scholarly journals Ethical and Clinical Issues in Integrated Care Settings: Patient Privacy Concerns and Electronic Health Records

2017 ◽  
Vol 15 (3) ◽  
pp. 301-305
Author(s):  
Mytilee Vemuri ◽  
Laura B. Dunn
Keyword(s):  
Electronic Health Records ◽  
Integrated Care ◽  
Patient Privacy ◽  
Health Records ◽  
Clinical Issues ◽  
Privacy Concerns ◽  
Electronic Health

Privacy-Friendly Management of Electronic Health Records in the eHealth Context

2015 ◽  
pp. 251-264
Author(s):  
Milica Milutinovic ◽  
Bart De Decker
Keyword(s):  
Electronic Health Records ◽  
Large Scale ◽  
Patient Data ◽  
Health Records ◽  
Privacy Concerns ◽  
Ubiquitous Technology ◽  
Emergency Situations ◽  
User Centric ◽  
Electronic Health

Electronic Health Records (EHRs) are becoming the ubiquitous technology for managing patients' records in many countries. They allow for easier transfer and analysis of patient data on a large scale. However, privacy concerns linked to this technology are emerging. Namely, patients rarely fully understand how EHRs are managed. Additionally, the records are not necessarily stored within the organization where the patient is receiving her healthcare. This service may be delegated to a remote provider, and it is not always clear which health-provisioning entities have access to this data. Therefore, in this chapter the authors propose an alternative where users can keep and manage their records in their existing eHealth systems. The approach is user-centric and enables the patients to have better control over their data while still allowing for special measures to be taken in case of emergency situations with the goal of providing the required care to the patient.

scholarly journals The OpenDeID corpus for patient de-identification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jitendra Jonnagaddala ◽  
Aipeng Chen ◽  
Sean Batongbacal ◽  
Chandini Nekkantti
Keyword(s):  
Electronic Health Records ◽  
Health Information ◽  
Sensitive Information ◽  
Protected Health Information ◽  
Patient Privacy ◽  
Health Records ◽  
Automatic Methods ◽  
Electronic Health ◽  
Pathology Reports ◽  
Privacy And Confidentiality

AbstractFor research purposes, protected health information is often redacted from unstructured electronic health records to preserve patient privacy and confidentiality. The OpenDeID corpus is designed to assist development of automatic methods to redact sensitive information from unstructured electronic health records. We retrieved 4548 unstructured surgical pathology reports from four urban Australian hospitals. The corpus was developed by two annotators under three different experimental settings. The quality of the annotations was evaluated for each setting. Specifically, we employed serial annotations, parallel annotations, and pre-annotations. Our results suggest that the pre-annotations approach is not reliable in terms of quality when compared to the serial annotations but can drastically reduce annotation time. The OpenDeID corpus comprises 2,100 pathology reports from 1,833 cancer patients with an average of 737.49 tokens and 7.35 protected health information entities annotated per report. The overall inter annotator agreement and deviation scores are 0.9464 and 0.9726, respectively. Realistic surrogates are also generated to make the corpus suitable for distribution to other researchers.

scholarly journals Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19

2020 ◽  
Author(s):  
Akhil Vaid ◽  
Suraj K Jaladanki ◽  
Jie Xu ◽  
Shelly Teng ◽  
Arvind Kumar ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Large Datasets ◽  
Mortality Prediction ◽  
Patient Privacy ◽  
Local Data ◽  
Health Records ◽  
Electronic Health ◽  
Mlp Model ◽  
Combined Data ◽  
Better Than

Machine learning (ML) models require large datasets which may be siloed across different healthcare institutions. Using federated learning, a ML technique that avoids locally aggregating raw clinical data across multiple institutions, we predict mortality within seven days in hospitalized COVID-19 patients. Patient data was collected from Electronic Health Records (EHRs) from five hospitals within the Mount Sinai Health System (MSHS). Logistic Regression with L1 regularization (LASSO) and Multilayer Perceptron (MLP) models were trained using local data at each site, a pooled model with combined data from all five sites, and a federated model that only shared parameters with a central aggregator. Both the federated LASSO and federated MLP models performed better than their local model counterparts at four hospitals. The federated MLP model also outperformed the federated LASSO model at all hospitals. Federated learning shows promise in COVID-19 EHR data to develop robust predictive models without compromising patient privacy.

scholarly journals Estimate of disease heritability using 7.4 million familial relationships inferred from electronic health records

2016 ◽  
Author(s):  
Fernanda Polubriaginof ◽  
Rami Vanguri ◽  
Kayla Quinnies ◽  
Gillian M. Belbin ◽  
Alexandre Yahi ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Patient Privacy ◽  
Next Of Kin ◽  
Health Records ◽  
Familial Relationships ◽  
Wide Range ◽  
Causes Of Disease ◽  
Contact Data ◽  
Electronic Health ◽  
Patient Emergency

AbstractHeritability is essential for understanding the biological causes of disease, but requires laborious patient recruitment and phenotype ascertainment. Electronic health records (EHR) passively capture a wide range of clinically relevant data and provide a novel resource for studying the heritability of traits that are not typically accessible. EHRs contain next-of-kin information collected via patient emergency contact forms, but until now, these data have gone unused in research. We mined emergency contact data at three academic medical centers and identified millions of familial relationships while maintaining patient privacy. Identified relationships were consistent with genetically-derived relatedness. We used EHR data to compute heritability estimates for 500 disease phenotypes. Overall, estimates were consistent with literature and between sites. Inconsistencies were indicative of limitations and opportunities unique to EHR research. These analyses provide a novel validation of the use of EHRs for genetics and disease research.One Sentence SummaryWe demonstrate that next-of-kin information can be used to identify familial relationships in the EHR, providing unique opportunities for precision medicine studies.

Legal and Ethical Considerations in the Implementation of Electronic Health Records

2017 ◽  
pp. 960-973
Author(s):  
Karen Ervin
Keyword(s):  
United States ◽  
Electronic Health Records ◽  
Relevant Literature ◽  
Database Systems ◽  
The United States ◽  
Patient Privacy ◽  
Health Records ◽  
Electronic Health ◽  
Health Database ◽  
State Library

This chapter examines the literature of healthcare in the United States during the transitioning to electronic records. Key government legislation, such as the Health Insurance Portability and Accountability Act (HIPAA) and the Health Information Technology for Economic and Clinical Health Act (HITECH), which were part of the American Recovery and Reinvestment Act (ARRA) and the Affordable Health Care Act, are reviewed. The review concentrates on patient privacy issues, how they have been addressed in these acts, and what recommendations for improvement have been found in the literature. A comparison of the adoption of electronic health records on a nationwide scale in three countries is included. England, Australia, and the United States are all embarking in and are at different stages of implementing nationwide electronic health database systems. The resources used in locating relevant literature were PubMed, Medline, Highwire Press, State Library of Pennsylvania, and Google Scholar databases.

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