scholarly journals Analysis of Requirements for the Medication Profile to Be Used in Clinical Research: Protocol Feasibility Studies and Patient Recruitment

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Julie M. James ◽  
Dipak Kalra ◽  
Jane Portlock
Keyword(s):  
Clinical Research ◽  
Feasibility Studies ◽  
Patient Recruitment ◽  
Eligibility Criteria ◽  
Related Data ◽  
Electronic Health ◽  
Data Elements ◽  
Electronic Health Information ◽  
Core Part ◽  
Research Domain

A “Medication Profile,” the information about the medicines a person is using and has used, is a core part of many electronic health record systems and summaries. However, there is little objective research into the data elements that the profile should contain to support the uses it must serve. With the increasing emphasis on secondary uses of electronic health information, as well as supporting the requirements to support direct to patient care, the Medication Profile should also support the requirements from clinical research. However, there is little, if any, description of these available. This paper describes an analysis of a set of study eligibility criteria that was undertaken to investigate which medication-related data elements would be required to support two clinical research use cases: the parameters to query a patient’s Medication Profile to assess their suitability for entry into a trial (patient recruitment) and the parameters to query a set of Medication Profiles in a data warehouse to assess whether the eligibility criteria as described would yield a reasonable cohort of patients as potential subjects (protocol feasibility). These medication-related data elements then become information requirements that a Medication Profile should ideally meet, in order to be able to support these two uses in the clinical research domain.

scholarly journals Leveraging Real-World Data for the Selection of Relevant Eligibility Criteria for the Implementation of Electronic Recruitment Support in Clinical Trials

2021 ◽  
Vol 12 (01) ◽  
pp. 017-026
Author(s):  
Georg Melzer ◽  
Tim Maiwald ◽  
Hans-Ulrich Prokosch ◽  
Thomas Ganslandt
Keyword(s):  
Data Warehouse ◽  
Real World ◽  
Chart Review ◽  
Patient Recruitment ◽  
Real World Data ◽  
Eligibility Criteria ◽  
Health Records ◽  
World Data ◽  
Paper Chart ◽  
Data Elements

Abstract Background Even though clinical trials are indispensable for medical research, they are frequently impaired by delayed or incomplete patient recruitment, resulting in cost overruns or aborted studies. Study protocols based on real-world data with precisely expressed eligibility criteria and realistic cohort estimations are crucial for successful study execution. The increasing availability of routine clinical data in electronic health records (EHRs) provides the opportunity to also support patient recruitment during the prescreening phase. While solutions for electronic recruitment support have been published, to our knowledge, no method for the prioritization of eligibility criteria in this context has been explored. Methods In the context of the Electronic Health Records for Clinical Research (EHR4CR) project, we examined the eligibility criteria of the KATHERINE trial. Criteria were extracted from the study protocol, deduplicated, and decomposed. A paper chart review and data warehouse query were executed to retrieve clinical data for the resulting set of simplified criteria separately from both sources. Criteria were scored according to disease specificity, data availability, and discriminatory power based on their content and the clinical dataset. Results The study protocol contained 35 eligibility criteria, which after simplification yielded 70 atomic criteria. For a cohort of 106 patients with breast cancer and neoadjuvant treatment, 47.9% of data elements were captured through paper chart review, with the data warehouse query yielding 26.9% of data elements. Score application resulted in a prioritized subset of 17 criteria, which yielded a sensitivity of 1.00 and specificity 0.57 on EHR data (paper charts, 1.00 and 0.80) compared with actual recruitment in the trial. Conclusion It is possible to prioritize clinical trial eligibility criteria based on real-world data to optimize prescreening of patients on a selected subset of relevant and available criteria and reduce implementation efforts for recruitment support. The performance could be further improved by increasing EHR data coverage.

scholarly journals Development of an electronic health records datamart to support clinical and population health research

