Published on in Vol 16, No 7 (2014): July

Employing Computers for the Recruitment into Clinical Trials: A Comprehensive Systematic Review

Employing Computers for the Recruitment into Clinical Trials: A Comprehensive Systematic Review

Employing Computers for the Recruitment into Clinical Trials: A Comprehensive Systematic Review

Authors of this article:

Felix Köpcke1 ;   Hans-Ulrich Prokosch2

Journals

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  4. Feldmeth G, Naureckas E, Solway J, Lindau S. Embedding research recruitment in a community resource e-prescribing system: lessons from an implementation study on Chicago’s South Side. Journal of the American Medical Informatics Association 2019;26(8-9):840 View
  5. Frampton G, Shepherd J, Pickett K, Griffiths G, Wyatt J. Digital tools for the recruitment and retention of participants in randomised controlled trials: a systematic map. Trials 2020;21(1) View
  6. Applequist J, Burroughs C, Ramirez A, Merkel P, Rothenberg M, Trapnell B, Desnick R, Sahin M, Krischer J. A novel approach to conducting clinical trials in the community setting: utilizing patient-driven platforms and social media to drive web-based patient recruitment. BMC Medical Research Methodology 2020;20(1) View
  7. Palac H, Alam N, Kaiser S, Ciolino J, Lattie E, Mohr D. A Practical Do-It-Yourself Recruitment Framework for Concurrent eHealth Clinical Trials: Simple Architecture (Part 1). Journal of Medical Internet Research 2018;20(11):e11049 View
  8. Samuels M, Schuff R, Beninato P, Gorsuch A, Dursch J, Egan S, Adams B, Hollis K, Navarro R, Burdick T. Effectiveness and cost of recruiting healthy volunteers for clinical research studies using an electronic patient portal: A randomized study. Journal of Clinical and Translational Science 2017;1(6):366 View
  9. Miller H, Gleason K, Juraschek S, Plante T, Lewis-Land C, Woods B, Appel L, Ford D, Dennison Himmelfarb C. Electronic medical record–based cohort selection and direct-to-patient, targeted recruitment: early efficacy and lessons learned. Journal of the American Medical Informatics Association 2019;26(11):1209 View
  10. Taft T, Weir C, Kramer H, Facelli J. Primary care perspectives on implementation of clinical trial recruitment. Journal of Clinical and Translational Science 2020;4(1):61 View
  11. Rajkumar R. Wireless Heartrate Monitoring Along Prioritized Alert Notification Using Mobile Techniques. International Journal of Applied Research on Public Health Management 2019;4(1):35 View
  12. Toddenroth D, Sivagnanasundaram J, Prokosch H, Ganslandt T. Concept and implementation of a study dashboard module for a continuous monitoring of trial recruitment and documentation. Journal of Biomedical Informatics 2016;64:222 View
  13. von Lucadou M, Ganslandt T, Prokosch H, Toddenroth D. Feasibility analysis of conducting observational studies with the electronic health record. BMC Medical Informatics and Decision Making 2019;19(1) View
  14. Hein A, Gass P, Walter C, Taran F, Hartkopf A, Overkamp F, Kolberg H, Hadji P, Tesch H, Ettl J, Wuerstlein R, Lounsbury D, Lux M, Lüftner D, Wallwiener M, Müller V, Belleville E, Janni W, Fehm T, Wallwiener D, Ganslandt T, Ruebner M, Beckmann M, Schneeweiss A, Fasching P, Brucker S. Computerized patient identification for the EMBRACA clinical trial using real-time data from the PRAEGNANT network for metastatic breast cancer patients. Breast Cancer Research and Treatment 2016;158(1):59 View
  15. Krischer J, Cronholm P, Burroughs C, McAlear C, Borchin R, Easley E, Davis T, Kullman J, Carette S, Khalidi N, Koening C, Langford C, Monach P, Moreland L, Pagnoux C, Specks U, Sreih A, Ytterberg S, Merkel P. Experience With Direct-to-Patient Recruitment for Enrollment Into a Clinical Trial in a Rare Disease: A Web-Based Study. Journal of Medical Internet Research 2017;19(2):e50 View
  16. Heerman W, Jackson N, Roumie C, Harris P, Rosenbloom S, Pulley J, Wilkins C, Williams N, Crenshaw D, Leak C, Scherdin J, Muñoz D, Bachmann J, Rothman R, Kripalani S. Recruitment methods for survey research: Findings from the Mid-South Clinical Data Research Network. Contemporary Clinical Trials 2017;62:50 View
