scholarly journals An OpenEHR Template with the Integrated German LOINC Terms

2021 ◽  
Author(s):  
Abdul-Mateen Rajput ◽  
Samer Alkarkoukly
Keyword(s):  
Clinical Data ◽  
Patient Data ◽  
Data Capture ◽  
German Language ◽  
Independent Laboratory ◽  
Laboratory Form

An OpenEHR template based on LOINC terms in German language (LOINC-DE) has been created for the structured clinical data capture. The resulting template includes all terms available in LOINC-DE, which can be selected from the drop-down menu for clinical data capture. The template can be used as an independent laboratory form or it can be customized for local needs. This approach presents the possibility to include terminologies in EHR when capturing patient data.

On Educating about Medical Data Management

2007 ◽  
Vol 46 (01) ◽  
pp. 74-79 ◽  
Author(s):  
P. Knaup ◽  
F. Leiner ◽  
R. Haux
Keyword(s):  
Data Management ◽  
Clinical Data ◽  
Medical Data ◽  
Patient Data ◽  
Management Systems ◽  
Multiple Use ◽  
Data Management Systems ◽  
Medical Coding ◽  
Medical Data Management ◽  
Coding Systems

Summary Objectives: To summarize background, challenges, objectives, and methods for the usability of patient data, in particular with respect to their multiple use, and to point out how to lecture medical data management. Methods: Analyzing the literature, providing an example based on Simpson’s paradox and summarizing research and education in the field of medical data management, respectively health information management (in German: Medizinische Dokumentation). Results: For the multiple use of patient data, three main categories of use can be identified: patientoriented (or casuistic) analysis, patient-group reporting, and analysis for clinical studies. A so-called documentation protocol, related to study plans in clinical trials, supports the multiple use of data from the electronic health record in order to obtain valid, interpretable results. Lectures on medical data management may contain modules on introduction, basic concepts of clinical data management and coding systems, important medical coding systems (e.g. ICD, SNOMED, TNM, UMLS), typical medical documentation systems (e.g. on patient records, clinical and epidemiological registers), utilization of clinical data management systems, planning of medical coding systems and of clinical data management systems, hospital information systems and the electronic patient record, and on data management in clinical studies. Conclusion: Usability, the ultimate goal of recording and managing patient data, requires, besides technical considerations, in addition appropriate methodology on medical data management, especially if data is intended to be used for multiple purposes, e.g. for patient care and quality management and clinical research. Medical data management should be taught in health and biomedical informatics programs.

scholarly journals Differentiation of Clinical Patterns Associated With Rhinologic Disease

2020 ◽  
pp. 194589242094170
Author(s):  
Sean M. Parsel ◽  
Charles A. Riley ◽  
Cameron A. Todd ◽  
Andrew J. Thomas ◽  
Edward D. McCoul
Keyword(s):  
Chronic Rhinosinusitis ◽  
Clinical Data ◽  
Nasal Polyposis ◽  
Hierarchical Cluster ◽  
Patient Data ◽  
Diagnostic Efficiency ◽  
Cross Sectional ◽  
Low Frequencies ◽  
Patient Reported ◽  
Snot 22

Background Common rhinologic diagnoses have similar presentations with a varying degree of overlap. Patterns may exist within clinical data that can be useful for early diagnosis and predicting outcomes. Objective To explore the feasibility of artificial intelligence to differentiate patterns in patient data in order to develop clinically-meaningful diagnostic groups. Methods A cross-sectional study of prospectively-acquired patient data at a tertiary rhinology clinic was performed. Data extracted included objective findings on nasal endoscopy, patient reported quality of life (PRQOL) instrument ratings, peripheral eosinophil fraction, and past medical history. Unsupervised non-hierarchical cluster analysis was performed to discover patterns in the data using 22 input variables. Results A total of 545 patients were analyzed after application of inclusion and exclusion criteria yielding 7 unique patient clusters, highly dependent on PRQOL scores and demographics. The clusters were clinically-relevant with distinct characteristics. Chronic rhinosinusitis without nasal polyposis (CRSsNP) was associated with two clusters having low frequencies of asthma and low eosinophil fractions. Chronic rhinosinusitis with nasal polyposis (CRSwNP) was associated with high frequency of asthma, mean (standard deviation [SD]) NOSE scores of 66 (19) and SNOT-22 scores of 41 (15), and high eosinophil fractions. AR was present in multiple clusters. RARS was associated with the youngest population with mean (SD) NOSE score of 54 (23) and SNOT-22 score of 41 (19). Conclusion Broader consideration of initially available clinical data may improve diagnostic efficiency for rhinologic conditions without ancillary studies, using computer-driven algorithms. PRQOL scores and demographic information appeared to be useful adjuncts, with associations to diagnoses in this pilot study.

