scholarly journals Sharing of clinical data in a maternity setting: How do paper hand-held records and electronic health records compare for completeness?

2014 ◽  
Vol 14 (1) ◽  
Author(s):  
Glenda Hawley ◽  
Claire Jackson ◽  
Julie Hepworth ◽  
Shelley A Wilkinson
Keyword(s):  
Electronic Health Records ◽  
Clinical Data ◽  
Health Records ◽  
Electronic Health

scholarly journals Adapting electronic health records-derived phenotypes to claims data: Lessons learned in using limited clinical data for phenotyping

2020 ◽  
Vol 102 ◽  
pp. 103363 ◽  
Author(s):  
Anna Ostropolets ◽  
Christian Reich ◽  
Patrick Ryan ◽  
Ning Shang ◽  
George Hripcsak ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Clinical Data ◽  
Claims Data ◽  
Lessons Learned ◽  
Health Records ◽  
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 FIBER: enabling flexible retrieval of electronic health records data for clinical predictive modeling

JAMIA Open ◽  
2021 ◽  
Vol 4 (3) ◽  
Author(s):  
Suparno Datta ◽  
Jan Philipp Sachs ◽  
Harry FreitasDa Cruz ◽  
Tom Martensen ◽  
Philipp Bode ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electronic Health Records ◽  
Clinical Data ◽  
Heart Surgery ◽  
Data Warehouses ◽  
Learning Models ◽  
Health Records ◽  
Star Schema ◽  
Electronic Health ◽  
Machine Learning Models

Abstract Objectives The development of clinical predictive models hinges upon the availability of comprehensive clinical data. Tapping into such resources requires considerable effort from clinicians, data scientists, and engineers. Specifically, these efforts are focused on data extraction and preprocessing steps required prior to modeling, including complex database queries. A handful of software libraries exist that can reduce this complexity by building upon data standards. However, a gap remains concerning electronic health records (EHRs) stored in star schema clinical data warehouses, an approach often adopted in practice. In this article, we introduce the FlexIBle EHR Retrieval (FIBER) tool: a Python library built on top of a star schema (i2b2) clinical data warehouse that enables flexible generation of modeling-ready cohorts as data frames. Materials and Methods FIBER was developed on top of a large-scale star schema EHR database which contains data from 8 million patients and over 120 million encounters. To illustrate FIBER’s capabilities, we present its application by building a heart surgery patient cohort with subsequent prediction of acute kidney injury (AKI) with various machine learning models. Results Using FIBER, we were able to build the heart surgery cohort (n = 12 061), identify the patients that developed AKI (n = 1005), and automatically extract relevant features (n = 774). Finally, we trained machine learning models that achieved area under the curve values of up to 0.77 for this exemplary use case. Conclusion FIBER is an open-source Python library developed for extracting information from star schema clinical data warehouses and reduces time-to-modeling, helping to streamline the clinical modeling process.

scholarly journals Conceptual Design, Implementation, and Evaluation of Generic and Standard-Compliant Data Transfer into Electronic Health Records

2020 ◽  
Vol 11 (03) ◽  
pp. 374-386 ◽  
Author(s):  
Rogério Blitz ◽  
Martin Dugas
Keyword(s):  
Clinical Practice ◽  
Electronic Health Records ◽  
Conceptual Design ◽  
Clinical Data ◽  
Data Transfer ◽  
Care Hospital ◽  
Single Source ◽  
Everyday Clinical Practice ◽  
Health Records ◽  
Electronic Health

