OBJECTIVE: Failure to reach research subject recruitment goals is a significant impediment to the success of many clinical trials. Implementation of health-information technology has allowed retrospective analysis of data for cohort identification and recruitment, but few institutions have also leveraged real-time streams to support such activities. DESIGN: Duke Medicine has deployed a hybrid solution, The Duke Integrated Subject Cohort and Enrollment Research Network (DISCERN), that combines both retrospective warehouse data and clinical events contained in prospective Health Level 7 (HL7) messages to immediately alert study personnel of potential recruits as they become eligible. RESULTS: DISCERN analyzes more than 500000 messages daily in service of 12 projects. Users may receive results via email, text pages, or on-demand reports. Preliminary results suggest DISCERN's unique ability to reason over both retrospective and real-time data increases study enrollment rates while reducing the time required to complete recruitment-related tasks. The authors have introduced a preconfigured DISCERN function as a self-service feature for users. LIMITATIONS: The DISCERN framework is adoptable primarily by organizations using both HL7 message streams and a data warehouse. More efficient recruitment may exacerbate competition for research subjects, and investigators uncomfortable with new technology may find themselves at a competitive disadvantage in recruitment. CONCLUSION: DISCERN's hybrid framework for identifying real-time clinical events housed in HL7 messages complements the traditional approach of using retrospective warehoused data. DISCERN is helpful in instances when the required clinical data may not be loaded into the warehouse and thus must be captured contemporaneously during patient care. Use of an open-source tool supports generalizability to other institutions at minimal cost.
OBJECTIVE: Failure to reach research subject recruitment goals is a significant impediment to the success of many clinical trials. Implementation of health-information technology has allowed retrospective analysis of data for cohort identification and recruitment, but few institutions have also leveraged real-time streams to support such activities. DESIGN: Duke Medicine has deployed a hybrid solution, The Duke Integrated Subject Cohort and Enrollment Research Network (DISCERN), that combines both retrospective warehouse data and clinical events contained in prospective Health Level 7 (HL7) messages to immediately alert study personnel of potential recruits as they become eligible. RESULTS: DISCERN analyzes more than 500000 messages daily in service of 12 projects. Users may receive results via email, text pages, or on-demand reports. Preliminary results suggest DISCERN's unique ability to reason over both retrospective and real-time data increases study enrollment rates while reducing the time required to complete recruitment-related tasks. The authors have introduced a preconfigured DISCERN function as a self-service feature for users. LIMITATIONS: The DISCERN framework is adoptable primarily by organizations using both HL7 message streams and a data warehouse. More efficient recruitment may exacerbate competition for research subjects, and investigators uncomfortable with new technology may find themselves at a competitive disadvantage in recruitment. CONCLUSION: DISCERN's hybrid framework for identifying real-time clinical events housed in HL7 messages complements the traditional approach of using retrospective warehoused data. DISCERN is helpful in instances when the required clinical data may not be loaded into the warehouse and thus must be captured contemporaneously during patient care. Use of an open-source tool supports generalizability to other institutions at minimal cost.
Authors: Kenneth D Mandl; J Marc Overhage; Michael M Wagner; William B Lober; Paola Sebastiani; Farzad Mostashari; Julie A Pavlin; Per H Gesteland; Tracee Treadwell; Eileen Koski; Lori Hutwagner; David L Buckeridge; Raymond D Aller; Shaun Grannis Journal: J Am Med Inform Assoc Date: 2003-11-21 Impact factor: 4.497
Authors: Nancy S Sung; William F Crowley; Myron Genel; Patricia Salber; Lewis Sandy; Louis M Sherwood; Stephen B Johnson; Veronica Catanese; Hugh Tilson; Kenneth Getz; Elaine L Larson; David Scheinberg; E Albert Reece; Harold Slavkin; Adrian Dobs; Jack Grebb; Rick A Martinez; Allan Korn; David Rimoin Journal: JAMA Date: 2003-03-12 Impact factor: 56.272
Authors: Fu-Chiang Tsui; Jeremy U Espino; Virginia M Dato; Per H Gesteland; Judith Hutman; Michael M Wagner Journal: J Am Med Inform Assoc Date: 2003-06-04 Impact factor: 4.497
Authors: B Trinczek; F Köpcke; T Leusch; R W Majeed; B Schreiweis; J Wenk; B Bergh; C Ohmann; R Röhrig; H U Prokosch; M Dugas Journal: Appl Clin Inform Date: 2014-03-19 Impact factor: 2.342
Authors: Rhonda G Kost; Sabrena Mervin-Blake; Rose Hallarn; Charles Rathmann; H Robert Kolb; Cheryl Dennison Himmelfarb; Toni D'Agostino; Eric P Rubinstein; Ann M Dozier; Kathryn G Schuff Journal: Acad Med Date: 2014-08 Impact factor: 6.893
Authors: Alison Callahan; Vladimir Polony; José D Posada; Juan M Banda; Saurabh Gombar; Nigam H Shah Journal: J Am Med Inform Assoc Date: 2021-07-14 Impact factor: 4.497
Authors: Chaitanya Shivade; Preethi Raghavan; Eric Fosler-Lussier; Peter J Embi; Noemie Elhadad; Stephen B Johnson; Albert M Lai Journal: J Am Med Inform Assoc Date: 2013-11-07 Impact factor: 4.497
Authors: William A Mattingly; Robert R Kelley; Timothy L Wiemken; Julia H Chariker; Paula Peyrani; Brian E Guinn; Laura E Binford; Kimberley Buckner; Julio Ramirez Journal: Contemp Clin Trials Commun Date: 2015-10-30