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DOI: 10.4338/ACI-2013-07-RA-0047
Design and multicentric Implementation of a generic Software Architecture for Patient Recruitment Systems re-using existing HIS tools and Routine Patient Data
Summary
Objective: (1) To define features and data items of a Patient Recruitment System (PRS); (2) to design a generic software architecture of such a system covering the requirements; (3) to identify implementation options available within different Hospital Information System (HIS) environments; (4) to implement five PRS following the architecture and utilizing the implementation options as proof of concept.
Methods: Existing PRS were reviewed and interviews with users and developers conducted. All reported PRS features were collected and prioritized according to their published success and user’s request. Common feature sets were combined into software modules of a generic software architecture. Data items to process and transfer were identified for each of the modules. Each site collected implementation options available within their respective HIS environment for each module, provided a prototypical implementation based on available implementation possibilities and supported the patient recruitment of a clinical trial as a proof of concept.
Results: 24 commonly reported and requested features of a PRS were identified, 13 of them prioritized as being mandatory. A UML version 2 based software architecture containing 5 software modules covering these features was developed. 13 data item groups processed by the modules, thus required to be available electronically, have been identified. Several implementation options could be identified for each module, most of them being available at multiple sites. Utilizing available tools, a PRS could be implemented in each of the five participating German university hospitals.
Conclusion: A set of required features and data items of a PRS has been described for the first time. The software architecture covers all features in a clear, well-defined way. The variety of implementation options and the prototypes show that it is possible to implement the given architecture in different HIS environments, thus enabling more sites to successfully support patient recruitment in clinical trials.
Citation: Trinczek B, Köpcke F, Leusch T, Majeed RW, Schreiweis B, Wenk J, Bergh B, Ohmann C, Röhrig R, Prokosch HU, Dugas M. Design and multicentric implementation of a generic software architecture for patient recruitment systems re-using existing HIS tools and routine patient data. Appl Clin Inf 2014; 5: 264–283 http://dx.doi.org/10.4338/ACI-2013-07-RA-0047
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Keywords
Clinical trials as topic - patient selection - software architecture - patient identification systems - hospital information systems
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Conflicts of Interest
The authors declare that they have no conflicts of interest in the research.
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References
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- 2 McDonald A, Knight R, Campbell M, Entwistle V, Grant A, Cook J, Elbourne D, Francis D, Garcia J, Roberts I, Snowdon C. What influences recruitment to randomised controlled trials? A review of trials funded by two UK funding agencies. Trials. 2006 ; 7(1). Available from: http://dx.doi.org/10.1186/1745–6215–7–9
- 3 Sahoo A. Patient Recruitment and Retention in Clinical Trials; Emerging strategies in Europe, the US and Asia. London:: Business Insights Ltd; 2007 Jun p. 149. Report No.: RBI00152.
- 4 Siminoff LA, Zhang A, Colabianchi N, Sturm CM, Shen Q. Factors that predict the referral of breast cancer patients onto clinical trials by their surgeons and medical oncologists. J Clin Oncol 2000; 18 (06) 1203-1211.
- 5 Cuggia M, Besana P, Glasspool D. Comparing semi-automatic systems for recruitment of patients to clinical trials. Int J Med Inform 2011; Available from: http://www.ncbi.nlm.nih.gov/pubmed/21459664
- 6 UML 2.4.1. Available from: http://www.omg.org/spec/UML/2.4.1
- 7 modelio open source modeling environment. Available from: http://www.modelio.org/downloads/down/load-modelio.html
- 8 Weng C, Tu SW, Sim I, Richesson R. Formal representation of eligibility criteria: a literature review. J Biomed Inform 2010; 43 (03) 451-467.
- 9 Lonsdale DW, Tustison C, Parker CG, Embley DW. Assessing clinical trial eligibility with logic expression queries. Data & Knowledge Engineering 2008; 66 (01) 3-17.
- 10 Borlawsky T, Payne PRO. Evaluating an NLP-based approach to modeling computable clinical trial eligibility criteria. AMIA Annu Symp Proc 2007; 878.
- 11 Afrin LB, Oates JC, Boyd CK, Daniels MS. Leveraging of Open EMR Architecture for Clinical Trial Accrual. AMIA Annu Symp Proc 2003; 2003: 16-20.
- 12 Embi PJ, Jain A, Clark J, Harris CM. Development of an Electronic Health Record-based Clinical Trial Alert System to Enhance Recruitment at the Point of Care. AMIA Annu Symp Proc 2005; 2005: 231-235.