Abstract Objectives The objective of this study is the conceptual design, implementation and evaluation of a system for generic, standard-compliant data transfer into electronic health records (EHRs). This includes patient data from clinical research and medical care that has been semantically annotated and enhanced with metadata. The implementation is based on the single-source approach. Technical and clinical feasibilities, as well as cost-benefit efficiency, were investigated in everyday clinical practice. Methods Münster University Hospital is a tertiary care hospital with 1,457 beds and 10,823 staff who treated 548,110 patients in 2018. Single-source metadata architecture transformation (SMA:T) was implemented as an extension to the EHR system. This architecture uses Model Driven Software Development (MDSD) to generate documentation forms according to the Clinical Data Interchange Standards Consortium (CDISC) operational data model (ODM). Clinical data are stored in ODM format in the EHR system database. Documentation forms are based on Google's Material Design Standard. SMA:T was used at a total of five clinics and one administrative department in the period from March 1, 2018 until March 31, 2019 in everyday clinical practice. Results The technical and clinical feasibility of SMA:T was demonstrated in the course of the study. Seventeen documentation forms including 373 data items were created with SMA:T. Those were created for 2,484 patients by 283 users in everyday clinical practice. A total of 121 documentation forms were examined retrospectively. The Constructive cost model (COCOMO II) was used to calculate cost and time savings. The form development mean time was reduced by 83.4% from 3,357 to 557 hours. Average costs per form went down from EUR 953 to 158. Conclusion Automated generic transfer of standard-compliant data and metadata into EHRs is technically and clinically feasible, cost efficient, and a useful method to establish comprehensive and semantically annotated clinical documentation. Savings of time and personnel resources are possible.

scholarly journals Visualizing Semantic Structure of a Clinical Text Document

2020 ◽  
Vol 4 (6) ◽  
Author(s):  
Jonah Kenei ◽  
Elisha Opiyo ◽  
Robert Oboko
Keyword(s):  
Electronic Health Records ◽  
Clinical Data ◽  
Semantic Structure ◽  
Cognitive Workload ◽  
Health Records ◽  
Clinical Text ◽  
Clinical Notes ◽  
Healthcare Data ◽  
Text Document ◽  
Electronic Health

The increasing use of Electronic Health Records (EHRs) in healthcare delivery settings has led to increase availability of electronic clinical data. They generate a lot of patients’ clinical data each day, requiring physicians to review them to find clinically relevant information of different patients during care episodes. The availability of electronically collected healthcare data has created the need of computational tools to analyze them. One of the types of data which doctors have access to is clinical notes that resides in electronic health records. These notes are useful as they provide comprehensive information about patients’ health histories with many practical uses. For example, doctors always review these notes during care episodes to appraise themselves about the health history of a patient. These reviews are currently manual where a doctor reads a patient’s chart while looking for specific clinical information. Without the proper support, this manual process leads to information overload and increases physician cognitive workload. Current electronic health records (EHRs) do not provide support to help physicians reduce cognitive workload when completing clinical tasks. This is especially true for long clinical documents which require quick review at the point of care. The growing amount of clinical documentation available in EHRs has arose the need of tools that support synthesize of information in EHRs. The use of visual analytics to explore healthcare data is one such research direction to address this problem. However, existing visualization techniques are mainly based on structured electronic health record and rarely support therapeutic activities. Therefore, visualization of unstructured clinical records to support clinical practice is required. In this paper we propose a unique approach for graphically representing and visualizing the semantic structure of a clinical text document to aid doctors in reviewing electronic clinical notes. A user evaluation demonstrates that the proposed method for visualizing and navigating a document’s semantic structure facilitates a user’s document information exploration.

scholarly journals Best Paper Selection

2019 ◽  
Vol 28 (01) ◽  
pp. 206-207
Keyword(s):  
Electronic Health Records ◽  
Data Quality ◽  
Data Warehouse ◽  
Clinical Data ◽  
Predictive Models ◽  
Health Records ◽  
Clinical Data Warehouse ◽  
Quality Program ◽  
Electronic Health ◽  
Combining Information

Brisimi TS, Chen R, Mela T, Olshevsky A, Paschalidis IC, Shi W. Federated learning of predictive models from federated Electronic Health Records. Int J Med Inform 2018 Apr;112:59-67 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5836813/ Daniel C, Serre P, Orlova N, Bréant S, Paris N, Griffon N. Initializing a hospital-wide data quality program. The AP-HP experience. Comput Methods Programs Biomed 2018 Nov 9 https://www.sciencedirect.com/science/article/pii/S0169260718306242?via%3Dihub Estiri H, Stephens KA, Klann JG, Murphy SN. Exploring completeness in clinical data research networks with DQe-c. J Am Med Inform Assoc 2018 Jan 1;25(1):17-24 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6481389/ Sylvestre E, Bouzillé G, Chazard E, His-Mahier C, Riou C, Cuggia M. Combining information from a clinical data warehouse and a pharmaceutical database to generate a framework to detect comorbidities in electronic health records. BMC Med Inform Decis Mak 2018 Jan 24;18(1):9 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5784648/

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