scholarly journals Interface Terminologies: Facilitating Direct Entry of Clinical Data into Electronic Health Record Systems

2006 ◽  
Vol 13 (3) ◽  
pp. 277-288 ◽  
Author(s):  
S. T. Rosenbloom ◽  
R. A. Miller ◽  
K. B. Johnson ◽  
P. L. Elkin ◽  
S. H. Brown
Keyword(s):  
Electronic Health Record ◽  
Clinical Data ◽  
Health Record ◽  
Electronic Health

scholarly journals A Proposed Electronic Health Record Content Structure Based on Clinical Organizations Survey

2014 ◽  
Vol 15 (13) ◽  
pp. 5233-5246 ◽  
Author(s):  
Dr. Ayman E. Khedr ◽  
Fahad Kamal Alsheref
Keyword(s):  
Data Structure ◽  
Electronic Health Records ◽  
Electronic Health Record ◽  
Clinical Data ◽  
Data Exchange ◽  
Medical Information System ◽  
Health Record ◽  
Medical Organization ◽  
Health Records ◽  
Electronic Health

Computer systems and communication technologies made a strong and influential presence in the different fields of medicine. The cornerstone of a functional medical information system is the Electronic Health Records (EHR) management system. Several electronic health records systems were implemented in different states with different clinical data structures that prevent data exchange between systems even in the same state. This leads to the important barrier in implementing EHR system which is the lack of standards of EHR clinical data structure. In this paper we made a survey on several in international and Egyptian medical organization for implementing electronic health record systems for finding the best electronic health record clinical data structure that contains all patient’s medical data. We proposed an electronic health record system with a standard clinical data structure based on the international and Egyptian medical organization survey and with avoiding the limitations in the other electronic health record that exists in the survey.

scholarly journals Developing a scalable FHIR-based clinical data normalization pipeline for standardizing and integrating unstructured and structured electronic health record data

JAMIA Open ◽  
2019 ◽  
Vol 2 (4) ◽  
pp. 570-579 ◽  
Author(s):  
Na Hong ◽  
Andrew Wen ◽  
Feichen Shen ◽  
Sunghwan Sohn ◽  
Chen Wang ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Language Processing ◽  
Clinical Data ◽  
Large Scale ◽  
Structured Data ◽  
Health Record ◽  
Data Normalization ◽  
Electronic Health Record Data ◽  
Electronic Health ◽  
Clinical Resource

Abstract Objective To design, develop, and evaluate a scalable clinical data normalization pipeline for standardizing unstructured electronic health record (EHR) data leveraging the HL7 Fast Healthcare Interoperability Resources (FHIR) specification. Methods We established an FHIR-based clinical data normalization pipeline known as NLP2FHIR that mainly comprises: (1) a module for a core natural language processing (NLP) engine with an FHIR-based type system; (2) a module for integrating structured data; and (3) a module for content normalization. We evaluated the FHIR modeling capability focusing on core clinical resources such as Condition, Procedure, MedicationStatement (including Medication), and FamilyMemberHistory using Mayo Clinic’s unstructured EHR data. We constructed a gold standard reusing annotation corpora from previous NLP projects. Results A total of 30 mapping rules, 62 normalization rules, and 11 NLP-specific FHIR extensions were created and implemented in the NLP2FHIR pipeline. The elements that need to integrate structured data from each clinical resource were identified. The performance of unstructured data modeling achieved F scores ranging from 0.69 to 0.99 for various FHIR element representations (0.69–0.99 for Condition; 0.75–0.84 for Procedure; 0.71–0.99 for MedicationStatement; and 0.75–0.95 for FamilyMemberHistory). Conclusion We demonstrated that the NLP2FHIR pipeline is feasible for modeling unstructured EHR data and integrating structured elements into the model. The outcomes of this work provide standards-based tools of clinical data normalization that is indispensable for enabling portable EHR-driven phenotyping and large-scale data analytics, as well as useful insights for future developments of the FHIR specifications with regard to handling unstructured clinical data.

