{"doi":"10.1093/jamiaopen/ooaf139","title":"Evaluating the Impact of Electronic Health Record to Electronic Data Capture Technology on Workflow Efficiency: a Site Perspective","abstract":"Introduction: Clinical trial data is still predominantly manually entered by site staff into Electronic Data Capture (EDC) systems. This process of abstracting and manually transcribing patient data is time-consuming, inefficient and error prone. Use of Electronic Health Record to Electronic Data Capture (EHR-To-EDC) technologies that digitize this process would improve these inefficiencies. Objectives: This study measured the impact of EHR-To-EDC technology on the data entry workflow of clinical trial data managers. The primary objective was to compare the speed and accuracy of the EHR-To-EDC enabled data entry method to the traditional, manual method. The secondary objective was to measure end user satisfaction. Materials and Methods: Five data managers ranging in experience from 9 months to over 2 years, were assigned an investigator-initiated, Memorial Sloan Kettering-sponsored oncology study within their disease area of expertise. Each data manager performed one-hour of manual data entry, and a week later, one-hour of data entry using IgniteData's EHR-To-EDC solution, Archer, on a predetermined set of patients, timepoints and data domains (labs, vitals). The data entered into the EDC were compared side-by-side and used to evaluate the speed and accuracy of the EHR-To-EDC enabled method versus traditional, manual data entry. A user satisfaction survey using a 5-point Likert scale was used to collect feedback regarding the selected platform's learnability, ease of use, perceived time savings, perceived efficiency, and preference over the manual method. Results: The EHR-To-EDC method resulted in 58% more data entered versus the manual method (difference, 1745 data points; manual, 3023 data points; EHR-To-EDC, 4768 data points). The number of data entry errors was reduced by 99% (manual, 100 data points; EHR-To-EDC, 1 data point). Regarding user satisfaction, data managers either agreed or strongly agreed that the EHR-To-EDC workflow was easy to learn (5/5), easy to use (4.6/5), saved time (5/5), was more efficient (4.8/5), and preferred it over the manual entry workflow (4/5). Conclusion: EHR-To-EDC enabled data entry increases data manager productivity, reduces errors and is preferred by data managers over manual data entry.","journal":"JAMIA Open","year":2025,"id":548247,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9518,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1441913,"name":"Michael-Owen Panzarella","orcid":null,"position":1,"is_corresponding":false},{"id":900444,"name":"Michael Buckley","orcid":"0000-0001-9150-1326","position":2,"is_corresponding":false},{"id":1441914,"name":"Milena Silverman","orcid":null,"position":3,"is_corresponding":false},{"id":1441915,"name":"Evelyn Salazar","orcid":null,"position":4,"is_corresponding":false},{"id":1195118,"name":"Renata Panchal","orcid":null,"position":5,"is_corresponding":false},{"id":1118029,"name":"Joseph M. Lengfellner","orcid":"0000-0002-3921-9507","position":6,"is_corresponding":false},{"id":351395,"name":"Alexia Iasonos","orcid":"0000-0003-0471-8477","position":7,"is_corresponding":false},{"id":651655,"name":"Maryam Y. Garza","orcid":"0000-0002-2652-5935","position":8,"is_corresponding":false},{"id":513090,"name":"Byeong Yeob Choi","orcid":"0000-0002-4205-1442","position":9,"is_corresponding":false},{"id":536303,"name":"Meredith Zozus","orcid":"0000-0002-9332-1684","position":10,"is_corresponding":false},{"id":1195119,"name":"Stephanie Terzulli","orcid":null,"position":11,"is_corresponding":false},{"id":90775,"name":"Paul Sabbatini","orcid":"0009-0000-8853-2244","position":12,"is_corresponding":false},{"id":1441912,"name":"Anna Patruno","orcid":null,"position":0,"is_corresponding":true}],"reference_count":8,"raw_metadata":null,"created_at":"2026-07-19T02:53:58.530220Z","pmid":"41180891","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":[]}