{"doi":"10.1002/pds.70089","title":"<scp>INSIGHT</scp>\n                    : A Tool for Fit‐for‐Purpose Evaluation and Quality Assessment of Standardized Observational Data Sources for Real World Evidence on Medicine and Vaccine Safety","abstract":"<jats:title>ABSTRACT</jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose</jats:title>\n                    <jats:p>To describe the development of INSIGHT, a real‐world data quality tool to assess completeness, consistency, and fitness‐for‐purpose of observational health data sources.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods</jats:title>\n                    <jats:p>We designed a three‐level pipeline with data quality assessments (DQAs) to be performed in ConcePTION Common Data Model (CDM) instances. The pipeline has been coded using R.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Results</jats:title>\n                    <jats:p>INSIGHT is an open‐source tool that identifies potential data quality issues in CDM‐standardized instances through the systematic execution and summary of over 588 configurable DQAs. Level 1 focuses on conformance to the ConcePTION CDM specifications. Level 2 evaluates the temporal plausibility of events and uniqueness of records. Level 3 provides an overview of distributions, outliers, and trends over time to facilitate fit‐for‐purpose evaluation. Therefore, level 1 and 2 assure a proper data standardization, while level 3 provides information regarding the study population, and potential sub‐populations. The DQAs are run locally and assessed centrally by a data quality revisor together with the data access provider's representatives.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion</jats:title>\n                    <jats:p>Data quality is the sum of several internal and external features of the data. While DQAs can provide reassurance about fitness‐for‐purpose for secondary‐use data sources, improvements in data collection are essential to reduce errors and enhance overall data quality for Real World Evidence.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion</jats:title>\n                    <jats:p>INSIGHT aims to support clinical and regulatory decision‐making for medicines and vaccines by evaluating the quality of observational health data sources to support fit for purpose assessment. Assessing and improving data quality will enhance the reliability and quality of the generated evidence.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Registration</jats:title>\n                    <jats:p>This research was registered in EU PAS registration with number EU50142.</jats:p>\n                  </jats:sec>","journal":"Pharmacoepidemiology and Drug Safety","year":2025,"id":592236,"datarank":0.24141568686511508,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"self_citation_contribution":0.24141568686511508,"citation_network_contribution":0.0,"self_endowment_contribution":0.24141568686511508,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":4,"citer_count":3,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":104040,"name":"Constanza L. Andaur Navarro","orcid":"0000-0002-7745-2887","position":1,"is_corresponding":false},{"id":1515451,"name":"Judit Riera‐Arnau","orcid":"0000-0001-7591-0218","position":2,"is_corresponding":false},{"id":1515452,"name":"Roel J. H. J. Elbers","orcid":null,"position":3,"is_corresponding":false},{"id":1515453,"name":"Ema Alsina","orcid":null,"position":4,"is_corresponding":false},{"id":1515454,"name":"Caitlin Dodd","orcid":"0000-0002-8784-696X","position":5,"is_corresponding":false},{"id":1515455,"name":"Miriam C. J. M. Sturkenboom","orcid":"0000-0003-1360-2388","position":6,"is_corresponding":false},{"id":1515450,"name":"Vjola Hoxhaj","orcid":"0009-0007-2194-0818","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"<scp>INSIGHT</scp>\n                    : A Tool for Fit‐for‐Purpose Evaluation and Quality Assessment of Standardized Observational Data Sources for Real World Evidence on Medicine and Vaccine Safety","abstract":"<jats:title>ABSTRACT</jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose</jats:title>\n                    <jats:p>To describe the development of INSIGHT, a real‐world data quality tool to assess completeness, consistency, and fitness‐for‐purpose of observational health data sources.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods</jats:title>\n                    <jats:p>We designed a three‐level pipeline with data quality assessments (DQAs) to be performed in ConcePTION Common Data Model (CDM) instances. The pipeline has been coded using R.