Health informatics. Data elements and their metadata for describing structured clinical genomic sequence information in electronic health records

2017 ◽  
Keyword(s):  
Electronic Health Records ◽  
Health Informatics ◽  
Genomic Sequence ◽  
Sequence Information ◽  
Health Records ◽  
Electronic Health ◽  
Data Elements ◽  
Clinical Genomic ◽  
Genomic Sequence Information

scholarly journals From screening to ascertainment of the primary outcome using electronic health records: Challenges in the STRIDE trial

2020 ◽  
Vol 17 (4) ◽  
pp. 346-350
Author(s):  
Denise Esserman
Keyword(s):  
Electronic Health Records ◽  
Electronic Health Record ◽  
Health Care Systems ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Health Records ◽  
Record Data ◽  
Care Systems ◽  
Electronic Health ◽  
Data Elements

Electronic health record data are a rich resource and can be utilized to answer a wealth of research questions. It is important when using electronic health record data in clinical trials that systems be put in place and vetted prior to enrollment to ensure data elements can be collected consistently across all health care systems. It is often overlooked how something conceptualized on paper (e.g. use of the electronic health record in a study) can be difficult to implement in practice. This article discusses some of the challenges in using electronic health records in the conduct of the STRIDE (Strategies to Reduce Injuries and Develop Confidence in Elders) trial, how we handled those challenges, and the lessons we learned for the conduct of future trials looking to employ the electronic health record.

Five analytic challenges in working with electronic health records data to support clinical trials with some solutions

2020 ◽  
Vol 17 (4) ◽  
pp. 370-376
Author(s):  
Benjamin A Goldstein
Keyword(s):  
Clinical Trials ◽  
Electronic Health Records ◽  
Large Scale ◽  
Point Of Care ◽  
Cross Sectional ◽  
Health Records ◽  
Data Resource ◽  
Electronic Health ◽  
Data Elements ◽  
Data Efficiency

Electronic health records data are becoming a key data resource in clinical research. Owing to issues of data efficiency, electronic health records data are being used for clinical trials. This includes both large-scale pragmatic trails and smaller—more focused—point-of-care trials. While electronic health records data open up a number of scientific opportunities, they also present a number of analytic challenges. This article discusses five particular challenges related to organizing electronic health records data for analytic purposes. These are as follows: (1) data are not organized for research purposes, (2) data are both densely and irregularly observed, (3) we don’t have all data elements we may want or need, (4) data are both cross-sectional and longitudinal, and (5) data may be informatively observed. While laying out these challenges, the article notes how many of these challenges can be addressed by careful and thoughtful study design as well as by integration of clinicians and informaticians into the analytic team.

scholarly journals Documentation of social determinants in electronic health records with and without standardized terminologies: A comparative study

2018 ◽  
Vol 28 (1) ◽  
pp. 39-47 ◽  
Author(s):  
Karen A Monsen ◽  
Joyce M Rudenick ◽  
Nicole Kapinos ◽  
Kathryn Warmbold ◽  
Siobhan K McMahon ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Free Text ◽  
Snomed Ct ◽  
Health Records ◽  
Behavioral Determinants ◽  
Omaha System ◽  
Standardized Terminology ◽  
Electronic Health ◽  
Data Elements ◽  
Improve Health

Background: Electronic health records (EHRs) are a promising new source of population health data that may improve health outcomes. However, little is known about the extent to which social and behavioral determinants of health (SBDH) are currently documented in EHRs, including how SBDH are documented, and by whom. Standardized nursing terminologies have been developed to assess and document SBDH. Objective: We examined the documentation of SBDH in EHRs with and without standardized nursing terminologies. Methods: We carried out a review of the literature for SBDH phrases organized by topic, which were used for analyses. Key informant interviews were conducted regarding SBDH phrases. Results: In nine EHRs (six acute care, three community care) 107 SBDH phrases were documented using free text, structured text, and standardized terminologies in diverse screens and by multiple clinicians, admitting personnel, and other staff. SBDH phrases were documented using one of three standardized terminologies ( N = average number of phrases per terminology per EHR): ICD-9/10 ( N = 1); SNOMED CT ( N = 1); Omaha System ( N = 79). Most often, standardized terminology data were documented by nurses or other clinical staff versus receptionists or other non-clinical personnel. Documentation ‘unknown’ differed significantly between EHRs with and without the Omaha System (mean = 26.0 (standard deviation (SD) = 8.7) versus mean = 74.5 (SD = 16.5)) ( p = .005). SBDH documentation in EHRs differed based on the presence of a nursing terminology. Conclusions: The Omaha System enabled a more comprehensive, holistic assessment and documentation of interoperable SBDH data. Further research is needed to determine SBDH data elements that are needed across settings, the uses of SBDH data in practice, and to examine patient perspectives related to SBDH assessments.

scholarly journals Unlocking the Potential of Electronic Health Records for Health Research

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Seungwon Lee ◽  
Yuan Xu ◽  
Adam G D'Souza ◽  
Elliot A Martin ◽  
Chelsea Doktorchik ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Health Research ◽  
Care Delivery ◽  
Free Text ◽  
Imaging Data ◽  
Health Records ◽  
Data Source ◽  
Electronic Health ◽  
Data Elements ◽  
The City

Electronic health records (EHRs), originally designed to facilitate health care delivery, are becoming a valuable data source for health research. EHR systems have two components: the front end, where the data is entered by healthcare workers including physicians and nurses, and the back-end electronic data warehouse where the data is stored in a relational database. EHR data elements can be of many types, which can be categorized as structured, unstructured free-text, and imaging data. The Sunrise Clinical Manager (SCM) EHR is one example of an inpatient EHR system, which covers the city of Calgary (Alberta, Canada). This system, under the management of Alberta Health Services, is now being explored for research use. The purpose of the present paper is to describe the SCM EHR for research purposes, showing how this generalizes to EHRs in general. We further discuss advantages, challenges (e.g. potential bias and data quality issues), and analytical capacities and requirements associated with using EHRs.

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.

scholarly journals The Digitization of Patient Care: A Review of the Effects of Electronic Health Records on Health Care Quality and Utilization

2019 ◽  
Vol 40 (1) ◽  
pp. 487-500 ◽  
Author(s):  
Hilal Atasoy ◽  
Brad N. Greenwood ◽  
Jeffrey Scott McCullough
Keyword(s):  
Electronic Health Records ◽  
Health Informatics ◽  
Health Care Quality ◽  
Clinical Care ◽  
Academic Research ◽  
Care Quality ◽  
Health Records ◽  
Policy Practice ◽  
Electronic Health ◽  
Cost Implications

Electronic health records (EHRs) adoption has become nearly universal during the past decade. Academic research into the effects of EHRs has examined factors influencing adoption, clinical care benefits, financial and cost implications, and more. We provide an interdisciplinary overview and synthesis of this literature, drawing on work in public and population health, informatics, medicine, management information systems, and economics. We then chart paths forward for policy, practice, and research.

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