Using Electronic Health Record(EHR) Data to Evaluate Kidney Function Decline in Children with CKD
Caroline GluckAmy Goodwin DaviesJill R. McdonaldMitchell MaltenfortMark MitsnefesVikas R. DharnidharkaBradley P. DixonJoseph T. FlynnMichael J.G. SomersWilliam E. SmoyerAlicia M. NeuCollin A. HovingaAmy L. SkverskyThomas EißingAndreas M. KaiserStefanie BreitensteinSusan L. FurthChristopher B. ForrestMichelle Denburg
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Background: Chronic kidney disease is a life threatening disease, which is a common cause of mortality and morbidity. The chronic kidney disease patients are at high risk of developing end stage renal disease, cardiovascular complications and stroke. Therefore, we carried out this study to know the functional status of kidneys in chronic kidney disease cases and to classify the chronic kidney disease into different stages by calculating estimated glomerular filtration rate. Material and Methods: Twenty five cases of chronic kidney disease, between 25-70 years of age of either sex, admitted at R.L.Jalappa Hospital and Research Centre, Kolar, India and twenty five healthy age and gender matched controls were enrolled into the study. For calculating estimated glomerular filtration rate serum creatinine values, age, sex, race, and weight of the patients are considered. Results: The mean estimated glomerular filtration rate in cases was 22.096 and in control group 118.28(p<0.001) as per Cockcroft Gault Equation and as per Modification of Diet in Renal Disease equation in cases it was 18.176 and in controls 113.796(p<0.001). The estimated glomerular filtration rate was significantly low in cases when compared with healthy subjects. Conclusion: Estimated glomerular filtration rate better predicts the functional status of kidneys and is more accurate than serum creatinine and can be used to classify chronic kidney disease. Key words: Chronic kidney disease (CKD), Cockcroft Gault Equation (CCG), Estimated Glomerular Filtration Rate (eGFR), End Stage Renal Disease (ESRD), glomerular filtration rate (GFR), Modification of Diet in Renal Disease(MDRD), Serum creatinine.
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See related article at [www.cmajopen.ca/lookup/doi/10.9778/cmajo.20180096][1] KEY POINTS Increasing interest in use of routinely collected data for research has been paralleled by a rising interest in using electronic health record (EHR) data for health research, as such records have become more
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The chapter covers the electronic health record and electronic health record system that facilitates the use of EHR. The EHR is compared with the traditional handwritten health care record. Definition of Electronic Health Records and its association with the terminology, classification and coding is presented. The architecture of the Electronic Health Record is of strong significance as well as its attributes. Strategic approaches of designing systems supporting the use of electronic health records are depicted. A short presentation of current state of implementation and the obstacles for further implementation are given in the final part of the chapter.
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The electronic world continues to advance in the 21st century. In 2009, the American Recovery and Reinvestment Act (ARRA) and the Health Information Technology for Economic and Clinical Health (HITECH) Act were enacted; in response, hospitals and oncology physician offices have or are implementing electronic health records (EHRs). As with any new technology or process, a steep learning curve is associated with the implementation of EHRs. Often, the full impact of a sweeping, nationwide change such as EHRs is not realized for many years after implementation, and many suppositions about the usefulness and benefits of EHRs still exist. The current article focuses on the initial impact of EHRs, their role in diagnosis, and the responses of healthcare providers in patient outcomes and in research.
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A new study reports that the percentage of pediatricians using electronic health records (EHRs) has increased from 58% to 79% since 2009, when passage of the Health Information Technology for Economic and Clinical Health (HITECH) Act implemented incentives for adopting EHRs.
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We present RAM-EHR, a Retrieval AugMentation pipeline to improve clinical predictions on Electronic Health Records (EHRs). RAM-EHR first collects multiple knowledge sources, converts them into text format, and uses dense retrieval to obtain information related to medical concepts. This strategy addresses the difficulties associated with complex names for the concepts. RAM-EHR then augments the local EHR predictive model co-trained with consistency regularization to capture complementary information from patient visits and summarized knowledge. Experiments on two EHR datasets show the efficacy of RAM-EHR over previous knowledge-enhanced baselines (3.4% gain in AUROC and 7.2% gain in AUPR), emphasizing the effectiveness of the summarized knowledge from RAM-EHR for clinical prediction tasks. The code will be published at \url{https://github.com/ritaranx/RAM-EHR}.
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Early decisions during electronic health record (EHR) implementation can determine the long-term success of the EHR within organizations. Questions that should be addressed during EHR implementation are presented with an emphasis on how these questions relate to the success and usability of EHRs.
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Electronic Health Record (EHR) is an umbrella term encompassing demographics and health information of a patient from many different sources in a digital format. Deep learning has been used on EHRs ...
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