scholarly journals Clinical Research Informatics

2020 ◽  
Vol 29 (01) ◽  
pp. 203-207
Author(s):  
Christel Daniel ◽  
Dipak Kalra ◽  
Keyword(s):  
Clinical Research ◽  
Real World ◽  
Reporting System ◽  
Quality Data ◽  
Editorial Team ◽  
Common Data Model ◽  
Free Text ◽  
Real World Data ◽  
World Data ◽  
Clinical Research Informatics

Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2019. Method: A bibliographic search using a combination of MeSH descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting between the two section editors and the editorial team was organized to finally conclude on the selected three best papers. Results: Among the 517 papers, published in 2019, returned by the search, that were in the scope of the various areas of CRI, the full review process selected three best papers. The first best paper describes the use of a homomorphic encryption technique to enable federated analysis of real-world data while complying more easily with data protection requirements. The authors of the second best paper demonstrate the evidence value of federated data networks reporting a large real world data study related to the first line treatment for hypertension. The third best paper reports the migration of the US Food and Drug Administration (FDA) adverse event reporting system database to the OMOP common data model. This work opens the combined analysis of both spontaneous reporting system and electronic health record (EHR) data for pharmacovigilance. Conclusions: The most significant research efforts in the CRI field are currently focusing on real world evidence generation and especially the reuse of EHR data. With the progress achieved this year in the areas of phenotyping, data integration, semantic interoperability, and data quality assessment, real world data is becoming more accessible and reusable. High quality data sets are key assets not only for large scale observational studies or for changing the way clinical trials are conducted but also for developing or evaluating artificial intelligence algorithms guiding clinical decision for more personalized care. And lastly, security and confidentiality, ethical and regulatory issues, and more generally speaking data governance are still active research areas this year.

scholarly journals Key Contributions in Clinical Research Informatics

2021 ◽  
Vol 30 (01) ◽  
pp. 233-238
Author(s):  
Christel Daniel ◽  
Ali Bellamine ◽  
Dipak Kalra ◽  
Keyword(s):  
Clinical Research ◽  
Large Scale ◽  
Data Science ◽  
Adverse Drug Events ◽  
Quality Data ◽  
Generative Adversarial Networks ◽  
Editorial Team ◽  
Free Text ◽  
Clinical Research Informatics ◽  
Electronic Health

Summary Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2020. Method: A bibliographic search using a combination of Medical Subject Headings (MeSH) descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting between two section editors and the editorial team was organized to finally conclude on the selected four best papers. Results: Among the 877 papers published in 2020 and returned by the search, there were four best papers selected. The first best paper describes a method for mining temporal sequences from clinical documents to infer disease trajectories and enhancing high-throughput phenotyping. The authors of the second best paper demonstrate that the generation of synthetic Electronic Health Record (EHR) data through Generative Adversarial Networks (GANs) could be substantially improved by more appropriate training and evaluation criteria. The third best paper offers an efficient advance on methods to detect adverse drug events by computer-assisting expert reviewers with annotated candidate mentions in clinical documents. The large-scale data quality assessment study reported by the fourth best paper has clinical research informatics implications, in terms of the trustworthiness of inferences made from analysing electronic health records. Conclusions: The most significant research efforts in the CRI field are currently focusing on data science with active research in the development and evaluation of Artificial Intelligence/Machine Learning (AI/ML) algorithms based on ever more intensive use of real-world data and especially EHR real or synthetic data. A major lesson that the coronavirus disease 2019 (COVID-19) pandemic has already taught the scientific CRI community is that timely international high-quality data-sharing and collaborative data analysis is absolutely vital to inform policy decisions.

scholarly journals Gap between real-world data and clinical research within hospitals in China: a qualitative study