2020 ◽  
pp. 1-6
Author(s):  
Jillian H. Hurst ◽  
Yaxing Liu ◽  
Pamela J. Maxson ◽  
Sallie R. Permar ◽  
L. Ebony Boulware ◽  
...  
Keyword(s):  
Clinical Research ◽  
Population Health ◽  
Contextual Information ◽  
Data Resource ◽  
Level Data ◽  
Duke University ◽  
Electronic Health ◽  
Data Tables ◽  
Data Elements ◽  
Future Work

Abstract Introduction: Electronic health record (EHR) data have emerged as an important resource for population health and clinical research. There have been significant efforts to leverage EHR data for research; however, given data security concerns and the complexity of the data, EHR data are frequently difficult to access and use for clinical studies. We describe the development of a Clinical Research Datamart (CRDM) that was developed to provide well-curated and easily accessible EHR data to Duke University investigators. Methods: The CRDM was designed to (1) contain most of the patient-level data elements needed for research studies; (2) be directly accessible by individuals conducting statistical analyses (including Biostatistics, Epidemiology, and Research Design (BERD) core members); (3) be queried via a code-based system to promote reproducibility and consistency across studies; and (4) utilize a secure protected analytic workspace in which sensitive EHR data can be stored and analyzed. The CRDM utilizes data transformed for the PCORnet data network, and was augmented with additional data tables containing site-specific data elements to provide additional contextual information. Results: We provide descriptions of ideal use cases and discuss dissemination and evaluation methods, including future work to expand the user base and track the use and impact of this data resource. Conclusions: The CRDM utilizes resources developed as part of the Clinical and Translational Science Awards (CTSAs) program and could be replicated by other institutions with CTSAs.

scholarly journals 2507

2017 ◽  
Vol 1 (S1) ◽  
pp. 20-20
Author(s):  
Ram Gouripeddi ◽  
Elizabeth Lane ◽  
Randy Madsen ◽  
Ryan Butcher ◽  
Bernie LaSalle ◽  
...  
Keyword(s):  
Back Pain ◽  
Clinical Research ◽  
Patient Record ◽  
Patient Records ◽  
User Centered Design ◽  
Clinical Workflow ◽  
Eligibility Criteria ◽  
Study Population ◽  
Data Elements ◽  
Research Studies

OBJECTIVES/SPECIFIC AIMS: Issues with recruiting the targeted number of participants in a timely manner often results in underpowered studies, with more than 60% of clinical studies failing to complete or requiring extensions due to enrollment issues. The objective of this study is to develop and implement a scalable, organization wide platform to enhance accrual into clinical research studies. METHODS/STUDY POPULATION: We are developing and evaluating an informatics platform called Utah Utility for Research Recruitment (U2R2). U2R2 consists of 2 components: (i) Semantic Matcher: an automated trial criterion to patient matching component that also reports uncertainty associated with the match, and (ii) Match Delivery: mechanisms to deliver the list of matched patients for different research and clinical settings. As a first step, we limited the Semantic Matcher to utilize only structured data elements from the patient record and trial criteria. We are now including distributional semantic methods to match complete patient records and trial criteria as documents. We evaluated the first phase of U2R2 based on a randomized trial with a target enrollment of 220 participants that compares 2 treatment strategies for managing back pain (physical therapy and usual care) for individuals consulting a nonsurgical provider and symptomatic <90 days. RESULTS/ANTICIPATED RESULTS: U2R2 identified 9370 patients from the University of Utah Hospitals and Clinics as potential matches. Of these 9370, 1145 responded to the Back Pain study research team’s email or phone communications, and were further screened by phone. In total, 250 participants completed a screening visit, resulting in the current study enrollment of 130 participants. Forty-three of 1145 patients refused to participate, and 50 participants no-showed their screening visit. DISCUSSION/SIGNIFICANCE OF IMPACT: A recruitment platform can enhance potential participant identification, but requires attention to multiple issues involved with clinical research studies. Clinical eligibility criteria are usually unstructured and require human mediation and abstraction into discrete data elements for matching against patient records. In addition, key eligibility data are often embedded within text in the patient record. Distributional semantic approaches, by leveraging this content, can identify potential participants for screening with more specificity. The delivery of the list of matched patient results should consider characteristics of the research study, population, and targeted enrollment (eg, back pain being a common disorder and the possibility of the patient visiting different types of clinics), as well as organizational and socio-technical issues surrounding clinical practice and research. Embedding the delivery of match results into the clinical workflow by utilizing user-centered design approaches and involving the clinician, the clinic, and the patient in the recruitment process, could yield higher accrual indices.