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Results</jats:title>\n                    <jats:p>INSIGHT is an open‐source tool that identifies potential data quality issues in CDM‐standardized instances through the systematic execution and summary of over 588 configurable DQAs. Level 1 focuses on conformance to the ConcePTION CDM specifications. Level 2 evaluates the temporal plausibility of events and uniqueness of records. Level 3 provides an overview of distributions, outliers, and trends over time to facilitate fit‐for‐purpose evaluation. Therefore, level 1 and 2 assure a proper data standardization, while level 3 provides information regarding the study population, and potential sub‐populations. The DQAs are run locally and assessed centrally by a data quality revisor together with the data access provider's representatives.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion</jats:title>\n                    <jats:p>Data quality is the sum of several internal and external features of the data. While DQAs can provide reassurance about fitness‐for‐purpose for secondary‐use data sources, improvements in data collection are essential to reduce errors and enhance overall data quality for Real World Evidence.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion</jats:title>\n                    <jats:p>INSIGHT aims to support clinical and regulatory decision‐making for medicines and vaccines by evaluating the quality of observational health data sources to support fit for purpose assessment. Assessing and improving data quality will enhance the reliability and quality of the generated evidence.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Registration</jats:title>\n                    <jats:p>This research was registered in EU PAS registration with number EU50142.</jats:p>\n                  </jats:sec>","is_dataset_classified":null,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"39805807","pmcid":"PMC11730612","openalex_id":"https://openalex.org/W4406319777","authors":[],"funders":[{"funder_name":"Innovative Medicines Initiative","grant_id":"821520","title":"Building an ecosystem for better monitoring and communicating of medication safety in pregnancy and breastfeeding: validated and regulatory endorsed workflows for fast, optimised evidence generation"},{"funder_name":"European Union's Horizon 2020 Research and Innovation Programme","grant_id":"","title":null},{"funder_name":"European Federation of Pharmaceutical Industries and Associations","grant_id":"","title":null},{"funder_name":"EFPIA","grant_id":"","title":null},{"funder_name":"European Union's Horizon 2020 Research and Innovation Programme","grant_id":"","title":null},{"funder_name":"EFPIA","grant_id":"","title":null}],"total_grants":6,"fwci":2.167,"citation_percentile":0.82859209,"influential_citations":0,"citation_trend":[{"year":2025,"count":1},{"year":2026,"count":2}],"oa_status":"hybrid","license":"cc-by-nc","oa_locations":[{"url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/pds.70089","host_type":"journal"},{"url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/pds.70089","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/pdf/10.1002/pds.70089","host_type":"publisher"},{"url":"https://doi.org/10.1002/pds.70089","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/39805807","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11730612","host_type":"repository"},{"url":"https://ddd.uab.cat/record/328370","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11730612/pdf/PDS-34-e70089.pdf","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC11730612","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC11730612?pdf=render","host_type":"Europe_PMC"},{"url":"http://dx.doi.org/10.1002/pds.70089","host_type":""}],"fields_of_study":["Pharmacovigilance and Adverse Drug Reactions","Advanced Causal Inference Techniques","Vaccine Coverage and Hesitancy","Humans","Vaccines","Data Accuracy","Observational Studies as Topic","Pharmacoepidemiology","Information Sources"],"mesh_terms":["Data Accuracy","Information Sources","Data Collection","Humans","Vaccines","Pharmacoepidemiology","Observational Studies as Topic"],"keywords":["Data quality","Observational study","Standardization","Quality (philosophy)","Medicine","Reliability (semiconductor)","Data collection","Data science","Consistency (knowledge bases)","Computer science","Quality assurance","Data mining","Risk analysis (engineering)","Engineering","Statistics","Operations management","External quality assessment","Pregnancy","Drug safety","Common Data Model","Quality Checks","Multi‐database Studies","Real‐world Data","Vaccines","Observational Studies as Topic","Pharmacoepidemiology","Humans","Original Article","Information Sources","Data Accuracy"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-26T12:50:09.966841Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}