BMJ Open ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. e038375
Author(s):  
Feifei Jin ◽  
Chen Yao ◽  
Xiaoyan Yan ◽  
Chongya Dong ◽  
Junkai Lai ◽  
...  
Keyword(s):  
Qualitative Study ◽  
Clinical Research ◽  
Real World ◽  
Data Standards ◽  
Medical Record Data ◽  
Real World Data ◽  
World Data ◽  
Electronic Medical Record Data ◽  
Research Platform ◽  
Record Data

ObjectiveTo investigate the gap between real-world data and clinical research initiated by doctors in China, explore the potential reasons for this gap and collect different stakeholders’ suggestions.DesignThis qualitative study involved three types of hospital personnel based on three interview outlines. The data analysis was performed using the constructivist grounded theory analysis process.SettingSix tertiary hospitals (three general hospitals and three specialised hospitals) in Beijing, China, were included.ParticipantsIn total, 42 doctors from 12 departments, 5 information technology managers and 4 clinical managers were interviewed through stratified purposive sampling.ResultsElectronic medical record data cannot be directly downloaded into clinical research files, which is a major problem in China. The lack of data interoperability, unstructured electronic medical record data and concerns regarding data security create a gap between real-world data and research data. Updating hospital information systems, promoting data standards and establishing an independent clinical research platform may be feasible suggestions for solving the current problems.ConclusionsDetermining the causes of gaps and targeted solutions could contribute to the development of clinical research in China. This research suggests that updating the hospital information system, promoting data standards and establishing a clinical research platform could promote the use of real-world data in the future.

scholarly journals Blockchain-Authenticated Sharing of Genomic and Clinical Outcomes Data of Patients With Cancer: A Prospective Cohort Study

10.2196/16810 ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. e16810 ◽  
Author(s):  
Benjamin Scott Glicksberg ◽  
Shohei Burns ◽  
Rob Currie ◽  
Ann Griffin ◽  
Zhen Jane Wang ◽  
...  
Keyword(s):  
Real World ◽  
Web Application ◽  
Data Extraction ◽  
Data Representation ◽  
Health Data ◽  
Common Data Model ◽  
Patient Privacy ◽  
Real World Data ◽  
World Data ◽  
Patients With Cancer

Background Efficiently sharing health data produced during standard care could dramatically accelerate progress in cancer treatments, but various barriers make this difficult. Not sharing these data to ensure patient privacy is at the cost of little to no learning from real-world data produced during cancer care. Furthermore, recent research has demonstrated a willingness of patients with cancer to share their treatment experiences to fuel research, despite potential risks to privacy. Objective The objective of this study was to design, pilot, and release a decentralized, scalable, efficient, economical, and secure strategy for the dissemination of deidentified clinical and genomic data with a focus on late-stage cancer. Methods We created and piloted a blockchain-authenticated system to enable secure sharing of deidentified patient data derived from standard of care imaging, genomic testing, and electronic health records (EHRs), called the Cancer Gene Trust (CGT). We prospectively consented and collected data for a pilot cohort (N=18), which we uploaded to the CGT. EHR data were extracted from both a hospital cancer registry and a common data model (CDM) format to identify optimal data extraction and dissemination practices. Specifically, we scored and compared the level of completeness between two EHR data extraction formats against the gold standard source documentation for patients with available data (n=17). Results Although the total completeness scores were greater for the registry reports than those for the CDM, this difference was not statistically significant. We did find that some specific data fields, such as histology site, were better captured using the registry reports, which can be used to improve the continually adapting CDM. In terms of the overall pilot study, we found that CGT enables rapid integration of real-world data of patients with cancer in a more clinically useful time frame. We also developed an open-source Web application to allow users to seamlessly search, browse, explore, and download CGT data. Conclusions Our pilot demonstrates the willingness of patients with cancer to participate in data sharing and how blockchain-enabled structures can maintain relationships between individual data elements while preserving patient privacy, empowering findings by third-party researchers and clinicians. We demonstrate the feasibility of CGT as a framework to share health data trapped in silos to further cancer research. Further studies to optimize data representation, stream, and integrity are required.

