scholarly journals PMD56 Using Advanced Health Care Data Analytics to Identify and Characterize Central Venous Catheterization Episodes via Electronic Health Records in the Veterans Affairs

2012 ◽  
Vol 15 (4) ◽  
pp. A72
Author(s):  
S.L. Duvall ◽  
A.W.C. Kamauu ◽  
B.C. Sauer
Keyword(s):  
Health Care ◽  
Electronic Health Records ◽  
Data Analytics ◽  
Central Venous Catheterization ◽  
Veterans Affairs ◽  
Central Venous ◽  
Health Records ◽  
Health Care Data ◽  
Venous Catheterization ◽  
Electronic Health

scholarly journals dxpr: an R package for generating analysis-ready data from electronic health records—diagnoses and procedures

2021 ◽  
Vol 7 ◽  
pp. e520
Author(s):  
Yi-Ju Tseng ◽  
Hsiang-Ju Chiu ◽  
Chun Ju Chen
Keyword(s):  
Health Care ◽  
Electronic Health Records ◽  
Clinical Data ◽  
Data Analytics ◽  
Medical Information ◽  
Ductus Arteriosus ◽  
R Package ◽  
Health Records ◽  
Significant Difference ◽  
Electronic Health

Background Enriched electronic health records (EHRs) contain crucial information related to disease progression, and this information can help with decision-making in the health care field. Data analytics in health care is deemed as one of the essential processes that help accelerate the progress of clinical research. However, processing and analyzing EHR data are common bottlenecks in health care data analytics. Methods The dxpr R package provides mechanisms for integration, wrangling, and visualization of clinical data, including diagnosis and procedure records. First, the dxpr package helps users transform International Classification of Diseases (ICD) codes to a uniform format. After code format transformation, the dxpr package supports four strategies for grouping clinical diagnostic data. For clinical procedure data, two grouping methods can be chosen. After EHRs are integrated, users can employ a set of flexible built-in querying functions for dividing data into case and control groups by using specified criteria and splitting the data into before and after an event based on the record date. Subsequently, the structure of integrated long data can be converted into wide, analysis-ready data that are suitable for statistical analysis and visualization. Results We conducted comorbidity data processes based on a cohort of newborns from Medical Information Mart for Intensive Care-III (n = 7,833) by using the dxpr package. We first defined patent ductus arteriosus (PDA) cases as patients who had at least one PDA diagnosis (ICD, Ninth Revision, Clinical Modification [ICD-9-CM] 7470*). Controls were defined as patients who never had PDA diagnosis. In total, 381 and 7,452 patients with and without PDA, respectively, were included in our study population. Then, we grouped the diagnoses into defined comorbidities. Finally, we observed a statistically significant difference in 8 of the 16 comorbidities among patients with and without PDA, including fluid and electrolyte disorders, valvular disease, and others. Conclusions This dxpr package helps clinical data analysts address the common bottleneck caused by clinical data characteristics such as heterogeneity and sparseness.

scholarly journals Identifying Multisite Chronic Pain with Electronic Health Records Data

Pain Medicine ◽  
2020 ◽  
Vol 21 (12) ◽  
pp. 3387-3392
Author(s):  
Michael Von Korff ◽  
Lynn L DeBar ◽  
Richard A Deyo ◽  
Meghan Mayhew ◽  
Robert D Kerns ◽  
...  
Keyword(s):  
Health Care ◽  
Chronic Pain ◽  
Electronic Health Records ◽  
Single Site ◽  
Self Report ◽  
Survey Questionnaire ◽  
Health Records ◽  
Health Care Data ◽  
Electronic Health ◽  
Weighted Prevalence

Abstract Background Multisite chronic pain (MSCP) is associated with increased chronic pain impact, but methods for identifying MSCP for epidemiological research have not been evaluated. Objective We assessed the validity of identifying MSCP using electronic health care data compared with survey questionnaires. Methods Stratified random samples of adults served by Kaiser Permanente Northwest and Washington (N = 2,059) were drawn for a survey, oversampling persons with frequent use of health care for pain. MSCP and single-site chronic pain were identified by two methods, with electronic health care data and with self-report of common chronic pain conditions by survey questionnaire. Analyses were weighted to adjust for stratified sampling. Results MSCP was somewhat less common when ascertained by electronic health records (14.7% weighted prevalence) than by survey questionnaire (25.9% weighted prevalence). Agreement of the two MSCP classifications was low (kappa agreement statistic of 0.21). Ascertainment of MSCP with electronic health records was 30.9% sensitive, 91.0% specific, and had a positive predictive value of 54.5% relative to MSCP identified by self-report as the standard. After adjusting for age and gender, patients with MSCP identified by either electronic health records or self-report showed higher levels of pain-related disability, pain severity, depressive symptoms, and long-term opioid use than persons with single-site chronic pain identified by the same method. Conclusions Identification of MSCP with electronic health care data was insufficiently accurate to be used as a surrogate or screener for MSCP identified by self-report, but both methods identified persons with heightened chronic pain impact.

Negotiating stigma in health care: disclosure and the role of electronic health records

2015 ◽  
Vol 24 (3) ◽  
pp. 227-241 ◽  
Author(s):  
Timothy Stablein ◽  
Joseph Lorenzo Hall ◽  
Chauna Pervis ◽  
Denise L. Anthony
Keyword(s):  
Health Care ◽  
Electronic Health Records ◽  
Health Records ◽  
Electronic Health

Implementing Health Care Quality Measures in Electronic Health Records: A Conceptual Model

2021 ◽  
Author(s):  
Claire M. Campbell ◽  
Daniel R. Murphy ◽  
George E. Taffet ◽  
Anita B. Major ◽  
Christine S. Ritchie ◽  
...  
Keyword(s):  
Health Care ◽  
Electronic Health Records ◽  
Conceptual Model ◽  
Health Care Quality ◽  
Quality Measures ◽  
Care Quality ◽  
Health Records ◽  
Electronic Health

scholarly journals Secure Exchange of Electronic Health Records

2012 ◽  
pp. 1403-1424
Author(s):  
Alejandro Enrique Flores ◽  
Khin Than Win ◽  
Willy Susilo
Keyword(s):  
Health Care ◽  
Access Control ◽  
Electronic Health Records ◽  
Shared Care ◽  
Health Records ◽  
Discretionary Access Control ◽  
Attribute Based Encryption ◽  
Electronic Health ◽  
Role Based ◽  
Traditional Approaches

Protecting the confidentiality of a patient’s information in a shared care environment could become a complex task. Correct identification of users, assigning of access permissions, and resolution of conflict rise as main points of interest in providing solutions for data exchange among health care providers. Traditional approaches such as Mandatory Access Control, Discretionary Access control and Role-Based Access Control policies do not always provide a suitable solution for health care settings, especially for shared care environments. The core of this contribution consists in the description of an approach which uses attribute-based encryption to protect the confidentiality of patients’ information during the exchange of electronic health records among healthcare providers. Attribute-based encryption allows the reinforcing of access policies and reduces the risk of unauthorized access to sensitive information; it also provides a set of functionalities which are described using a case study. Attribute-based encryption provides an answer to restrictions presented by traditional approaches and facilitate the reinforcing of existing security policies over the transmitted data.

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