LATTE: A knowledge-based method to normalize various expressions of laboratory test results in free text of Chinese electronic health records

Abstract Accurate display and interpretation of clinical laboratory test results is essential for safe and effective diagnosis and treatment. In an attempt to ascertain how well current electronic health records (EHRs) facilitated these processes, we evaluated the graphical displays of laboratory test results in eight EHRs using objective criteria for optimal graphs based on literature and expert opinion. None of the EHRs met all 11 criteria; the magnitude of deficiency ranged from one EHR meeting 10 of 11 criteria to three EHRs meeting only 5 of 11 criteria. One criterion (i.e., the EHR has a graph with y-axis labels that display both the name of the measured variable and the units of measure) was absent from all EHRs. One EHR system graphed results in reverse chronological order. One EHR system plotted data collected at unequally-spaced points in time using equally-spaced data points, which had the effect of erroneously depicting the visual slope perception between data points. This deficiency could have a significant, negative impact on patient safety. Only two EHR systems allowed users to see, hover-over, or click on a data point to see the precise values of the x–y coordinates. Our study suggests that many current EHR-generated graphs do not meet evidence-based criteria aimed at improving laboratory data comprehension.

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An individualized risk prediction model for new-onset, progression and regression of chronic kidney disease in a retrospective cohort of patients with type 2 diabetes under primary care in Hong Kong

10.1101/2020.01.28.20019182 ◽

2020 ◽

Author(s):

Lin Yang ◽

Tsun Kit Chu ◽

Jinxiao Lian ◽

Cheuk Wai Lo ◽

Shi Zhao ◽

...

Keyword(s):

Chronic Kidney Disease ◽

Kidney Disease ◽

Electronic Health Records ◽

Laboratory Test ◽

List Type ◽

Test Results ◽

Health Records ◽

Laboratory Test Results ◽

Electronic Health ◽

New Onset

AbstractObjectivesThis study is aimed to develop and validate a prediction model for multi-state transitions across different stages of chronic kidney disease in patients with type 2 diabetes mellitus under primary care.SettingWe retrieved the anonymized electronic health records of a population based retrospective cohort in Hong Kong.ParticipantsA total of 26,197 patients were included in the analysis.Primary and secondary outcome measuresThe new-onset, progression, and regression of chronic kidney disease were defined by the transitions of four stages that were classified by combining glomerular filtration rate and urine albumin-to-creatinine ratio. We applied a multi-scale multi-state Poisson regression model to estimate the rates of the stage transitions by integrating the baseline demographic characteristics, routine laboratory test results and clinical data from electronic health records.ResultsDuring the mean follow-up time of 1.7 years, there were 2,935 patients newly diagnosed with chronic kidney disease, 1,443 progressed to the next stage and 1,971 regressed into an earlier stage. The models achieved the best performance in predicting the new-onset and progression with the predictors of sex, age, body mass index, systolic blood pressure, diastolic blood pressure, serum creatinine, HbA1c, total cholesterol, LDL, HDL, triglycerides and drug prescriptions.ConclusionsThis study demonstrated that individual risks of new-onset and progression of chronic kidney disease can be predicted from the routine physical and laboratory test results. The individualized prediction curves developed from this study could potentially be applied to routine clinical practices, to facilitate clinical decision making, risk communications with patients and early interventions.Article summaryStrengths of this studyEarly predictions for chronic kidney disease progression and timely intervention is critical for clinical management of patients with diabetes.We successfully developed a multi-scale multi-state Poisson regression models that achieved the satisfactory performance in predicting the new-onset and progression of chronic kidney diseases.The model incorporates the predictors of demographic characteristics, routine laboratory test results and clinical data from electronic health records.The individualized prediction curves could potentially be applied to facilitate clinical decision making, risk communications with patients and early interventions of CKD progression.Limitations of this studyThe cohort has a relatively short follow-up period and the retrospective study design might suffer from report bias and selection bias.

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A Framework for Systematic Assessment of Clinical Trial Population Representativeness Using Electronic Health Records Data

Applied Clinical Informatics ◽

10.1055/s-0041-1733846 ◽

2021 ◽

Vol 12 (04) ◽

pp. 816-825

Author(s):

Yingcheng Sun ◽

Alex Butler ◽

Ibrahim Diallo ◽

Jae Hyun Kim ◽

Casey Ta ◽

...

