MetaMap to extract medical concepts from IVT eligibility criteria and expanded the concepts using the Unified Medical Language System Metathesaurus. Concepts identified from clinical notes by MetaMap were compared to those from IVT eligibility criteria. The task-specific EMR interface displays IVT-relevant information by highlighting phrases that contain matched concepts. Clinical usability was assessed with clinicians staffing the acute stroke team by comparing user performance while using the task-specific and the current EMR interfaces. RESULTS: The algorithm identified IVT-relevant concepts with micro-averaged precisions, recalls, and F1 measures of 0.998, 0.812, and 0.895 at the phrase level and of 1, 0.972, and 0.986 at the document level. Users using the task-specific interface achieved a higher accuracy score than those using the current interface (91% versus 80%, p = 0.016) in assessing the IVT eligibility criteria. The completion time between the interfaces was statistically similar (2.46 min versus 1.70 min, p = 0.754). DISCUSSION: Although the information processing algorithm had room for improvement, the task-specific EMR interface significantly reduced errors in assessing IVT eligibility criteria. CONCLUSION: The study findings provide evidence to support an NLP enhanced EMR system to facilitate IVT decision-making by presenting meaningful and timely information to clinicians, thereby offering a new avenue for improvements in acute stroke care.
OBJECTIVE: To reduce errors in determining eligibility for intravenous thrombolytic therapy (IVT) in strokepatients through use of an enhanced task-specific electronic medical record (EMR) interface powered by natural language processing (NLP) techniques. MATERIALS AND METHODS: The information processing algorithm utilized MetaMap to extract medical concepts from IVT eligibility criteria and expanded the concepts using the Unified Medical Language System Metathesaurus. Concepts identified from clinical notes by MetaMap were compared to those from IVT eligibility criteria. The task-specific EMR interface displays IVT-relevant information by highlighting phrases that contain matched concepts. Clinical usability was assessed with clinicians staffing the acute stroke team by comparing user performance while using the task-specific and the current EMR interfaces. RESULTS: The algorithm identified IVT-relevant concepts with micro-averaged precisions, recalls, and F1 measures of 0.998, 0.812, and 0.895 at the phrase level and of 1, 0.972, and 0.986 at the document level. Users using the task-specific interface achieved a higher accuracy score than those using the current interface (91% versus 80%, p = 0.016) in assessing the IVT eligibility criteria. The completion time between the interfaces was statistically similar (2.46 min versus 1.70 min, p = 0.754). DISCUSSION: Although the information processing algorithm had room for improvement, the task-specific EMR interface significantly reduced errors in assessing IVT eligibility criteria. CONCLUSION: The study findings provide evidence to support an NLP enhanced EMR system to facilitate IVT decision-making by presenting meaningful and timely information to clinicians, thereby offering a new avenue for improvements in acute stroke care.
Authors: Cody C Wyles; Meagan E Tibbo; Sunyang Fu; Yanshan Wang; Sunghwan Sohn; Walter K Kremers; Daniel J Berry; David G Lewallen; Hilal Maradit-Kremers Journal: J Bone Joint Surg Am Date: 2019-11-06 Impact factor: 5.284