  17. Kondylakis H, Claerhout B, Keyur M, Koumakis L, van Leeuwen J, Marias K, Perez-Rey D, De Schepper K, Tsiknakis M, Bucur A. The INTEGRATE project: Delivering solutions for efficient multi-centric clinical research and trials. Journal of Biomedical Informatics 2016;62:32 View
  18. Geller N, Kim D, Tian X. Smart Technology in Lung Disease Clinical Trials. Chest 2016;149(1):22 View
  19. Girardeau Y, Doods J, Zapletal E, Chatellier G, Daniel C, Burgun A, Dugas M, Rance B. Leveraging the EHR4CR platform to support patient inclusion in academic studies: challenges and lessons learned. BMC Medical Research Methodology 2017;17(1) View
  20. Shivade C, Hebert C, Lopetegui M, de Marneffe M, Fosler-Lussier E, Lai A. Textual inference for eligibility criteria resolution in clinical trials. Journal of Biomedical Informatics 2015;58:S211 View
  21. Blatch-Jones A, Nuttall J, Bull A, Worswick L, Mullee M, Peveler R, Falk S, Tape N, Hinks J, Lane A, Wyatt J, Griffiths G. Using digital tools in the recruitment and retention in randomised controlled trials: survey of UK Clinical Trial Units and a qualitative study. Trials 2020;21(1) View
  22. Diaz-Garelli F, Strowd R, Lawson V, Mayorga M, Wells B, Lycan T, Topaloglu U. Workflow Differences Affect Data Accuracy in Oncologic EHRs: A First Step Toward Detangling the Diagnosis Data Babel. JCO Clinical Cancer Informatics 2020;(4):529 View
  23. Leyens L, Reumann M, Malats N, Brand A. Use of big data for drug development and for public and personal health and care. Genetic Epidemiology 2017;41(1):51 View
  24. Jonnalagadda S, Adupa A, Garg R, Corona-Cox J, Shah S. Text Mining of the Electronic Health Record: An Information Extraction Approach for Automated Identification and Subphenotyping of HFpEF Patients for Clinical Trials. Journal of Cardiovascular Translational Research 2017;10(3):313 View
  25. Xu J, Rasmussen L, Shaw P, Jiang G, Kiefer R, Mo H, Pacheco J, Speltz P, Zhu Q, Denny J, Pathak J, Thompson W, Montague E. Review and evaluation of electronic health records-driven phenotype algorithm authoring tools for clinical and translational research. Journal of the American Medical Informatics Association 2015;22(6):1251 View
  26. Beskow L, Brelsford K, Hammack C. Patient perspectives on use of electronic health records for research recruitment. BMC Medical Research Methodology 2019;19(1) View
  27. Becker L, Ganslandt T, Prokosch H, Newe A. Applied Practice and Possible Leverage Points for Information Technology Support for Patient Screening in Clinical Trials: Qualitative Study. JMIR Medical Informatics 2020;8(6):e15749 View
  28. Pung J, Rienhoff O. Key components and IT assistance of participant management in clinical research: a scoping review. JAMIA Open 2020;3(3):449 View
  29. Beskow L, Brelsford K, Hammack-Aviran C. EHR phenotyping for research recruitment: Researcher, IRB, and physician perspectives on approaches to contacting patients. Journal of Clinical and Translational Science 2021;5(1) View
  30. Huebner H, Kurbacher C, Kuesters G, Hartkopf A, Lux M, Huober J, Volz B, Taran F, Overkamp F, Tesch H, Häberle L, Lüftner D, Wallwiener M, Müller V, Beckmann M, Belleville E, Ruebner M, Untch M, Fasching P, Janni W, Fehm T, Kolberg H, Wallwiener D, Brucker S, Schneeweiss A, Ettl J. Heregulin (HRG) assessment for clinical trial eligibility testing in a molecular registry (PRAEGNANT) in Germany. BMC Cancer 2020;20(1) View
  31. Callahan A, Polony V, Posada J, Banda J, Gombar S, Shah N. ACE: the Advanced Cohort Engine for searching longitudinal patient records. Journal of the American Medical Informatics Association 2021;28(7):1468 View
  32. Naceanceno K, House S, Asaro P. Shared-Task Worklists Improve Clinical Trial Recruitment Workflow in an Academic Emergency Department. Applied Clinical Informatics 2021;12(02):293 View
