scholarly journals Leveraging Electronic Health Records to Support Chronic Disease Management: The Need for Temporal Data Views

2014 ◽  
pp. 299-318
Keyword(s):  
Chronic Disease ◽  
Disease Management ◽  
Electronic Health Records ◽  
Chronic Disease Management ◽  
Temporal Data ◽  
Health Records ◽  
Electronic Health

scholarly journals Open source electronic health records and chronic disease management

2014 ◽  
Vol 21 (e1) ◽  
pp. e50-e54 ◽  
Author(s):  
Jason C Goldwater ◽  
Nancy J Kwon ◽  
Ashley Nathanson ◽  
Alison E Muckle ◽  
Alexa Brown ◽  
...  
Keyword(s):  
Chronic Disease ◽  
Disease Management ◽  
Electronic Health Records ◽  
Open Source ◽  
Chronic Disease Management ◽  
Health Records ◽  
Electronic Health

scholarly journals Using big data to improve cardiovascular care and outcomes in China: a protocol for the CHinese Electronic health Records Research in Yinzhou (CHERRY) Study

BMJ Open ◽  
2018 ◽  
Vol 8 (2) ◽  
pp. e019698 ◽  
Author(s):  
Hongbo Lin ◽  
Xun Tang ◽  
Peng Shen ◽  
Dudan Zhang ◽  
Jinguo Wu ◽  
...  
Keyword(s):  
Information System ◽  
Big Data ◽  
Chronic Disease ◽  
Disease Management ◽  
Electronic Health Records ◽  
Chronic Disease Management ◽  
Health Information System ◽  
Health Records ◽  
Electronic Health ◽  
Cardiovascular Care

IntroductionData based on electronic health records (EHRs) are rich with individual-level longitudinal measurement information and are becoming an increasingly common data source for clinical risk prediction worldwide. However, few EHR-based cohort studies are available in China. Harnessing EHRs for research requires a full understanding of data linkages, management, and data quality in large data sets, which presents unique analytical opportunities and challenges. The purpose of this study is to provide a framework to establish a uniquely integrated EHR database in China for scientific research.Methods and analysisThe CHinese Electronic health Records Research in Yinzhou (CHERRY) Study will extract individual participant data within the regional health information system of an eastern coastal area of China to establish a longitudinal population-based ambispective cohort study for cardiovascular care and outcomes research. A total of 1 053 565 Chinese adults aged over 18 years were registered in the health information system in 2009, and there were 23 394 deaths from 1 January 2009 to 31 December 2015. The study will include information from multiple epidemiological surveys; EHRs for chronic disease management; and health administrative, clinical, laboratory, drug and electronic medical record (EMR) databases. Follow-up of fatal and non-fatal clinical events is achieved through records linkage to the regional system of disease surveillance, chronic disease management and EMRs (based on diagnostic codes from the International Classification of Diseases, tenth revision). The CHERRY Study will provide a unique platform and serve as a valuable big data resource for cardiovascular risk prediction and population management, for primary and secondary prevention of cardiovascular events in China.Ethics and disseminationThe CHERRY Study was approved by the Peking University Institutional Review Board (IRB00001052-16011) in April 2016. Results of the study will be disseminated through published journal articles, conferences and seminar presentations, and on the study website (http://www.cherry-study.org).

Sensitivity of Clinical Pediatric Obesity Diagnosis Documented in Electronic Health Records

2020 ◽  
Vol 59 (14) ◽  
pp. 1274-1281
Author(s):  
Christine B. San Giovanni ◽  
Myla Ebeling ◽  
Robert A. Davis ◽  
C. Shaun Wagner ◽  
William T. Basco
Keyword(s):  
Body Mass Index ◽  
Logistic Regression ◽  
Chronic Disease ◽  
Electronic Health Records ◽  
Pediatric Obesity ◽  
Disease Diagnosis ◽  
Health Records ◽  
Academic Affiliation ◽  
Specific Factors ◽  
Electronic Health

Objective. This study tested the sensitivity of obesity diagnosis in electronic health records (EHRs) using body mass index (BMI) classification and identified variables associated with obesity diagnosis. Methods. Eligible children aged 2 to 18 years had a calculable BMI in 2017 and had at least 1 visit in 2016 and 2017. Sensitivity of clinical obesity diagnosis compared with children’s BMI percentile was calculated. Logistic regression was performed to determine variables associated with obesity diagnosis. Results. Analyses included 31 059 children with BMI at or above 95th percentile. Sensitivity of clinical obesity diagnosis was 35.81%. Clinical obesity diagnosis was more likely if the child had a well visit, had Medicaid insurance, was female, Hispanic or Black, had a chronic disease diagnosis, and saw a provider in a practice in an urban area or with academic affiliation. Conclusion. Sensitivity of clinical obesity diagnosis in EHR is low. Clinical obesity diagnosis is associated with nonmodifiable child-specific factors but also modifiable practice-specific factors.

scholarly journals Learning from heterogeneous temporal data in electronic health records

2017 ◽  
Vol 65 ◽  
pp. 105-119 ◽  
Author(s):  
Jing Zhao ◽  
Panagiotis Papapetrou ◽  
Lars Asker ◽  
Henrik Boström
Keyword(s):  
Electronic Health Records ◽  
Temporal Data ◽  
Health Records ◽  
Electronic Health
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