A multidisciplinary-guided digital solution to data capture in early-phase clinical trials.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e18063-e18063
Author(s):  
Donna M. Graham ◽  
Joanna Clarke ◽  
Gemma Wickert ◽  
Leanna Goodwin ◽  
Carla Timmins ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Focus Group ◽  
Data Collection ◽  
Early Phase ◽  
Patient Data ◽  
Simulated Patient ◽  
Data Capture ◽  
Health Staff ◽  
Case Report Form ◽  
Acceptance Test

e18063 Background: Data capture in early phase cancer clinical trials (EPCCT) is usually via paper records with manual transcription to the sponsor’s case report form. Capturing real time trial data directly to computer (eSource) may reduce errors and increase completeness and timeliness of data entry. A simulated system pilot took place between Oct 2018 and Jan 2019 at an EPCCT facility to appraise Foundry Health’s eSource system “ClinSpark”. Aims were to assess consistency and effectiveness of creating electronic templates for source data capture and live data collection compliance. Methods: A multidisciplinary focus group (2 research nurses, 1 doctor, 3 data managers) was created to collaborate with Foundry Health staff. The focus group agreed on a 52 item user acceptance test listing ideal features for a data collection tool, classifying items as high, medium or low priority. Specialised features of the eSource system were adapted to handle the complex needs of EPCCT. The pilot incorporated a 5 day boot camp for familiarisation to the digital platform; a conference room test using simulated patient data; construction of a trial template including contingency planning; and a clinic floor test with live simulated patient data collection using digital tablets. Results: During the 3 month pilot, templates for 2 EPCCT were planned and created. Using eSource, 43 items (83%) of the acceptance test were passed compared with 27 items (52%) for the current (paper-based) system. The paper system did not pass any of the 9 items for which eSource failed. For the 30 high priority items, eSource passed 30 (100%) compared with 22 for the paper system (73%). Time saving and potential error reduction were noted as additional benefits. Conclusions: This process demonstrates that a multidisciplinary approach can be used to successfully integrate a customised eSource system working with previously untrained staff. Improved performance across pre-specified domains and potential additional benefits were noted. As FDA encourages the use of digital solutions in clinical trials, using eSource provides a potential solution for compliant and efficient capture of data from protocol assessments at investigator sites and rapid data transfer to sponsors.

Computerized Management of Clinical Trials and Patient Data: The Clinical Data Manager

1987 ◽  
Vol 5 (4) ◽  
pp. 203-230
Author(s):  
Israel M. Stein ◽  
Crystal Sloan ◽  
Chester King ◽  
Edna Shattuck ◽  
Daniel Doxtader
Keyword(s):  
Clinical Trials ◽  
Clinical Data ◽  
Patient Data ◽  
Data Manager

Validation of the Oral Survey-B System for Electronic Data Capture in National Oral Health Surveys

2016 ◽  
Vol 50 (3) ◽  
pp. 288-294 ◽  
Author(s):  
J.C. Carvalho ◽  
D. Declerck ◽  
E. De Vos ◽  
J. Kellen ◽  
J.P. Van Nieuwenhuysen ◽  
...  
Keyword(s):  
Oral Health ◽  
Clinical Data ◽  
Health Surveys ◽  
Interrater Reliability ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Validation Process ◽  
Electronic Data ◽  
Kappa Value ◽  
Time Savings

The aims of the present study were to incorporate and to validate the electronic capture of participant-related outcomes into the Oral Survey-B System, which was originally developed for the electronic capture of clinical data. The validation process compared the performances of electronic and handwritten data captures. The hypothesis of noninferiority would be established if participants performed electronic data capture of the questionnaire survey with an effectiveness of at least 95% of that of handwritten data capture. In this multicenter, randomized, one-period crossover study design, participants (n = 261) were allocated to start with either electronic or handwritten data capture. The incorporation of the electronic self-completed questionnaire into the Oral Survey-B System was successful. The validation of the electronic questionnaire was performed by participants aged from 18 to 75 years. The interrater reliability of participants performing electronic and handwritten data capture of nonclinical assessments per questionnaire and per entry showed a kappa value of 0.72 (95% CI: 0.53-0.94). The noninferiority of electronic data capture in relation to that of the handwritten data capture and transfer was shown (p < 0.0001; 95% CI: 1.47-2.99). In conclusion, the electronic capture of participant-related outcomes with the Oral Survey-B System, originally designed for capture of clinical data, was validated. The electronic data capture was accurate and limited the number of errors. The participants were able to perform electronic data capture effectively, supporting its implementation in further National Oral Health Surveys. With the consideration of participant preference and time savings, this could lead to the implementation of electronic data capture worldwide in National Oral Health Surveys.

scholarly journals Core Data Elements in Acute Myeloid Leukemia: A Unified Medical Language System–Based Semantic Analysis and Experts’ Review (Preprint)