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Electronic Health Records ◽

The United States ◽

Design Stage ◽

Common Data Model ◽

Free Text ◽

Eligibility Criteria ◽

Health Records ◽

Electronic Health

Abstract Background Clinical trials are the gold standard for generating robust medical evidence, but clinical trial results often raise generalizability concerns, which can be attributed to the lack of population representativeness. The electronic health records (EHRs) data are useful for estimating the population representativeness of clinical trial study population. Objectives This research aims to estimate the population representativeness of clinical trials systematically using EHR data during the early design stage. Methods We present an end-to-end analytical framework for transforming free-text clinical trial eligibility criteria into executable database queries conformant with the Observational Medical Outcomes Partnership Common Data Model and for systematically quantifying the population representativeness for each clinical trial. Results We calculated the population representativeness of 782 novel coronavirus disease 2019 (COVID-19) trials and 3,827 type 2 diabetes mellitus (T2DM) trials in the United States respectively using this framework. With the use of overly restrictive eligibility criteria, 85.7% of the COVID-19 trials and 30.1% of T2DM trials had poor population representativeness. Conclusion This research demonstrates the potential of using the EHR data to assess the clinical trials population representativeness, providing data-driven metrics to inform the selection and optimization of eligibility criteria.

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Validity of acute cardiovascular outcome diagnoses in European electronic health records: a systematic review protocol

BMJ Open ◽

10.1136/bmjopen-2019-031373 ◽

2019 ◽

Vol 9 (10) ◽

pp. e031373 ◽

Cited By ~ 1

Author(s):

Jennifer Anne Davidson ◽

Amitava Banerjee ◽

Rutendo Muzambi ◽

Liam Smeeth ◽

Charlotte Warren-Gash

Keyword(s):

Systematic Review ◽

Electronic Health Records ◽

Predictive Value ◽

Grey Literature ◽

Cochrane Library ◽

Free Text ◽

Health Records ◽

Coronary Syndrome ◽

Validation Measure ◽

Electronic Health

IntroductionCardiovascular diseases (CVDs) are among the leading causes of death globally. Electronic health records (EHRs) provide a rich data source for research on CVD risk factors, treatments and outcomes. Researchers must be confident in the validity of diagnoses in EHRs, particularly when diagnosis definitions and use of EHRs change over time. Our systematic review provides an up-to-date appraisal of the validity of stroke, acute coronary syndrome (ACS) and heart failure (HF) diagnoses in European primary and secondary care EHRs.Methods and analysisWe will systematically review the published and grey literature to identify studies validating diagnoses of stroke, ACS and HF in European EHRs. MEDLINE, EMBASE, SCOPUS, Web of Science, Cochrane Library, OpenGrey and EThOS will be searched from the dates of inception to April 2019. A prespecified search strategy of subject headings and free-text terms in the title and abstract will be used. Two reviewers will independently screen titles and abstracts to identify eligible studies, followed by full-text review. We require studies to compare clinical codes with a suitable reference standard. Additionally, at least one validation measure (sensitivity, specificity, positive predictive value or negative predictive value) or raw data, for the calculation of a validation measure, is necessary. We will then extract data from the eligible studies using standardised tables and assess risk of bias in individual studies using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Data will be synthesised into a narrative format and heterogeneity assessed. Meta-analysis will be considered when a sufficient number of homogeneous studies are available. The overall quality of evidence will be assessed using the Grading of Recommendations, Assessment, Development and Evaluation tool.Ethics and disseminationThis is a systematic review, so it does not require ethical approval. Our results will be submitted for peer-review publication.PROSPERO registration numberCRD42019123898

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De-identifying Free Text of Japanese Dummy Electronic Health Records

10.18653/v1/w18-5608 ◽

2018 ◽

Author(s):

Kohei Kajiyama ◽

Hiromasa Horiguchi ◽

Takashi Okumura ◽

Mizuki Morita ◽

Yoshinobu Kano

Keyword(s):

Electronic Health Records ◽

Free Text ◽

Health Records ◽

Electronic Health

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Utilizing Acquired Healthcare Knowledge, Based on Using Electronic Health Records

Lecture Notes in Computer Science - Information Quality in e-Health ◽

10.1007/978-3-642-25364-5_30 ◽

2011 ◽

pp. 429-439 ◽

Cited By ~ 2

Author(s):

Sara Nasiri ◽

Mohammad Mehdi Sepehri ◽

Marjan Khobreh

Keyword(s):

Electronic Health Records ◽

Health Records ◽

Knowledge Based ◽

Electronic Health

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Abstract MP21: Feasibility of Electronic Health Records-based community surveillance of cardiovascular disease: Findings from the Atherosclerosis Risk in Communities Study.

