scholarly journals Diabetes and the direct secondary use of electronic health records: Using routinely collected and stored data to drive research and understanding

2018 ◽  
Vol 4 ◽  
pp. 205520761880465 ◽  
Author(s):  
Tim Robbins ◽  
Sarah N Lim Choi Keung ◽  
Sailesh Sankar ◽  
Harpal Randeva ◽  
Theodoros N Arvanitis
Keyword(s):  
Electronic Health Records ◽  
Missing Values ◽  
Research Output ◽  
Healthcare Research ◽  
Healthcare Sector ◽  
Diabetes Research ◽  
Health Records ◽  
Diverse Range ◽  
Secondary Use ◽  
Electronic Health

Introduction Electronic health records provide an unparalleled opportunity for the use of patient data that is routinely collected and stored, in order to drive research and develop an epidemiological understanding of disease. Diabetes, in particular, stands to benefit, being a data-rich, chronic-disease state. This article aims to provide an understanding of the extent to which the healthcare sector is using routinely collected and stored data to inform research and epidemiological understanding of diabetes mellitus. Methods Narrative literature review of articles, published in both the medical- and engineering-based informatics literature. Results There has been a significant increase in the number of papers published, which utilise electronic health records as a direct data source for diabetes research. These articles consider a diverse range of research questions. Internationally, the secondary use of electronic health records, as a research tool, is most prominent in the USA. The barriers most commonly described in research studies include missing values and misclassification, alongside challenges of establishing the generalisability of results. Discussion Electronic health record research is an important and expanding area of healthcare research. Much of the research output remains in the form of conference abstracts and proceedings, rather than journal articles. There is enormous opportunity within the United Kingdom to develop these research methodologies, due to national patient identifiers. Such a healthcare context may enable UK researchers to overcome many of the barriers encountered elsewhere and thus to truly unlock the potential of electronic health records.

scholarly journals Secondary Use of Electronic Health Records for Building Large, Real-World ILD Cohorts

2021 ◽  
Author(s):  
E.D. Farrand ◽  
O. Gologorskaya ◽  
H. Mills ◽  
L. Radhakrishnan ◽  
H.R. Collard ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Real World ◽  
Health Records ◽  
Secondary Use ◽  
Electronic Health

scholarly journals Defining and measuring completeness of electronic health records for secondary use

2013 ◽  
Vol 46 (5) ◽  
pp. 830-836 ◽  
Author(s):  
Nicole G. Weiskopf ◽  
George Hripcsak ◽  
Sushmita Swaminathan ◽  
Chunhua Weng
Keyword(s):  
Electronic Health Records ◽  
Health Records ◽  
Secondary Use ◽  
Electronic Health

scholarly journals Similarity Analyzer for Semantic Interoperability of Electronic Health Records Using Artificial Intelligence (AI)

2019 ◽  
Vol 1 (2) ◽  
Author(s):  
Arjmand Naveed
Keyword(s):  
Artificial Intelligence ◽  
Electronic Health Records ◽  
Semantic Interoperability ◽  
Healthcare Sector ◽  
Data Mapping ◽  
Mapping Data ◽  
Data Types ◽  
Health Records ◽  
Different Types ◽  
Electronic Health

The introduction of Electronic Health Records (EHR) has opened possibilities for solving interoperability issues within the healthcare sector. However, even with the introduction of EHRs, healthcare systems like hospitals and pharmacies remain isolated with no sharing of EHRs due to semantic interoperability issues. This paper extends our previous work in which we proposed a framework that dealt with semantic interoperability and security of EHR. The extension is the proposal of a cloud-based similarity analyzer for data structuring, data mapping, data modeling and conflict removal using Word2vec Artificial Intelligence (AI) technique.  Different types of conflicts are removed from data in order to model data into common data types which can be interpreted by different stakeholders.

scholarly journals A Stochastic Multivariate Irregularly Sampled Time Series Imputation Method for Electronic Health Records