Frequency Analysis of Medical Concepts in Clinical Trials and their Coverage in MeSH and SNOMED-CT

2015 ◽  
Vol 54 (01) ◽  
pp. 83-92 ◽  
Author(s):  
M. Dugas ◽  
J. Varghese
Keyword(s):  
Clinical Trials ◽  
Clinical Research ◽  
Significance Test ◽  
University Hospital ◽  
Free Text ◽  
Snomed Ct ◽  
Eligibility Criteria ◽  
Unified Medical Language System ◽  
Data Elements ◽  
Medical Concepts

Summary Background: Eligibility criteria (EC) of clinical trials play a key role in selecting appropriate study candidates and the validity of the outcome of a clinical trial. However, in most cases EC are provided in unstandardised ways such as free text, which raises significant challenges for machine-readability. Objectives: To establish a list of most frequent medical concepts in clinical trials with semantic annotations. This concept list contributes to standardisation of EC and identifies relevant data items in electronic health records (EHRs) for clinical research. The coverage of the list in two major clinical vocabularies, MeSH and SNOMED-CT, will be assessed. Methods: Four hundred and twenty-fivec linical trials conducted between 2000 and 2011 at a German university hospital were analysed. 6671 EC were manually annotated by a medical coder using Concept Unique Identifiers (CUIs) provided by the Unified Medical Language System. Two physicians performed a semi-automatic CUI code revision. Concept frequency was analysed and clusters of concepts were manually identified.A binomial significance test was applied to quantify coverage differences of the most frequent concepts in MeSH and SNOMED-CT. Results: Based on manual medical coding of 425 clinical trials, 7588 concepts were identified, of which 5236 were distinct. A top 100 list containing 101 most frequent medical concepts was established. The concepts of this list cover 25 % of all concept occur-rences in all analysed clinical trials. This list reveals six missing entries in SNOMED-CT, 12 in MeSH. The median of EC frequency per trial has increased throughout the trial years (2000 –2005: 8 EC/trial, 2011: 14 EC/ trial). Conclusions: Relatively few concepts cover one quarter of concept occurrences that represent EC in recent studies. Therefore, these concepts can serve as candidate data elements for integration into EHRs to optimise patient recruitment in clinical research.

Clinical Research Networks for Neurological Emergencies: An Evidentiary Review

2009 ◽  
Vol 2 (1) ◽  
pp. 27-32
Author(s):  
Andrew Storer
Keyword(s):  
Clinical Research ◽  
Research Infrastructure ◽  
Patient Recruitment ◽  
Research Networks ◽  
Neurological Emergencies

Consensus groups believe that clinical research networks are a more effective method of conducting clinical research than stand-alone sites. For example, clinical research networks have increased patient recruitment, decreased financial overhead, and allowed for coordinated research efforts, resulting in decreased duplication within high-cost research infrastructure. To date, there is little evidence describing the benefits and effectiveness of clinical research networks.

scholarly journals Gender disparity between authors in leading medical journals during the COVID-19 pandemic: a cross-sectional review

BMJ Open ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. e051224
Author(s):  
Vaidehi Misra ◽  
Frozan Safi ◽  
Kathryn A Brewerton ◽  
Wei Wu ◽  
Robin Mason ◽  
...  
Keyword(s):  
Data Extraction ◽  
Gender Disparity ◽  
High Impact ◽  
Original Research ◽  
Eligibility Criteria ◽  
Cross Sectional ◽  
Medical Journals ◽  
The Usa ◽  
Study Characteristics ◽  
Data Elements