scholarly journals Real-world data: how they can help to improve quality of care

2021 ◽  
Vol 8 ◽  
pp. 134-139
Author(s):  
Giovanni Corrao ◽  
Giovanni Alquati ◽  
Giovanni Apolone ◽  
Andrea Ardizzoni ◽  
Giuliano Buzzetti ◽  
...  
Keyword(s):  
Public Health ◽  
Quality Of Care ◽  
Real World ◽  
Ethical Issues ◽  
Data Availability ◽  
Quality Data ◽  
Real World Data ◽  
World Data ◽  
Critical Issues

The current COVID pandemic crisis made it even clearer that the solutions to several questions that public health must face require the access to good quality data. Several issues of the value and potential of health data and the current critical issues that hinder access are discussed in this paper. In particular, the paper (i) focuses on “real-world data” definition; (ii) proposes a review of the real-world data availability in our country; (iii) discusses its potential, with particular focus on the possibility of improving knowledge on the quality of care provided by the health system; (iv) emphasizes that the availability of data alone is not sufficient to increase our knowledge, underlining the need that innovative analysis methods (e.g., artificial intelligence techniques) must be framed in the paradigm of clinical research; and (v) addresses some ethical issues related to their use. The proposal is to realize an alliance between organizations interested in promoting research aimed at collecting scientifically solid evidence to support the clinical governance of public health.

scholarly journals Honeur (Heamatology Outcomes Network in Europe): A Federated Model to Support Real World Data Research in Hematology

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 4839-4839
Author(s):  
Kristina Bardenheuer ◽  
Alun Passey ◽  
Maria d'Errico ◽  
Barbara Millier ◽  
Carine Guinard-Azadian ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Overall Survival ◽  
Real World ◽  
Data Model ◽  
Quality Analysis ◽  
Common Data Model ◽  
Real World Data ◽  
World Data ◽  
New Concepts ◽  
The Common

Abstract Introduction: The Haematology Outcomes Network in EURope (HONEUR) is an interdisciplinary initiative aimed at improving patient outcomes by analyzing real world data across hematological centers in Europe. Its overarching goal is to create a secure network which facilitates the development of a collaborative research community and allows access to big data tools for analysis of the data. The central paradigm in the HONEUR network is a federated model whereby the data stays at the respective sites and the analysis is executed at the local data sources. To allow for a uniform data analysis, the common data model 'OMOP' (Observational Medical Outcomes Partnership) was selected and extended to accommodate specific hematology data elements. Objective: To demonstrate feasibility of the OMOP common data model for the HONEUR network. Methods: In order to validate the architecture of the HONEUR network and the applicability of the OMOP common data model, data from the EMMOS registry (NCT01241396) have been used. This registry is a prospective, non-interventional study that was designed to capture real world data regarding treatments and outcomes for multiple myeloma at different stages of the disease. Data was collected between Oct 2010 and Nov 2014 on more than 2,400 patients across 266 sites in 22 countries. Data was mapped to the OMOP common data model version 5.3. Additional new concepts to the standard OMOP were provided to preserve the semantic mapping quality and reduce the potential loss of granularity. Following the mapping process, a quality analysis was performed to assess the completeness and accuracy of the mapping to the common data model. Specific critical concepts in multiple myeloma needed to be represented in OMOP. This applies in particular for concepts like treatment lines, cytogenetic observations, disease progression, risk scales (in particular ISS and R-ISS). To accommodate these concepts, existing OMOP structures were used with the definition of new concepts and concept-relationships. Results: Several elements of mapping data from the EMMOS registry to the OMOP common data model (CDM) were evaluated via integrity checks. Core entities from the OMOP CDM were reconciled against the source data. This was applied for the following entities: person (profile of year of birth and gender), drug exposure (profile of number of drug exposures per drug, at ATC code level), conditions (profile of number of occurrences of conditions per condition code, converted to SNOMED), measurement (profile of number of measurements and value distribution per (lab) measurement, converted to LOINC) and observation (profile of number of observations per observation concept). Figure 1 shows the histogram of year of birth distribution between the EMMOS registry and the OMOP CDM. No discernible differences exist, except for subjects which have not been included in the mapping to the OMOP CDM due to lacking confirmation of a diagnosis of multiple myeloma. As additional part of the architecture validation, the occurrence of the top 20 medications in the EMMOS registry and the OMOP CDM were compared, with a 100% concordance for the drug codes, which is shown in Figure 2. In addition to the reconciliation against the different OMOP entities, a comparison was also made against 'derived' data, in particular 'time to event' analysis. Overall survival was plotted from calculated variables in the analysis level data from the EMMOS registry and derived variables in the OMOP CDM. Probability of overall survival over time was virtually identical with only one day difference in median survival and 95% confidence intervals identically overlapping over the period of measurement (Figure 3). Conclusions: The concordance of year of birth, drug code mapping and overall survival between the EMMOS registry and the OMOP common data model indicates the reliability of mapping potential in HONEUR, especially where auxiliary methods have been developed to handle outcomes and treatment data in a way that can be harmonized across platform datasets. Disclosures No relevant conflicts of interest to declare.