- 13 Rollman BL, Fischer GS, Zhu F, Belnap BH. Comparison of electronic physician prompts versus waitroom case-finding on clinical trial enrollment. J Gen Intern Med 2008; 23 (04) 447-450.
- 14 Embi PJ, Jain A, Clark J, Bizjack S, Hornung R, Harris CM. Effect of a Clinical Trial Alert System on Physician Participation in Trial Recruitment. Arch Intern Med 2005; 165 (19) 2272-2277.
- 15 Butte AJ, Weinstein DA, Kohane IS. Enrolling patients into clinical trials faster using RealTime Recuiting. Proc AMIA Symp 2000; 111-115.
- 16 Ross J, Tu SW, Carini S, Sim I. Analysis of Eligibility Criteria Complexity in ClinicalTrials. San Francisco, CA.; 2010
- 17 Trinczek B, Köpcke F, Leusch T, Majeed RW, Schreiweis B, Wenk J, Bergh B, Ganslandt T, Ohmann C, Röhrig R, Dugas M. Multizentrische Analyse von Routinedaten im Hinblick auf Ein-/Ausschlusskriterien klinischer Studien. Mainz: German Medical Science GMS Publishing House; 2011
- 18 Köpcke F, Trinczek B, Majeed RW, Schreiweis B, Wenk J, Leusch T, Ganslandt T, Ohmann C, Bergh B, Röhrig R, Dugas M, Prokosch HU. Evaluation of data completeness in the electronic health record for the purpose of patient recruitment into clinical trials: a retrospective analysis of element presence. BMC Med Inform Decis Mak 2013; 13: 37.
- 19 Ainsworth J, Buchan I. Preserving consent-for-consent with feasibility-assessment and recruitment in clinical studies: FARSITE architecture. Stud Health Technol Inform 2009; 147: 137-148.
- 20 Olasov B, Sim I. RuleEd, a Web-based Semantic Network Interface for Constructing and Revising Computable Eligibility Rules. AMIA Annu Symp Proc 2006; 2006: 1051-1051.
- 21 Weiner DL, Butte AJ, Hibberd PL, Fleisher GR. Computerized recruiting for clinical trials in real time. Annals of Emergency Medicine 2003; 41 (02) 242-246.
- 22 Chow E, Zuberi M, Seto R, Hota S, Fish EN, Morra D. Using real-time alerts for clinical trials. Applied Clinical Informatics 2011; 2: 472-480.
- 23 Ferranti JM, Gilbert W, McCall J, Shang H, Barros T, Horvath MM. The design and implementation of an open-source, data-driven cohort recruitment system: the Duke Integrated Subject Cohort and Enrollment Research Network (DISCERN). Journal of the American Medical Informatics Association: JAMIA 2012; 19 e1 e68-e75.
- 24 Dugas M, Lange M, Berdel W, Muller-Tidow C. Workflow to improve patient recruitment for clinical trials within hospital information systems –a case-study. Trials 2008; 9: 2.
- 25 Embi PJ, Leonard AC. Evaluating alert fatigue over time to EHR-based clinical trial alerts: findings from a randomized controlled study. Journal of the American Medical Informatics Association: JAMIA 2012; 19 e1 e145-e148.
- 26 Ganslandt T, Mate S, Helbing K, Sax U, Prokosch HU. Unlocking Data for Clinical Research –The German i2b2 Experience. Appl Clin Inform 2011; 2 (01) 116-127.
- 27 Dugas M, Lange M, Muller-Tidow C, Kirchhof P, Prokosch H-U. Routine data from hospital information systems can support patient recruitment for clinical studies. Clin Trials 2010; 7 (02) 183-189.
- 28 Fink E, Kokku PK, Nikiforou S, Hall LO, Goldgof DB, Krischer JP. Selection of patients for clinical trials: an interactive web-based system. Artif Intell Med 2004; 31 (03) 241-254.
- 29 Thadani SR, Weng C, Bigger JT, Ennever JF, Wajngurt D. Electronic Screening Improves Efficiency in Clinical Trial Recruitment. Journal of the American Medical Informatics Association 2009; 16 (06) 869-873.
- 30 Patel C, Cimino J, Dolby J, Fokoue A, Kalyanpur A, Kershenbaum A, Ma L, Schonberg E, Srinivas K. Matching patient records to clinical trials using ontologies. Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference. Berlin, Heidelberg: Springer-Verlag; 2007. p. 816–29. Available from: http://portal.acm.org/citation.cfm?id=1785162.1785223
- 31 Ash N, Ogunyemi O, Zeng Q, Ohno-Machado L. Finding appropriate clinical trials: evaluating encoded eligibility criteria with incomplete data. Proc AMIA Symp 2001; 27-31.