Circulation ◽

10.1161/circ.137.suppl_1.mp21 ◽

2018 ◽

Vol 137 (suppl_1) ◽

Author(s):

Brittany M Bogle ◽

Wayne D Rosamond ◽

Aaron R Folsom ◽

Paul Sorlie ◽

Elsayed Z Soliman ◽

...

Keyword(s):

Cardiovascular Disease ◽

Electronic Health Records ◽

Cardiac Biomarkers ◽

Free Text ◽

Health Records ◽

Efficient System ◽

Atherosclerosis Risk In Communities ◽

Atherosclerosis Risk ◽

Electronic Health ◽

Aric Study

Background: Accurate community surveillance of cardiovascular disease requires hospital record abstraction, which is typically a manual process. The costly and time-intensive nature of manual abstraction precludes its use on a regional or national scale in the US. Whether an efficient system can accurately reproduce traditional community surveillance methods by processing electronic health records (EHRs) has not been established. Objective: We sought to develop and test an EHR-based system to reproduce abstraction and classification procedures for acute myocardial infarction (MI) as defined by the Atherosclerosis Risk in Communities (ARIC) Study. Methods: Records from hospitalizations in 2014 within ARIC community surveillance areas were sampled using a broad set of ICD discharge codes likely to harbor MI. These records were manually abstracted by ARIC study personnel and used to classify MI according to ARIC protocols. We requested EHRs in a unified data structure for the same hospitalizations at 6 hospitals and built programs to convert free text and structured data into the ARIC criteria elements necessary for MI classification. Per ARIC protocol, MI was classified based on cardiac biomarkers, cardiac pain, and Minnesota-coded electrocardiogram abnormalities. We compared MI classified from manually abstracted data to (1) EHR-based classification and (2) final ICD-9 coded discharge diagnoses (410-414). Results: These preliminary results are based on hospitalizations from 1 hospital. Of 684 hospitalizations, 355 qualified for full manual abstraction; 83 (23%) of these were classified as definite MI and 78 (22%) as probable MI. Our EHR-based abstraction is sensitive (>75%) and highly specific (>83%) in classifying ARIC-defined definite MI and definite or probable MI (Table). Conclusions: Our results support the potential of a process to extract comprehensive sets of data elements from EHR from different hospitals, with completeness and accuracy sufficient for a standardized definition of hospitalized MI.

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Incorporating natural language processing to improve classification of axial spondyloarthritis using electronic health records

Rheumatology ◽

10.1093/rheumatology/kez375 ◽

2019 ◽

Vol 59 (5) ◽

pp. 1059-1065 ◽

Cited By ~ 1

Author(s):

Sizheng Steven Zhao ◽

Chuan Hong ◽

Tianrun Cai ◽

Chang Xu ◽

Jie Huang ◽

...

Keyword(s):

Electronic Health Records ◽

Predictive Value ◽

Area Under The Curve ◽

Free Text ◽

Text Data ◽

Health Records ◽

Disease Concepts ◽

Icd Codes ◽

Electronic Health

Abstract Objectives To develop classification algorithms that accurately identify axial SpA (axSpA) patients in electronic health records, and compare the performance of algorithms incorporating free-text data against approaches using only International Classification of Diseases (ICD) codes. Methods An enriched cohort of 7853 eligible patients was created from electronic health records of two large hospitals using automated searches (⩾1 ICD codes combined with simple text searches). Key disease concepts from free-text data were extracted using NLP and combined with ICD codes to develop algorithms. We created both supervised regression-based algorithms—on a training set of 127 axSpA cases and 423 non-cases—and unsupervised algorithms to identify patients with high probability of having axSpA from the enriched cohort. Their performance was compared against classifications using ICD codes only. Results NLP extracted four disease concepts of high predictive value: ankylosing spondylitis, sacroiliitis, HLA-B27 and spondylitis. The unsupervised algorithm, incorporating both the NLP concept and ICD code for AS, identified the greatest number of patients. By setting the probability threshold to attain 80% positive predictive value, it identified 1509 axSpA patients (mean age 53 years, 71% male). Sensitivity was 0.78, specificity 0.94 and area under the curve 0.93. The two supervised algorithms performed similarly but identified fewer patients. All three outperformed traditional approaches using ICD codes alone (area under the curve 0.80–0.87). Conclusion Algorithms incorporating free-text data can accurately identify axSpA patients in electronic health records. Large cohorts identified using these novel methods offer exciting opportunities for future clinical research.

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