Medical Data Breaches: What the Reported Data Illustrates, and Implications for Transitioning to Electronic Medical Records

2013 ◽  
Vol 8 (1) ◽  
pp. 61-79
Author(s):  
Akshat Kapoor ◽  
Derek L. Nazareth
Keyword(s):  
Electronic Medical Records ◽  
Medical Records ◽  
Medical Data ◽  
Data Breaches ◽  
Reported Data

scholarly journals Outcome of COVID-19 and Match-Population Analysis with Compassionate Use of Remdesivir

2020 ◽  
Vol 1 ◽  
pp. 2-4
Author(s):  
Olga Vasylyeva ◽  
Tara Chen ◽  
John Hanna
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Population Analysis ◽  
Exclusion Criterion ◽  
Invasive Ventilation ◽  
Inclusion Criteria ◽  
Compassionate Use ◽  
Reported Data

Abstract: Objectives:  To analyze treatment outcomes for patients with COVID-19 with and without compassionate use of Remdesivir. Methods:  A retrospective review of electronic medical records for patients who did not receive Remdesivir due to unavailability. Match-population analysis based on inclusion criteria for compassionate use Remdesivir of the patient population who received Remdesivir as reported in literature and patients without Remdesivir.  Results: Sixty-six percent of patients met the criteria for compassionate use Remdesivir, 41% required intensive care unit admission, 20% invasive ventilation, and 10% died. The median time of hospitalization for survivors was eight days.  In the separate group of patients who did not meet the criteria for compassion use Remdesivir, mortality among patients with CrCl > 30 ml min, an exclusion criterion, was significantly higher as compared with patients with CrCl < 30 ml min. Conclusion: When compared with previously reported data from patients who received compassionate use Remdesivir, our population had notably fewer patients requiring invasive ventilation. 

scholarly journals An Unsupervised Approach to Structuring and Analyzing Repetitive Semantic Structures in Free Text of Electronic Medical Records

2022 ◽  
Vol 12 (1) ◽  
pp. 25
Author(s):  
Varvara Koshman ◽  
Anastasia Funkner ◽  
Sergey Kovalchuk
Keyword(s):  
Electronic Medical Records ◽  
Medical Records ◽  
Medical Data ◽  
Validation Dataset ◽  
Free Text ◽  
Automatic Annotation ◽  
Text Data ◽  
Data Annotation ◽  
Unsupervised Approach ◽  
Labeling Method

Electronic medical records (EMRs) include many valuable data about patients, which is, however, unstructured. Therefore, there is a lack of both labeled medical text data in Russian and tools for automatic annotation. As a result, today, it is hardly feasible for researchers to utilize text data of EMRs in training machine learning models in the biomedical domain. We present an unsupervised approach to medical data annotation. Syntactic trees are produced from initial sentences using morphological and syntactical analyses. In retrieved trees, similar subtrees are grouped using Node2Vec and Word2Vec and labeled using domain vocabularies and Wikidata categories. The usage of Wikidata categories increased the fraction of labeled sentences 5.5 times compared to labeling with domain vocabularies only. We show on a validation dataset that the proposed labeling method generates meaningful labels correctly for 92.7% of groups. Annotation with domain vocabularies and Wikidata categories covered more than 82% of sentences of the corpus, extended with timestamp and event labels 97% of sentences got covered. The obtained method can be used to label EMRs in Russian automatically. Additionally, the proposed methodology can be applied to other languages, which lack resources for automatic labeling and domain vocabulary.

scholarly journals Enhancing Electronic Laboratory Reporting by Incorporating Electronic Medical Records for Notifiable Disease Surveillance (Preprint)

2018 ◽  
Author(s):  
Mei-Hua Wang ◽  
Han-Kun Chen ◽  
Min-Huei Hsu ◽  
Hui-Chi Wang ◽  
Yu-Ting Yeh
Keyword(s):  
Influenza Virus ◽  
Hepatitis B Virus ◽  
Hepatitis B ◽  
Infectious Diseases ◽  
Electronic Medical Records ◽  
Sensitivity And Specificity ◽  
Medical Records ◽  
Notifiable Disease ◽  
B Virus ◽  
Reported Data