scholarly journals 4521 Collecting, Interpreting and Utilizing Retrospective Clinical Data from Data Warehouses

2020 ◽  
Vol 4 (s1) ◽  
pp. 46-47
Author(s):  
Brandon Joseph Sonn ◽  
Andrew Monte
Keyword(s):  
Electronic Health Record ◽  
Clinical Data ◽  
High Performance ◽  
Clinical Effectiveness ◽  
Health Record ◽  
Data Warehouses ◽  
Chip Technology ◽  
Data Points ◽  
Study Population ◽  
Electronic Health

OBJECTIVES/GOALS: Utilizing clinical electronic health record (eHR) data pulled en masse from data warehouses provides unique challenges when applying it to retrospective studies. Use of this data in conjunction with metabolomic and genomic results to predict response to lisinopril or ondansetron has been completed. METHODS/STUDY POPULATION: Study population consists of >2000 subjects recruited from the Emergency Medicine Specimen Bank at University of Colorado Denver (UCD). All patients presenting to the emergency department are approached to participate which significantly increases demographic diversity of our study populations. Clinical data is pulled from Health Data Compass (data warehouse at UCD that collects all electronic health record (EHR) data to be able to deliver de-identified). Effectiveness of lisinopril and ondansetron were investigated using metabolomic data collected via ultra-high performance liquid chromatography mass spectrometry and genomic data from Illumina chip technology to find relevant correlations. RESULTS/ANTICIPATED RESULTS: Obtaining retrospective clinical data from data warehouses comes with significant challenges to be addressed. Verifying all clinical variables from patient EHRs is a crucial step that requires extensive quality control steps. As well, ensuring data validity, appropriateness of data points pulled as relate to the study criteria and identifying alternate EHR data points is needed. Chart review is a critical step necessary to surmount these challenges. Additionally, use of retrospective EHR data often necessitates the development of novel definitions of clinical effectiveness that can be abstracted from the EHR– such as how to determine decrease in nausea without a visual analogue scale. DISCUSSION/SIGNIFICANCE OF IMPACT: Utilizing data warehouses to deliver EHR data provides a valuable tool for completing retrospective precision medicine projects. The validation of definitions for clinical outcomes identifiable retrospectively are necessary and provide novel guidance for future studies.

scholarly journals “Big Data” and the Electronic Health Record

2014 ◽  
Vol 23 (01) ◽  
pp. 97-104 ◽  
Author(s):  
M. K. Ross ◽  
Wei Wei ◽  
L. Ohno-Machado
Keyword(s):  
Big Data ◽  
Electronic Health Record ◽  
Language Processing ◽  
Clinical Data ◽  
Improve Patient Care ◽  
Clinical Decision ◽  
Health Record ◽  
Privacy And Security ◽  
Pubmed Database ◽  
Electronic Health

Summary Objectives: Implementation of Electronic Health Record (EHR) systems continues to expand. The massive number of patient encounters results in high amounts of stored data. Transforming clinical data into knowledge to improve patient care has been the goal of biomedical informatics professionals for many decades, and this work is now increasingly recognized outside our field. In reviewing the literature for the past three years, we focus on “big data” in the context of EHR systems and we report on some examples of how secondary use of data has been put into practice. Methods: We searched PubMed database for articles from January 1, 2011 to November 1, 2013. We initiated the search with keywords related to “big data” and EHR. We identified relevant articles and additional keywords from the retrieved articles were added. Based on the new keywords, more articles were retrieved and we manually narrowed down the set utilizing predefined inclusion and exclusion criteria. Results: Our final review includes articles categorized into the themes of data mining (pharmacovigilance, phenotyping, natural language processing), data application and integration (clinical decision support, personal monitoring, social media), and privacy and security. Conclusion: The increasing adoption of EHR systems worldwide makes it possible to capture large amounts of clinical data. There is an increasing number of articles addressing the theme of “big data”, and the concepts associated with these articles vary. The next step is to transform healthcare big data into actionable knowledge.

