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

Assessing function of electronic health records for real-world data generation

2018 ◽  
Vol 24 (3) ◽  
pp. 95-98 ◽  
Author(s):  
Daphne Guinn ◽  
Erin E Wilhelm ◽  
Grazyna Lieberman ◽  
Sean Khozin
Keyword(s):  
Electronic Health Records ◽  
Real World ◽  
Data Generation ◽  
Real World Data ◽  
Health Records ◽  
World Data ◽  
Electronic Health

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 A Distribution-based Method for Assessing The Differences between Clinical Trial Target Populations and Patient Populations in Electronic Health Records

2014 ◽  
Vol 05 (02) ◽  
pp. 463-479 ◽  
Author(s):  
P. Ryan ◽  
Y. Zhang ◽  
F. Liu ◽  
J. Gao ◽  
J.T. Bigger ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Electronic Health Records ◽  
Real World ◽  
World Population ◽  
Health Records ◽  
Target Populations ◽  
Electronic Health

SummaryObjective: To improve the transparency of clinical trial generalizability and to illustrate the method using Type 2 diabetes as an example.Methods: Our data included 1,761 diabetes clinical trials and the electronic health records (EHR) of 26,120 patients with Type 2 diabetes who visited Columbia University Medical Center of New-York Presbyterian Hospital. The two populations were compared using the Generalizability Index for Study Traits (GIST) on the earliest diagnosis age and the mean hemoglobin A1c (HbA1c) values.Results: Greater than 70% of Type 2 diabetes studies allow patients with HbA1c measures between 7 and 10.5, but less than 40% of studies allow HbA1c<7 and fewer than 45% of studies allow HbA1c>10.5. In the real-world population, only 38% of patients had HbA1c between 7 and 10.5, with 12% having values above the range and 52% having HbA1c<7. The GIST for HbA1c was 0.51. Most studies adopted broad age value ranges, with the most common restrictions excluding patients >80 or <18 years. Most of the real-world population fell within this range, but 2% of patients were <18 at time of first diagnosis and 8% were >80. The GIST for age was 0.75. Conclusions: We contribute a scalable method to profile and compare aggregated clinical trial target populations with EHR patient populations. We demonstrate that Type 2 diabetes studies are more generalizable with regard to age than they are with regard to HbA1c. We found that the generalizability of age increased from Phase 1 to Phase 3 while the generalizability of HbA1c decreased during those same phases. This method can generalize to other medical conditions and other continuous or binary variables. We envision the potential use of EHR data for examining the generaliz-ability of clinical trials and for defining population-representative clinical trial eligibility criteria.Citation: Weng C, Li Y, Ryan P, Zhang Y, Liu F, Gao J, Bigger JT, Hripcsak G. A distribution-based method for assessing the differences between clinical trial target populations and patient populations in electronic health records. Appl Clin Inf 2014; 5: 463–479 http://dx.doi.org/10.4338/ACI-2013-12-RA-0105

scholarly journals Development and implementation of a dynamically updated big data intelligence platform from electronic health records for nasopharyngeal carcinoma research

2019 ◽  
Vol 92 (1102) ◽  
pp. 20190255 ◽  
Author(s):  
Li Lin ◽  
Wei Liang ◽  
Chao-Feng Li ◽  
Xiao-Dan Huang ◽  
Jia-Wei Lv ◽  
...  
Keyword(s):  
Big Data ◽  
Electronic Health Records ◽  
Real World ◽  
Nasopharyngeal Cancer ◽  
Recall Rate ◽  
Health Records ◽  
Cancer Centre ◽  
Standard Data ◽  
Electronic Health ◽  
Data Elements

Objective: To develop a big data intelligence platform for secondary use of electronic health records (EHRs) data to facilitate research for nasopharyngeal cancer (NPC). Methods: This project was launched in 2015 and carried out by the cooperation of an academic cancer centre and a technology company. Patients diagnosed with NPC at Sun Yat-sen University Cancer Centre since January 2008 were included in the platform. Standard data elements were established to defined 981 variables for the platform. For each patient, data from 13 EHRs systems were extracted, integrated, structurized and normalized. Eight functional modules were constructed for the platform to facilitate the investigators to identify eligible patients, establish research projects, conduct statistical analysis, track the follow-up, search literature, etc. Results: From January 2008 to December 2018, 54,703 patients diagnosed with NPC were included. Of these patients, 39,058 (71.4%) were male, and 15,645 (28.6%) were female; median age was 47 (interquartile range, 39–55) years. Of 981 variables, 341 were obtained from data structurization and normalization, of which 68 were generated by interacting multiple data sources via well-defined logical rules. The average precision rate, recall rate and F-measure for 341 variables were 0.97 ± 0.024, 0.92 ± 0.030, and 0.94 ± 0.027 respectively. The platform is regularly updated every seven days to include new patients and add new data for existing patients. Up to now, eight big data-driven retrospective studies have been published from the platform. Conclusion: Our big data intelligence platform demonstrates the feasibility of integrating EHRs data of routine healthcare, and offers an important perspective on real-world study of NPC. The continued efforts may be focus on data sharing among multiple hospitals and publicly releasing of data files. Advances in knowledge: Our big data intelligence platform is the first disease-specific data platform for NPC research. It incorporates comprehensive EHRs data from routine healthcare, which can facilitate real-world study of NPC in risk stratification, decision-making and comorbidities management.

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