Electronic Medical Record Search Engine (EMERSE): An Information Retrieval Tool for Supporting Cancer Research

PURPOSE The Electronic Medical Record Search Engine (EMERSE) is a software tool built to aid research spanning cohort discovery, population health, and data abstraction for clinical trials. EMERSE is now live at three academic medical centers, with additional sites currently working on implementation. In this report, we describe how EMERSE has been used to support cancer research based on a variety of metrics. METHODS We identified peer-reviewed publications that used EMERSE through online searches as well as through direct e-mails to users based on audit logs. These logs were also used to summarize use at each of the three sites. Search terms for two of the sites were characterized using the natural language processing tool MetaMap to determine to which semantic types the terms could be mapped. RESULTS We identified a total of 326 peer-reviewed publications that used EMERSE through August 2019, although this is likely an underestimation of the true total based on the use log analysis. Oncology-related research comprised nearly one third (n = 105; 32.2%) of all research output. The use logs showed that EMERSE had been used by multiple people at each site (nearly 3,500 across all three) who had collectively logged into the system > 100,000 times. Many user-entered search queries could not be mapped to a semantic type, but the most common semantic type for terms that did match was “disease or syndrome,” followed by “pharmacologic substance.” CONCLUSION EMERSE has been shown to be a valuable tool for supporting cancer research. It has been successfully deployed at other sites, despite some implementation challenges unique to each deployment environment.

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Natural Language Processing Performance for the Identification of Venous Thromboembolism in an Integrated Healthcare System

Clinical and Applied Thrombosis/Hemostasis ◽

10.1177/10760296211013108 ◽

2021 ◽

Vol 27 ◽

pp. 107602962110131

Author(s):

Bela Woller ◽

Austin Daw ◽

Valerie Aston ◽

Jim Lloyd ◽

Greg Snow ◽

...

Keyword(s):

Natural Language Processing ◽

Venous Thromboembolism ◽

Natural Language ◽

Medical Record ◽

Electronic Medical Record ◽

Ct Angiography ◽

Language Processing ◽

Predictive Value ◽

Chart Review ◽

Imaging Studies

Real-time identification of venous thromboembolism (VTE), defined as deep vein thrombosis (DVT) and pulmonary embolism (PE), can inform a healthcare organization’s understanding of these events and be used to improve care. In a former publication, we reported the performance of an electronic medical record (EMR) interrogation tool that employs natural language processing (NLP) of imaging studies for the diagnosis of venous thromboembolism. Because we transitioned from the legacy electronic medical record to the Cerner product, iCentra, we now report the operating characteristics of the NLP EMR interrogation tool in the new EMR environment. Two hundred randomly selected patient encounters for which the imaging report assessed by NLP that revealed VTE was present were reviewed. These included one hundred imaging studies for which PE was identified. These included computed tomography pulmonary angiography—CTPA, ventilation perfusion—V/Q scan, and CT angiography of the chest/ abdomen/pelvis. One hundred randomly selected comprehensive ultrasound (CUS) that identified DVT were also obtained. For comparison, one hundred patient encounters in which PE was suspected and imaging was negative for PE (CTPA or V/Q) and 100 cases of suspected DVT with negative CUS as reported by NLP were also selected. Manual chart review of the 400 charts was performed and we report the sensitivity, specificity, positive and negative predictive values of NLP compared with manual chart review. NLP and manual review agreed on the presence of PE in 99 of 100 cases, the presence of DVT in 96 of 100 cases, the absence of PE in 99 of 100 cases and the absence of DVT in all 100 cases. When compared with manual chart review, NLP interrogation of CUS, CTPA, CT angiography of the chest, and V/Q scan yielded a sensitivity = 93.3%, specificity = 99.6%, positive predictive value = 97.1%, and negative predictive value = 99%.

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THE USE OF NATURAL LANGUAGE PROCESSING TOOLS AND THE INTEGRATED ELECTRONIC MEDICAL RECORD (EMR) TO IDENTIFY PATIENTS AT HIGH-RISK FOR TRANSTHYRETIN CARDIAC AMYLOIDOSIS

Journal of the American College of Cardiology ◽

10.1016/s0735-1097(21)02198-7 ◽

2021 ◽

Vol 77 (18) ◽

pp. 839

Author(s):

Trejeeve Martyn ◽

Joshua Saef ◽

Jerry Estep ◽

Patrick Collier ◽

Deborah Kwon ◽

...

Keyword(s):

Natural Language Processing ◽

High Risk ◽

Natural Language ◽

Medical Record ◽

Electronic Medical Record ◽

Language Processing ◽

Cardiac Amyloidosis

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Discrepancies in automated, electronic medical record-based CHA2DS2-VASc scores and clinician assessment for atrial fibrillation patients

10.1101/2020.12.23.20248775 ◽

2020 ◽

Author(s):

Aubrey E. Jones ◽

Zameer Abedin ◽

Olesya Ilkun ◽

Rebeka Mukherjee ◽

Mingyuan Zhang ◽

...