scholarly journals AB0951 THE ITALIAN FIBROMYALGIA REGISTRY: A NEW WAY OF USING ROUTINE REAL-WORLD DATA CONCERNING PATIENT-REPORTED DISEASE STATUS IN HEALTHCARE RESEARCH AND CLINICAL PRACTICE

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1773.2-1773
Author(s):  
F. Salaffi ◽  
S. Farah ◽  
V. Giorgi ◽  
P. Sarzi-Puttini
Keyword(s):  
Clinical Practice ◽  
Real World ◽  
Quality Data ◽  
Study Patient ◽  
Italian Population ◽  
Real World Data ◽  
Web Based ◽  
World Data ◽  
American College Of Rheumatology ◽  
Distress Scale

Background:Fibromyalgia (FM), the most frequently encountered cause of widespread musculoskeletal pain, affects an estimated 2% of the general Italian population. However, it is not a homogeneous clinical entity, and a number of interacting factors can influence patient prognosis and the outcomes of standardised treatment programmes. Registries are a source of high-quality data for clinical research, but relating this information to individual patients is technically challenging.Objectives:The aim of this article is to describe the structure and objectives of the first Italian Fibromyalgia Registry (IFR), a new web-based registry of patients with FM.Methods:The IFR was developed to collect, store, and share information electronically entered by physicians throughout Italy who are members of the Italian Society of Rheumatology and have a particular interest in FM. It has a web-based architecture that uses two separate servers and an encryption algorithm to ensure the confidentiality and integrity of the exchanged data. The questionnaires included on the platform are the Revised Fibromyalgia Impact Questionnaire (FIQR), the modified Fibromyalgia Assessment Status (ModFAS), and the Polysymptomatic Distress Scale (PDS).Results:The registry includes data relating to 2,339 patients (93.2% female) who satisfied the 1990 or 2010/2011 American College of Rheumatology Classification Criteria for Fibromyalgia at the time of diagnosis. At the time of this analysis, the patients had a mean age of 51.9 years (SD 11.5) and a mean disease duration of 7.3 years (SD 6.9). The majority were married (71.3%), and generally well educated. The overall median FIQR, ModFAS and PDS scores and 25th-75thpercentiles were respectively 61.16 (41.16-77.00), 8.91 (41.16-77.00), and 19.0 (13.00-24.00). The six highest scoring items indicating the greatest impact of the disease on the patients related to fatigue/energy (7.18), sleep quality (6.87), tenderness (6.69), pain (6.68), stiffness (6.66), and environmental sensitivity (6.35). A high proportion of the responding patients reported experiencing pain in the neck (80.46%), upper back (68.36%), and lower back (75.05%).Conclusion:The IFR is the most comprehensive FM registry in Italy, and provides healthcare professionals with a secure, reliable, and easy-to-use means of monitoring the patients’ clinical progression, treatment history and treatment responses. This can help clinicians to plan patient management, facilitates research study patient recruitment, and provides the participating pain clinics with statistics based on real-world data. It also helps address the Italian Ministry of Health long-term goal of using precision medicine for chronic pain prevention and treatment. It is hoped that the IFR will enhance both scientific research and clinical practice.References:[1]Drolet BC, Johnson KB: Categorizing the world of registries 2008; 41: 1009–20.[2]Martinez JE, Paiva ES, Rezende MC, Heymann RE, Helfenstein M, Ranzolin A, et al.: EpiFibro (Brazilian Fibromyalgia Registry): data on the ACR classification and diagnostic preliminary criteria fulfillment and the follow-up evaluation. 2017; 57: 129–33[3]Whipple MO, McAllister SJ, Oh TH, Luedtke CA, Toussaint LL, Vincent A: Construction of a US fibromyalgia registry using the Fibromyalgia Research Survey criteria. 2013; 6: 398–99[4]Wolfe F, Smythe HA, Yunus MB, Bennett RM, Bombardier C, Goldenberg D, et al.: The American College of Rheumatology 1990 Criteria for the Classification of Fibromyalgia. Report of the Multicenter Criteria Committee. 1990; 33: 160–72Disclosure of Interests:None declared