scholarly journals Feasibility of real-time capture of routine clinical data in the electronic health record: a hospital-based, observational service-evaluation study

BMJ Open ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. e019790 ◽  
Author(s):  
Neil Bodagh ◽  
R Andrew Archbold ◽  
Roshan Weerackody ◽  
Meredith K D Hawking ◽  
Michael R Barnes ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Real Time ◽  
Clinical Data ◽  
Outpatient Clinic ◽  
Information Exchange ◽  
Evaluation Study ◽  
Service Evaluation ◽  
Health Record ◽  
Patient Reports ◽  
Electronic Health

ObjectivesThe electronic health record (EHR) is underused in the hospital setting. The aim of this service evaluation study was to respond to National Health Service (NHS) Digital’s ambition for a paperless NHS by capturing routinely collected cardiac outpatient data in the EHR to populate summary patient reports and provide a resource for audit and research.DesignA PowerForm template was developed within the Cerner EHR, for real-time entry of routine clinical data by clinicians attending a cardiac outpatient clinic. Data captured within the PowerForm automatically populated a SmartTemplate to generate a view-only report that was immediately available for the patient and for electronic transmission to the referring general practitioner (GP).ResultsDuring the first 8 months, the PowerForm template was used in 61% (360/594) of consecutive outpatient referrals increasing from 42% to 77% during the course of the study. Structured patient reports were available for immediate sharing with the referring GP using Cerner Health Information Exchange technology while electronic transmission was successfully developed in a substudy of 64 cases, with direct delivery by the NHS Data Transfer Service in 29 cases and NHS mail in the remainder. In feedback, the report’s immediate availability was considered very or extremely important by >80% of the patients and GPs who were surveyed. Both groups reported preference of the patient report to the conventional typed letter. Deidentified template data for all 360 patients were successfully captured within the Trust system, confirming availability of these routinely collected outpatient data for audit and research.ConclusionElectronic template development tailored to the requirements of a specialist outpatient clinic facilitates capture of routinely collected data within the Cerner EHR. These data can be made available for audit and research. They can also be used to enhance communication by populating structured reports for immediate delivery to patients and GPs.

scholarly journals Clinical Research Informatics and Electronic Health Record Data

2014 ◽  
Vol 23 (01) ◽  
pp. 215-223 ◽  
Author(s):  
M. M. Horvath ◽  
S. A. Rusincovitch ◽  
R. L. Richesson
Keyword(s):  
Clinical Research ◽  
Electronic Health Record ◽  
Clinical Data ◽  
Health Record ◽  
Healthcare Operations ◽  
Electronic Health Record Data ◽  
Pragmatic Clinical Trials ◽  
Clinical Research Informatics ◽  
Electronic Health ◽  
The Impact

Summary Objectives: The goal of this survey is to discuss the impact of the growing availability of electronic health record (EHR) data on the evolving field of Clinical Research Informatics (CRI), which is the union of biomedical research and informatics. Results: Major challenges for the use of EHR-derived data for research include the lack of standard methods for ensuring that data quality, completeness, and provenance are sufficient to assess the appropriateness of its use for research. Areas that need continued emphasis include methods for integrating data from heterogeneous sources, guidelines (including explicit phenotype definitions) for using these data in both pragmatic clinical trials and observational investigations, strong data governance to better understand and control quality of enterprise data, and promotion of national standards for representing and using clinical data. Conclusions: The use of EHR data has become a priority in CRI. Awareness of underlying clinical data collection processes will be essential in order to leverage these data for clinical research and patient care, and will require multi-disciplinary teams representing clinical research, informatics, and healthcare operations. Considerations for the use of EHR data provide a starting point for practical applications and a CRI research agenda, which will be facilitated by CRI’s key role in the infrastructure of a learning healthcare system.

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