Keyword(s):

Atrial Fibrillation ◽

Treatment Group ◽

Medical Record ◽

Electronic Medical Record ◽

Language Processing ◽

Significant Proportion ◽

Left Ventricular ◽

Left Ventricular Ejection ◽

Full Cohort ◽

Ventricular Ejection Fraction

AbstractBackgroundClinical decision support tools for atrial fibrillation (AF) should include CHA2DS2- VASc scores to guide oral anticoagulant (OAC) treatment.ObjectiveWe compared automated, electronic medical record (EMR) generated CHA2DS2- VASc scores to clinician-documented scores, and report the resulting proportions of patients in the OAC treatment group.MethodsPatients were included if they had both a clinician documented and EMR-generated CHA2DS2-VASc score on the same day. EMR scores were based on billing codes, left ventricular ejection fraction from echocardiograms, and demographics; documented scores were identified using natural language processing. Patients were deemed “re-classified” if the EMR score was ≥2 but the documented score was <2, and vice versa. For the overall cohort and subgroups (sex and age group), we compared mean scores using paired t-tests and re-classification rates using chi-squared tests.ResultsAmong 5,767 patients, the mean scores were higher using EMR compared to documented scores (4.05 [SD 2.1] versus 3.13 [SD 1.8]; p<0.01) for the full cohort, and all subgroups (p<0.01 for all comparisons). If EMR scores were used to determine OAC treatment instead of documented scores, 8.3% (n=479, p<0.01) of patients would be re-classified, with 7.2% moving into and 1.1% moving out of the treatment group. Among 2,322 women, 4.7% (n=109, p<0.01) would be re-classified, with 4.1% into and 0.7% out of the treatment group. Among 3,445 men, 10.7% (n=370, p<0.01) would be re-classified, with 9.2% into and 1.5% out of the treatment group. Among 2,060 patients <65 years old, 18.1% (n=372, p<0.01) would be re-classified, with 15.8% into and 2.3% out of the treatment group. Among 1,877 patients 65-74 years old, 5.4% (n=101, p<0.01) would be re-classified, with 4.4% into and 1.0% out of the treatment group. Among 1,830 patients ≥75 years old, <1% would move into to the treatment group and none would move out of the treatment group.ConclusionsEMR-based CHA2DS2-VASc scores were, on average, almost a full point higher than the clinician-documented scores. Using EMR scores in lieu of documented scores would result in a significant proportion of patients moving into the treatment group, with the highest re-classifications rates in men and patients <65 years old.

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Automated Identification of Postoperative Complications Within an Electronic Medical Record Using Natural Language Processing

JAMA ◽

10.1001/jama.2011.1204 ◽

2011 ◽

Vol 306 (8) ◽

Cited By ~ 137

Author(s):

Harvey J. Murff ◽

Fern FitzHenry ◽

Michael E. Matheny ◽

Nancy Gentry ◽

Kristen L. Kotter ◽

...

Keyword(s):

Natural Language Processing ◽

Postoperative Complications ◽

Natural Language ◽

Medical Record ◽

Electronic Medical Record ◽

Language Processing ◽

Automated Identification

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Electronic Medical Record Implementation Challenges for the National Health System in Greece

International Journal of Reliable and Quality E-Healthcare ◽

10.4018/ijrqeh.2018010102 ◽

2018 ◽

Vol 7 (1) ◽

pp. 16-30 ◽

Cited By ~ 2

Author(s):

Dimitrios G. Katehakis

Keyword(s):

Health System ◽

Medical Record ◽

Electronic Medical Record ◽

National Health ◽

National Health System ◽

Public Hospitals ◽

Medical Decision ◽

Significant Information ◽

Implementation Challenges ◽

Clinically Significant

The purpose of this work is to expose challenges related to the implementation of quality electronic medical record (EMR) systems in public hospitals in Greece, a country where the national health system (NHS) has already acquired electronic medical records (EMRs). The level of EMR implementation, together with organizational maturity at a hospital level, are explored. What is discovered is that there are different adoption levels, not recorded in a systematic manner. The majority of physicians are either reluctant to implement EMRs or do not know options available to them. Implications include not continuous flow of events, cut off of critical information, lower quality of health services, patients not empowered to carry with them clinically significant information, unnecessary repetition of medical procedures and higher costs. It is concluded that focus should be paid on enabling the use of quality, interoperable and secure EMRs to better support medical decision, in an effort to improve the health of the population and to better control costs.

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Deep Cardiovascular Disease Prediction with Risk Factors Powered Bi-Attention

10.21203/rs.3.rs-31061/v1 ◽

2020 ◽

Author(s):

Yanlong Qiu ◽

Zhichang Zhang ◽

Xiaohui Qin ◽

Shengxin Tao

Keyword(s):

Risk Factors ◽

Cardiovascular Disease ◽

Medical Record ◽

Electronic Medical Record ◽

Language Processing ◽

Attention Mechanism ◽

Entity Recognition ◽

Disease Prediction ◽

Human Beings ◽

Vector Coupling

Abstract Background Cardiovascular disease (CVD), as a chronic disease, has been perplexing human beings and is one of the serious diseases endangering life and health. Therefore, using the electronic medical record information of patients to automatically predict CVD has important application value in intelligent auxiliary diagnosis and treatment, and is a hot issue in intelligent medical research. In recent years, attention mechanism has been successfully extended to various tasks of natural language processing. Typically, these methods use attention to focus on a small part of the context and summarize it with a fixed-size vector, coupling attention in time, and/or often forming a uni-directional attention. Methods In this paper, we propose a CVD risk factors powered bi-directional attention (RFPBiA) network, which is a multi-stage hierarchical process, representing information fusion at different granularity levels, and uses the bi-directional attention to obtain the text representation of risk factors without early aggregation. Results The experimental results show that the proposed method can obviously improve the performance of CVD prediction, and the F-score reaches 0.9424, which is better than the existing related methods. Conclusions We propose to extract the risk factors leading to CVD by using the existing mature entity recognition technology, which provides a new idea for disease prediction tasks. Moreover, the memory- less attention mechanism in both directions in our proposed prediction model of RFPBiA can fuse the character sequence and the risk factors contained in the electronic medical record text to predict CVD.

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