BACKGROUND Outbreaks of several serious infectious diseases have occurred in recent years. In response, to mitigate public health risks, countries worldwide have dedicated efforts to establish an information system for effective disease monitoring, risk assessment, and early warning management for international disease outbreaks. A cloud computing framework can effectively provide the required hardware resources and information access and exchange to conveniently connect information related to infectious diseases and develop a cross-system surveillance and control system for infectious diseases. OBJECTIVE The objective of our study was to develop a Hospital Automated Laboratory Reporting (HALR) system based on such a framework and evaluate its effectiveness. METHODS We collected data for 6 months and analyzed the cases reported within this period by the HALR and the Web-based Notifiable Disease Reporting (WebNDR) systems. Furthermore, system evaluation indicators were gathered, including those evaluating sensitivity and specificity. RESULTS The HALR system reported 15 pathogens and 5174 cases, and the WebNDR system reported 34 cases. In a comparison of the two systems, sensitivity was 100% and specificity varied according to the reported pathogens. In particular, the specificity for Streptococcus pneumoniae, Mycobacterium tuberculosis complex, and hepatitis C virus were 99.8%, 96.6%, and 97.4%, respectively. However, the specificity for influenza virus and hepatitis B virus were only 79.9% and 47.1%, respectively. After the reported data were integrated with patients’ diagnostic results in their electronic medical records (EMRs), the specificity for influenza virus and hepatitis B virus increased to 89.2% and 99.1%, respectively. CONCLUSIONS The HALR system can provide early reporting of specified pathogens according to test results, allowing for early detection of outbreaks and providing trends in infectious disease data. The results of this study show that the sensitivity and specificity of early disease detection can be increased by integrating the reported data in the HALR system with the cases’ clinical information (eg, diagnostic results) in EMRs, thereby enhancing the control and prevention of infectious diseases.

scholarly journals A Study Of The Security Of Electronic Medical Records Utilizing Six Knowledge Categories And Subjects Demographics

2016 ◽  
Vol 20 (3) ◽  
pp. 51-58
Author(s):  
Natalya Goreva ◽  
Sushma Mishra ◽  
Peter Draus ◽  
George Bromall ◽  
Don Caputo
Keyword(s):  
Electronic Medical Records ◽  
Data Security ◽  
Medical Records ◽  
Medical Data ◽  
Security Policies ◽  
Organization Structures ◽  
Healthcare Employees

Healthcare employees with their motivation to comply with security policies play an extremely important role in protecting patients’ privacy. In this research we attempt to survey the attitude of healthcare employees towards security of Electronic Medical Records. We further review what factors impact their perception of the medical data security and determine how well they understand policies, procedures, organization structures, and other aspects related to EMR protection. 

scholarly journals Health Information Held Hostage

2019 ◽  
Vol 87 (2) ◽  
pp. 46-48
Author(s):  
Jacek Orzylowski ◽  
Katherine Fleshner
Keyword(s):  
Health Information ◽  
Electronic Medical Records ◽  
Distributed System ◽  
Medical Records ◽  
File System ◽  
Patient Records ◽  
Data Breaches

Electronic medical records are increasingly vulnerable to hackers exploiting the consistent lack of cybersecurity expertise in hospitals. Since education in digital hygiene will likely never truly conquer the absent-minded nature of humankind, it could be worthwhile to implement the distributed system of InterPlanetary File System, reducing the incidence of patient records being held hostage by hackers, but possibly increasing the incidence of data breaches for informational gain only.  (Included as first paragraph of main article)

scholarly journals An Unsupervised Approach to Structuring and Analyzing Repetitive Semantic Structures in Free Text of Electronic Medical Records

2021 ◽  
Author(s):  
Varvara Koshman ◽  
Anastasia Funkner ◽  
Sergey Kovalchuk
Keyword(s):  
Electronic Medical Records ◽  
Medical Records ◽  
Medical Data ◽  
Free Text ◽  
Automatic Annotation ◽  
Text Data ◽  
Data Annotation ◽  
Valuable Data ◽  
Medical Text ◽  
Unsupervised Approach

Electronic Medical Records (EMR) contain a lot of valuable data about patients, which is however unstructured. There is a lack of labeled medical text data in Russian and there are no tools for automatic annotation. We present an unsupervised approach to medical data annotation. Morphological and syntactical analyses of initial sentences produce syntactic trees, from which similar subtrees are then grouped by Word2Vec and labeled using dictionaries and Wikidata categories. This method can be used to automatically label EMRs in Russian and proposed methodology can be applied to other languages, which lack resources for automatic labeling and domain vocabularies.

Adolescent Substance Use Screening Integrated With Electronic Medical Records

2014 ◽  
Author(s):  
C. McKenna ◽  
B. Gaines ◽  
C. Hatfield ◽  
S. Helman ◽  
L. Meyer ◽  
...  
Keyword(s):  
Substance Use ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Adolescent Substance Use ◽  
Adolescent Substance
Sign in / Sign up

Export Citation Format

Share Document