- 32 Gennari JH, Sklar D, Silva J. Cross-tool communication: from protocol authoring to eligibility determination. Proc AMIA Symp 2001; 199-203.
- 33 Kamal J, Pasuparthi K, Rogers P, Buskirk J, Mekhjian H. Using an information warehouse to screen patients for clinical trials: A prototype. AMIA Annu Symp Proc 2005; 2005: 1004-1004.
- 34 Breitfeld PP, Ullrich F, Anderson J, Crist WM. Web-based decision support for clinical trial eligibility determination in an international clinical trials network. Controlled Clinical Trials 2003; 24 (06) 702-710.
- 35 Grundmeier RW, Swietlik M, Bell LM. Research subject enrollment by primary care pediatricians using an electronic health record. AMIA Annu Symp Proc 2007; 2007: 289-293.
Correspondence to:
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References
- 1 ClinicalTrials.gov. Available from: http://clinicaltrials.gov/
- 2 McDonald A, Knight R, Campbell M, Entwistle V, Grant A, Cook J, Elbourne D, Francis D, Garcia J, Roberts I, Snowdon C. What influences recruitment to randomised controlled trials? A review of trials funded by two UK funding agencies. Trials. 2006 ; 7(1). Available from: http://dx.doi.org/10.1186/1745–6215–7–9
- 3 Sahoo A. Patient Recruitment and Retention in Clinical Trials; Emerging strategies in Europe, the US and Asia. London:: Business Insights Ltd; 2007 Jun p. 149. Report No.: RBI00152.
- 4 Siminoff LA, Zhang A, Colabianchi N, Sturm CM, Shen Q. Factors that predict the referral of breast cancer patients onto clinical trials by their surgeons and medical oncologists. J Clin Oncol 2000; 18 (06) 1203-1211.
- 5 Cuggia M, Besana P, Glasspool D. Comparing semi-automatic systems for recruitment of patients to clinical trials. Int J Med Inform 2011; Available from: http://www.ncbi.nlm.nih.gov/pubmed/21459664
- 6 UML 2.4.1. Available from: http://www.omg.org/spec/UML/2.4.1
- 7 modelio open source modeling environment. Available from: http://www.modelio.org/downloads/down/load-modelio.html
- 8 Weng C, Tu SW, Sim I, Richesson R. Formal representation of eligibility criteria: a literature review. J Biomed Inform 2010; 43 (03) 451-467.
- 9 Lonsdale DW, Tustison C, Parker CG, Embley DW. Assessing clinical trial eligibility with logic expression queries. Data & Knowledge Engineering 2008; 66 (01) 3-17.
- 10 Borlawsky T, Payne PRO. Evaluating an NLP-based approach to modeling computable clinical trial eligibility criteria. AMIA Annu Symp Proc 2007; 878.
- 11 Afrin LB, Oates JC, Boyd CK, Daniels MS. Leveraging of Open EMR Architecture for Clinical Trial Accrual. AMIA Annu Symp Proc 2003; 2003: 16-20.
- 12 Embi PJ, Jain A, Clark J, Harris CM. Development of an Electronic Health Record-based Clinical Trial Alert System to Enhance Recruitment at the Point of Care. AMIA Annu Symp Proc 2005; 2005: 231-235.
- 13 Rollman BL, Fischer GS, Zhu F, Belnap BH. Comparison of electronic physician prompts versus waitroom case-finding on clinical trial enrollment. J Gen Intern Med 2008; 23 (04) 447-450.
- 14 Embi PJ, Jain A, Clark J, Bizjack S, Hornung R, Harris CM. Effect of a Clinical Trial Alert System on Physician Participation in Trial Recruitment. Arch Intern Med 2005; 165 (19) 2272-2277.
- 15 Butte AJ, Weinstein DA, Kohane IS. Enrolling patients into clinical trials faster using RealTime Recuiting. Proc AMIA Symp 2000; 111-115.