2019 ◽  
Author(s):  
Christian Holz ◽  
Torsten Kessler ◽  
Martin Dugas ◽  
Julian Varghese
Keyword(s):  
Acute Myeloid Leukemia ◽  
Clinical Data ◽  
Myeloid Leukemia ◽  
Data Capture ◽  
Large Set ◽  
Unified Medical Language System ◽  
Medical Language ◽  
Data Elements ◽  
Medical Concepts ◽  
Acute Myeloid

BACKGROUND For cancer domains such as acute myeloid leukemia (AML), a large set of data elements is obtained from different institutions with heterogeneous data definitions within one patient course. The lack of clinical data harmonization impedes cross-institutional electronic data exchange and future meta-analyses. OBJECTIVE This study aimed to identify and harmonize a semantic core of common data elements (CDEs) in clinical routine and research documentation, based on a systematic metadata analysis of existing documentation models. METHODS Lists of relevant data items were collected and reviewed by hematologists from two university hospitals regarding routine documentation and several case report forms of clinical trials for AML. In addition, existing registries and international recommendations were included. Data items were coded to medical concepts via the Unified Medical Language System (UMLS) by a physician and reviewed by another physician. On the basis of the coded concepts, the data sources were analyzed for concept overlaps and identification of most frequent concepts. The most frequent concepts were then implemented as data elements in the standardized format of the Operational Data Model by the Clinical Data Interchange Standards Consortium. RESULTS A total of 3265 medical concepts were identified, of which 1414 were unique. Among the 1414 unique medical concepts, the 50 most frequent ones cover 26.98% of all concept occurrences within the collected AML documentation. The top 100 concepts represent 39.48% of all concepts’ occurrences. Implementation of CDEs is available on a European research infrastructure and can be downloaded in different formats for reuse in different electronic data capture systems. CONCLUSIONS Information management is a complex process for research-intense disease entities as AML that is associated with a large set of lab-based diagnostics and different treatment options. Our systematic UMLS-based analysis revealed the existence of a core data set and an exemplary reusable implementation for harmonized data capture is available on an established metadata repository.

scholarly journals CLINICAL DATA MANAGEMENT IMPORTANCE IN CLINICAL RESEARCH

2016 ◽  
pp. 59
Author(s):  
Deepa Murugesan ◽  
Ranganath Banerjee ◽  
Gopal Ramesh Kumar
Keyword(s):  
Clinical Trial ◽  
Data Management ◽  
Clinical Data ◽  
Clinical Trial Data ◽  
Drug Approval ◽  
Trial Data ◽  
Data Capture ◽  
Data Management System ◽  
Pharmaceutical Companies ◽  
Management Tools

<p>ABSTRACT<br />Over the last few decades, most of the pharmaceutical companies and research sponsors are facing a lot of challenges in clinical research for their<br />new drug approval. The sponsor research needs a high-quality data report for getting new drug approval from Food and Drug Administration for their<br />medical products. Clinical trial data are important for the drug and medical device development processing pharmaceutical companies to examine<br />and evaluate the efficacy and safety of the new medical product in human volunteers. The results of the clinical trial studies generate the most<br />valuable data and in recent years; there has been massive development in the field of clinical trials. A good clinical data management system reduces<br />the duration of the study and cost of drug development. Further a well-designed case report form (CRF) assists data collection and make facilitates<br />data management and statistical analysis. Nowadays, the electronic data capture (EDC) is very beneficial in data collection. EDC helps to speed up the<br />clinical trial process and reduces the duration, errors and make the work easy in the data management system. This article highlights the importance<br />of data management processes involved in the clinical trial and provides an overview of the clinical trial data management tools. The study concluded<br />that data management tools play a key role in the clinical trial and well-designed CRFs reduces the errors and save the time of the clinical trials and<br />facilitates the drug discovery and development.<br />Keywords: Pharmaceutical, Clinical trial, Clinical data management, Data capture.</p>

scholarly journals A generic concept for the development of model-guided clinical decision support systems

2015 ◽  
Vol 1 (1) ◽  
pp. 322-326
Author(s):  
Kerstin Denecke ◽  
Claire Chalopin
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Clinical Data ◽  
Clinical Decision Making ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Patient Data ◽  
Biological Knowledge ◽  
Biological Processes ◽  
Data Types

AbstractDisease development and progression are very complex processes which make clinical decision making non-trivial. On the one hand, examination results that are stored in multiple formats and data types in clinical information systems need to be considered. Beyond, biological or molecular-biological processes can influence clinical decision making. So far, biological knowledge and patient data is separated from each other. This complicates inclusion of all relevant knowledge and information into the decision making. In this paper, we describe a concept of model-based decision support that links knowledge about biological processes, treatment decisions and clinical data. It consists of three models: 1) a biological model, 2) a decision model encompassing medical knowledge about the treatment workflow and decision parameters, and 3) a patient data model generated from clinical data. Requirements and future steps for realizing the concept will be presented and it will be shown how the concept can support the clinical decision making.

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