scholarly journals An OMOP-CDM based pharmacovigilance data-processing pipeline (PDP) providing active surveillance for ADR signal detection from real-world data sources

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hyunah Shin ◽  
Suehyun Lee
Keyword(s):  
Data Processing ◽  
Active Surveillance ◽  
Drug Safety ◽  
Real World ◽  
Reporting System ◽  
Data Sources ◽  
Real World Data ◽  
World Data ◽  
Processing Pipeline ◽  
Real World Evidence

Abstract Background Adverse drug reactions (ADRs) are regarded as a major cause of death and a major contributor to public health costs. For the active surveillance of drug safety, the use of real-world data and real-world evidence as part of the overall pharmacovigilance process is important. In this regard, many studies apply the data-driven approaches to support pharmacovigilance. We developed a pharmacovigilance data-processing pipeline (PDP) that utilized electronic health records (EHR) and spontaneous reporting system (SRS) data to explore pharmacovigilance signals. Methods To this end, we integrated two medical data sources: Konyang University Hospital (KYUH) EHR and the United States Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). As part of the presented PDP, we converted EHR data on the Observation Medical Outcomes Partnership (OMOP) data model. To evaluate the ability of using the proposed PDP for pharmacovigilance purposes, we performed a statistical validation using drugs that induce ear disorders. Results To validate the presented PDP, we extracted six drugs from the EHR that were significantly involved in ADRs causing ear disorders: nortriptyline, (hazard ratio [HR] 8.06, 95% CI 2.41–26.91); metoclopramide (HR 3.35, 95% CI 3.01–3.74); doxycycline (HR 1.73, 95% CI 1.14–2.62); digoxin (HR 1.60, 95% CI 1.08–2.38); acetaminophen (HR 1.59, 95% CI 1.47–1.72); and sucralfate (HR 1.21, 95% CI 1.06–1.38). In FAERS, the strongest associations were found for nortriptyline (reporting odds ratio [ROR] 1.94, 95% CI 1.73–2.16), sucralfate (ROR 1.22, 95% CI 1.01–1.45), doxycycline (ROR 1.30, 95% CI 1.20–1.40), and hydroxyzine (ROR 1.17, 95% CI 1.06–1.29). We confirmed the results in a meta-analysis using random and fixed models for doxycycline, hydroxyzine, metoclopramide, nortriptyline, and sucralfate. Conclusions The proposed PDP could support active surveillance and the strengthening of potential ADR signals via real-world data sources. In addition, the PDP was able to generate real-world evidence for drug safety.

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