New developments in electronic health record analysis
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Health records
Electronic health record
Data extraction
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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Medical record
Presentation (obstetrics)
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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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Meaningful use
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This study will assess the efficacy and safety of Shenmai injection (SMI) for the treatment of chronic heart failure (CHF).The following electronic bibliographic databases will be searched from inception to the March 25, 2020 without language and publication time limitations: MEDLINE, PUBMED, Cochrane Library, Web of Science, Scopus, WANGFANG, Chinese Biomedical Literature Database, and China National Knowledge Infrastructure. All randomized controlled trials related to the SMI for patients with CHF will be included. All study selection, data extraction, and study quality will be carried out by 2 reviewers. Any disagreements will be solved by a third reviewer through discussion. RevMan 5.3 software will be used for data synthesis and data analysis.This study will summarize the present evidence of SMI for the treatment of patients with CHF.The findings of this study will determine whether SMI is effective and safety for the treatment of CHF or not.INPLASY202050029.
Data extraction
Web of science
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Health records
Electronic health record
Depression
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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.
Electronic health record
Health records
Meaningful use
Health information technology
Electronic Records
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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 ...
Health records
Electronic health record
Demographics
Code (set theory)
Digital Health
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Health Information Exchange
Electronic health record
Health records
Protected health information
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Health records
Electronic health record
Data extraction
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Background: Geriatric syndromes in older adults are associated with adverse outcomes. However, despite being reported in clinical notes, these syndromes are often poorly captured by diagnostic codes in the structured fields of electronic health records (EHRs) or administrative records. Objective: We aim to automatically determine if a patient has any geriatric syndromes by mining the free text of associated EHR clinical notes. We assessed which statistical natural language processing (NLP) techniques are most effective. Methods: We applied conditional random fields (CRFs), a widely used machine learning algorithm, to identify each of 10 geriatric syndrome constructs in a clinical note. We assessed three sets of features and attributes for CRF operations: a base set, enhanced token, and contextual features. We trained the CRF on 3901 manually annotated notes from 85 patients, tuned the CRF on a validation set of 50 patients, and evaluated it on 50 held-out test patients. These notes were from a group of US Medicare patients over 65 years of age enrolled in a Medicare Advantage Health Maintenance Organization and cared for by a large group practice in Massachusetts. Results: A final feature set was formed through comprehensive feature ablation experiments. The final CRF model performed well at patient-level determination (macroaverage F1=0.834, microaverage F1=0.851); however, performance varied by construct. For example, at phrase-partial evaluation, the CRF model worked well on constructs such as absence of fecal control (F1=0.857) and vision impairment (F1=0.798) but poorly on malnutrition (F1=0.155), weight loss (F1=0.394), and severe urinary control issues (F1=0.532). Errors were primarily due to previously unobserved words (ie, out-of-vocabulary) and a lack of context. Conclusions: This study shows that statistical NLP can be used to identify geriatric syndromes from EHR-extracted clinical notes. This creates new opportunities to identify patients with geriatric syndromes and study their health outcomes.
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