  33. Diaz-Garelli F, Strowd R, Ahmed T, Lycan T, Daley S, Wells B, Topaloglu U. What Oncologists Want: Identifying Challenges and Preferences on Diagnosis Data Entry to Reduce EHR-Induced Burden and Improve Clinical Data Quality. JCO Clinical Cancer Informatics 2021;(5):527 View
  34. Jungo K, Meier R, Valeri F, Schwab N, Schneider C, Reeve E, Spruit M, Schwenkglenks M, Rodondi N, Streit S. Baseline characteristics and comparability of older multimorbid patients with polypharmacy and general practitioners participating in a randomized controlled primary care trial. BMC Family Practice 2021;22(1) View
  35. BORGWARDT S, FORKEL W, KOVTUNOVA A. Temporal Minimal-World Query Answering over Sparse ABoxes. Theory and Practice of Logic Programming 2022;22(2):193 View
  36. Fitzer K, Haeuslschmid R, Blasini R, Altun F, Hampf C, Freiesleben S, Macho P, Prokosch H, Gulden C. Patient Recruitment System for Clinical Trials: Mixed Methods Study About Requirements at Ten University Hospitals. JMIR Medical Informatics 2022;10(4):e28696 View
  37. Idnay B, Dreisbach C, Weng C, Schnall R. A systematic review on natural language processing systems for eligibility prescreening in clinical research. Journal of the American Medical Informatics Association 2021;29(1):197 View
  38. Vorisek C, Lehne M, Klopfenstein S, Mayer P, Bartschke A, Haese T, Thun S. Fast Healthcare Interoperability Resources (FHIR) for Interoperability in Health Research: Systematic Review. JMIR Medical Informatics 2022;10(7):e35724 View
  39. Haynes R, Sirintrapun S, Gao J, McKenzie A. Using Technology to Enhance Cancer Clinical Trial Participation. American Society of Clinical Oncology Educational Book 2022;(42):39 View
  40. Rafee A, Riepenhausen S, Neuhaus P, Meidt A, Dugas M, Varghese J. ELaPro, a LOINC-mapped core dataset for top laboratory procedures of eligibility screening for clinical trials. BMC Medical Research Methodology 2022;22(1) View
  41. Lee S, Lee N, Kirkpatrick C. Effects of Communication Source and Racial Representation in Clinical Trial Recruitment Flyers. Health Communication 2023;38(4):790 View
  42. Bazoge A, Morin E, Daille B, Gourraud P. Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review. JMIR Medical Informatics 2023;11:e42477 View
  43. Widera P, Welsing P, Danso S, Peelen S, Kloppenburg M, Loef M, Marijnissen A, van Helvoort E, Blanco F, Magalhães J, Berenbaum F, Haugen I, Bay-Jensen A, Mobasheri A, Ladel C, Loughlin J, Lafeber F, Lalande A, Larkin J, Weinans H, Bacardit J. Development and validation of a machine learning-supported strategy of patient selection for osteoarthritis clinical trials: the IMI-APPROACH study. Osteoarthritis and Cartilage Open 2023;5(4):100406 View
  44. Boeker M, Zöller D, Blasini R, Macho P, Helfer S, Behrens M, Prokosch H, Gulden C. Effectiveness of IT-supported patient recruitment: study protocol for an interrupted time series study at ten German university hospitals. Trials 2024;25(1) View
  45. Idnay B, Liu J, Fang Y, Hernandez A, Kaw S, Etwaru A, Juarez Padilla J, Ramírez S, Marder K, Weng C, Schnall R. Sociotechnical feasibility of natural language processing-driven tools in clinical trial eligibility prescreening for Alzheimer’s disease and related dementias. Journal of the American Medical Informatics Association 2024;31(5):1062 View
  46. Gulden C, Macho P, Reinecke I, Strantz C, Prokosch H, Blasini R. recruIT: A cloud-native clinical trial recruitment support system based on Health Level 7 Fast Healthcare Interoperability Resources (HL7 FHIR) and the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM). Computers in Biology and Medicine 2024;174:108411 View
  47. Haun M, Tönnies J, Hartmann M, Wildenauer A, Wensing M, Szecsenyi J, Feißt M, Pohl M, Vomhof M, Icks A, Friederich H. Model of integrated mental health video consultations for people with depression or anxiety in primary care (PROVIDE-C): assessor masked, multicentre, randomised controlled trial. BMJ 2024:e079921 View

Books/Policy Documents

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