2021 ◽  
Vol 1 (3) ◽  
pp. 166-181
Author(s):  
Muhammad Adib Uz Zaman ◽  
Dongping Du
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Electronic Health Records ◽  
Missing Values ◽  
Time Series Data ◽  
Temporal Information ◽  
Series Data ◽  
High Dimensional ◽  
Health Records ◽  
Electronic Health

Electronic health records (EHRs) can be very difficult to analyze since they usually contain many missing values. To build an efficient predictive model, a complete dataset is necessary. An EHR usually contains high-dimensional longitudinal time series data. Most commonly used imputation methods do not consider the importance of temporal information embedded in EHR data. Besides, most time-dependent neural networks such as recurrent neural networks (RNNs) inherently consider the time steps to be equal, which in many cases, is not appropriate. This study presents a method using the gated recurrent unit (GRU), neural ordinary differential equations (ODEs), and Bayesian estimation to incorporate the temporal information and impute sporadically observed time series measurements in high-dimensional EHR data.

scholarly journals A Nuisance-Free Inference Procedure Accounting for the Unknown Missingness with Application to Electronic Health Records

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1154
Author(s):  
Jiwei Zhao ◽  
Chi Chen
Keyword(s):  
Electronic Health Records ◽  
Missing Values ◽  
Outcome Variable ◽  
High Dimensional ◽  
Real World Data ◽  
Inference Procedure ◽  
Health Records ◽  
Missingness Mechanism ◽  
Patient Reported ◽  
Electronic Health

We study how to conduct statistical inference in a regression model where the outcome variable is prone to missing values and the missingness mechanism is unknown. The model we consider might be a traditional setting or a modern high-dimensional setting where the sparsity assumption is usually imposed and the regularization technique is popularly used. Motivated by the fact that the missingness mechanism, albeit usually treated as a nuisance, is difficult to specify correctly, we adopt the conditional likelihood approach so that the nuisance can be completely ignored throughout our procedure. We establish the asymptotic theory of the proposed estimator and develop an easy-to-implement algorithm via some data manipulation strategy. In particular, under the high-dimensional setting where regularization is needed, we propose a data perturbation method for the post-selection inference. The proposed methodology is especially appealing when the true missingness mechanism tends to be missing not at random, e.g., patient reported outcomes or real world data such as electronic health records. The performance of the proposed method is evaluated by comprehensive simulation experiments as well as a study of the albumin level in the MIMIC-III database.

scholarly journals BSF-EHR: Blockchain Security Framework for Electronic Health Records of Patients

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2865
Author(s):  
Ibrahim Abunadi ◽  
Ramasamy Lakshmana Kumar
Keyword(s):  
Electronic Health Records ◽  
Medical Information ◽  
Health Sector ◽  
Personal Data ◽  
Security And Privacy ◽  
Healthcare Sector ◽  
Security Framework ◽  
Health Records ◽  
Blockchain Technology ◽  
Electronic Health

In the current epoch of smart homes and cities, personal data such as patients’ names, diseases and addresses are often violated. This is frequently associated with the safety of the electronic health records (EHRs) of patients. EHRs have numerous benefits worldwide, but at present, EHR information is subject to considerable security and privacy issues. This paper proposes a way to provide a secure solution to these issues. Previous sophisticated techniques dealing with the protection of EHRs usually make data inaccessible to patients. These techniques struggle to balance data confidentiality, patient demand and constant interaction with provider data. Blockchain technology solves the above problems since it distributes information in a transactional and decentralized manner. The usage of blockchain technology could help the health sector to balance the accessibility and privacy of EHRs. This paper proposes a blockchain security framework (BSF) to effectively and securely store and keep EHRs. It presents a safe and proficient means of acquiring medical information for doctors, patients and insurance agents while protecting the patient’s data. This work aims to examine how our proposed framework meets the security needs of doctors, patients and third parties and how the structure addresses safety and confidentiality concerns in the healthcare sector. Simulation outcomes show that this framework efficiently protects EHR data.

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