ObjectivesEvaluate gender differences in authorship of COVID-19 articles in high-impact medical journals compared with other topics.DesignCross-sectional review.Data sourcesMedline database.Eligibility criteriaArticles published from 1 January to 31 December 2020 in the seven leading general medical journals by impact factor. Article types included primary research, reviews, editorials and commentaries.Data extractionKey data elements were whether the study topic was related to COVID-19 and names of the principal and the senior authors. A hierarchical approach was used to determine the likely gender of authors. Logistic regression assessed the association of study characteristics, including COVID-19 status, with authors’ likely gender; this was quantified using adjusted ORs (aORs).ResultsWe included 2252 articles, of which 748 (33.2%) were COVID-19-related and 1504 (66.8%) covered other topics. A likely gender was determined for 2138 (94.9%) principal authors and 1890 (83.9%) senior authors. Men were significantly more likely to be both principal (1364 men; 63.8%) and senior (1332 men; 70.5%) authors. COVID-19-related articles were not associated with the odds of men being principal (aOR 0.99; 95% CI 0.81 to 1.21; p=0.89) or senior authors (aOR 0.96; 95% CI 0.78 to 1.19; p=0.71) relative to other topics. Articles with men as senior authors were more likely to have men as principal authors (aOR 1.49; 95% CI 1.21 to 1.83; p<0.001). Men were more likely to author articles reporting original research and those with corresponding authors based outside the USA and Europe.ConclusionsWomen were substantially under-represented as authors among articles in leading medical journals; this was not significantly different for COVID-19-related articles. Study limitations include potential for misclassification bias due to the name-based analysis. Results suggest that barriers to women’s authorship in high-impact journals during COVID-19 are not significantly larger than barriers that preceded the pandemic and that are likely to continue beyond it.PROSPERO registration numberCRD42020186702.

Patient and clinician perceptions of the immediate release of electronic health information

2021 ◽  
Author(s):  
Laura D. Leonard ◽  
Ben Himelhoch ◽  
Victoria Huynh ◽  
Dulcy Wolverton ◽  
Kshama Jaiswal ◽  
...  
Keyword(s):  
Health Information ◽  
Immediate Release ◽  
Electronic Health ◽  
Electronic Health Information

A Framework for Systematic Assessment of Clinical Trial Population Representativeness Using Electronic Health Records Data

2021 ◽  
Vol 12 (04) ◽  
pp. 816-825
Author(s):  
Yingcheng Sun ◽  
Alex Butler ◽  
Ibrahim Diallo ◽  
Jae Hyun Kim ◽  
Casey Ta ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Electronic Health Records ◽  
The United States ◽  
Design Stage ◽  
Common Data Model ◽  
Free Text ◽  
Eligibility Criteria ◽  
Health Records ◽  
Electronic Health

Abstract Background Clinical trials are the gold standard for generating robust medical evidence, but clinical trial results often raise generalizability concerns, which can be attributed to the lack of population representativeness. The electronic health records (EHRs) data are useful for estimating the population representativeness of clinical trial study population. Objectives This research aims to estimate the population representativeness of clinical trials systematically using EHR data during the early design stage. Methods We present an end-to-end analytical framework for transforming free-text clinical trial eligibility criteria into executable database queries conformant with the Observational Medical Outcomes Partnership Common Data Model and for systematically quantifying the population representativeness for each clinical trial. Results We calculated the population representativeness of 782 novel coronavirus disease 2019 (COVID-19) trials and 3,827 type 2 diabetes mellitus (T2DM) trials in the United States respectively using this framework. With the use of overly restrictive eligibility criteria, 85.7% of the COVID-19 trials and 30.1% of T2DM trials had poor population representativeness. Conclusion This research demonstrates the potential of using the EHR data to assess the clinical trials population representativeness, providing data-driven metrics to inform the selection and optimization of eligibility criteria.

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