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 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 Using electronic health records to inform trial feasibility in a rare autoimmune blistering skin disease in England

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
M. S. M. Persson ◽  
K. E. Harman ◽  
K. S. Thomas ◽  
J. R. Chalmers ◽  
Y. Vinogradova ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Bullous Pemphigoid ◽  
Rare Diseases ◽  
Skin Disease ◽  
Practice Research ◽  
Clinical Practice Research Datalink ◽  
Care Hospital ◽  
Health Records ◽  
People With Dementia ◽  
Electronic Health

Abstract Background Trials of novel agents are required to improve the care of patients with rare diseases, but trial feasibility may be uncertain due to concerns over insufficient patient numbers. We aimed to determine the size of the pool of potential participants in England 2015–2017 for trials in the autoimmune blistering skin disease bullous pemphigoid. Methods The size of the pool of potential participants was estimated using routinely collected healthcare data from linked primary care (Clinical Practice Research Datalink; CPRD) and secondary care (Hospital Episode Statistics; HES) databases. Thirteen consultant dermatologists were surveyed to determine the likelihood that a patient would be eligible for a trial based on the presence of cautions or contra-indications to prednisolone use. These criteria were applied to determine how they influenced the potential pool of participants. Results Extrapolated to the population of England, we would expect approximately 10,800 (point estimate 10,747; 95% CI 7191 to 17,239) new cases of bullous pemphigoid to be identified in a three-year period. For a future trial involving oral prednisolone (standard care), the application of cautions to its use as exclusion criteria would result in approximately 365 potential participants unlikely to be recruited, a further 5332 could be recruited with caution, and 5104 in whom recruitment is still possible. 11–17% of potential participants may have pre-existing dementia and require an alternative consent process. Conclusions Routinely collected electronic health records can be used to inform the feasibility of clinical trials in rare diseases, such as whether recruitment is feasible nationally and how long recruitment might take to meet recruitment targets. Future trials of bullous pemphigoid in England may use the data presented to inform trial design, including eligibility criteria and consent processes for enrolling people with dementia.

scholarly journals Electronic health records and patient registries in medical oncology departments in Spain

2021 ◽  
Author(s):  
N. Ribelles ◽  
I. Alvarez-Lopez ◽  
A. Arcusa ◽  
J. I. Chacon ◽  
J. de la Haba ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Electronic Health Records ◽  
Medical Oncology ◽  
Positive Impact ◽  
Work Organization ◽  
Free Text ◽  
Physician Patient Relationship ◽  
Patient Registries ◽  
Health Records ◽  
Electronic Health

Abstract Purpose We aimed to evaluate the current situation of electronic health records (EHRs) and patient registries in the oncology departments of hospitals in Spain. Methods This was a cross-sectional study conducted from December 2018 to September 2019. The survey was designed ad hoc by the Outcomes Evaluation and Clinical Practice Section of the Spanish Society of Medical Oncology (SEOM) and was distributed to all head of medical oncology department members of SEOM. Results We invited 148 heads of oncology departments, and 81 (54.7%) questionnaires were completed, with representation from all 17 Spanish autonomous communities. Seventy-seven (95%) of the respondents had EHRs implemented at their hospitals; of them, over 80% considered EHRs to have a positive impact on work organization and clinical practice, and 73% considered that EHRs improve the quality of patient care. In contrast, 27 (35.1%) of these respondents felt that EHRs worsened the physician–patient relationship and conveyed an additional workload (n = 29; 37.6%). Several drawbacks in the implementation of EHRs were identified, including the limited inclusion of information on both outpatients and inpatients, information recorded in free text data fields, and the availability of specific informed consent. Forty-six (56.7%) respondents had patient registries where they recorded information from all patients seen in the department. Conclusion Our study indicates that EHRs are almost universally implemented in the hospitals surveyed and are considered to have a positive impact on work organization and clinical practice. However, EHRs currently have several drawbacks that limit their use for investigational purposes. Clinical trial registration Not applicable

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