- 16 Ross J, Tu SW, Carini S, Sim I. Analysis of Eligibility Criteria Complexity in ClinicalTrials. San Francisco, CA.; 2010
- 17 Trinczek B, Köpcke F, Leusch T, Majeed RW, Schreiweis B, Wenk J, Bergh B, Ganslandt T, Ohmann C, Röhrig R, Dugas M. Multizentrische Analyse von Routinedaten im Hinblick auf Ein-/Ausschlusskriterien klinischer Studien. Mainz: German Medical Science GMS Publishing House; 2011
- 18 Köpcke F, Trinczek B, Majeed RW, Schreiweis B, Wenk J, Leusch T, Ganslandt T, Ohmann C, Bergh B, Röhrig R, Dugas M, Prokosch HU. Evaluation of data completeness in the electronic health record for the purpose of patient recruitment into clinical trials: a retrospective analysis of element presence. BMC Med Inform Decis Mak 2013; 13: 37.
- 19 Ainsworth J, Buchan I. Preserving consent-for-consent with feasibility-assessment and recruitment in clinical studies: FARSITE architecture. Stud Health Technol Inform 2009; 147: 137-148.
- 20 Olasov B, Sim I. RuleEd, a Web-based Semantic Network Interface for Constructing and Revising Computable Eligibility Rules. AMIA Annu Symp Proc 2006; 2006: 1051-1051.
- 21 Weiner DL, Butte AJ, Hibberd PL, Fleisher GR. Computerized recruiting for clinical trials in real time. Annals of Emergency Medicine 2003; 41 (02) 242-246.
- 22 Chow E, Zuberi M, Seto R, Hota S, Fish EN, Morra D. Using real-time alerts for clinical trials. Applied Clinical Informatics 2011; 2: 472-480.
- 23 Ferranti JM, Gilbert W, McCall J, Shang H, Barros T, Horvath MM. The design and implementation of an open-source, data-driven cohort recruitment system: the Duke Integrated Subject Cohort and Enrollment Research Network (DISCERN). Journal of the American Medical Informatics Association: JAMIA 2012; 19 e1 e68-e75.
- 24 Dugas M, Lange M, Berdel W, Muller-Tidow C. Workflow to improve patient recruitment for clinical trials within hospital information systems –a case-study. Trials 2008; 9: 2.
- 25 Embi PJ, Leonard AC. Evaluating alert fatigue over time to EHR-based clinical trial alerts: findings from a randomized controlled study. Journal of the American Medical Informatics Association: JAMIA 2012; 19 e1 e145-e148.
- 26 Ganslandt T, Mate S, Helbing K, Sax U, Prokosch HU. Unlocking Data for Clinical Research –The German i2b2 Experience. Appl Clin Inform 2011; 2 (01) 116-127.
- 27 Dugas M, Lange M, Muller-Tidow C, Kirchhof P, Prokosch H-U. Routine data from hospital information systems can support patient recruitment for clinical studies. Clin Trials 2010; 7 (02) 183-189.
- 28 Fink E, Kokku PK, Nikiforou S, Hall LO, Goldgof DB, Krischer JP. Selection of patients for clinical trials: an interactive web-based system. Artif Intell Med 2004; 31 (03) 241-254.
- 29 Thadani SR, Weng C, Bigger JT, Ennever JF, Wajngurt D. Electronic Screening Improves Efficiency in Clinical Trial Recruitment. Journal of the American Medical Informatics Association 2009; 16 (06) 869-873.
- 30 Patel C, Cimino J, Dolby J, Fokoue A, Kalyanpur A, Kershenbaum A, Ma L, Schonberg E, Srinivas K. Matching patient records to clinical trials using ontologies. Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference. Berlin, Heidelberg: Springer-Verlag; 2007. p. 816–29. Available from: http://portal.acm.org/citation.cfm?id=1785162.1785223
- 31 Ash N, Ogunyemi O, Zeng Q, Ohno-Machado L. Finding appropriate clinical trials: evaluating encoded eligibility criteria with incomplete data. Proc AMIA Symp 2001; 27-31.
- 32 Gennari JH, Sklar D, Silva J. Cross-tool communication: from protocol authoring to eligibility determination. Proc AMIA Symp 2001; 199-203.
- 33 Kamal J, Pasuparthi K, Rogers P, Buskirk J, Mekhjian H. Using an information warehouse to screen patients for clinical trials: A prototype. AMIA Annu Symp Proc 2005; 2005: 1004-1004.
- 34 Breitfeld PP, Ullrich F, Anderson J, Crist WM. Web-based decision support for clinical trial eligibility determination in an international clinical trials network. Controlled Clinical Trials 2003; 24 (06) 702-710.
- 35 Grundmeier RW, Swietlik M, Bell LM. Research subject enrollment by primary care pediatricians using an electronic health record. AMIA Annu Symp Proc 2007; 2007: 289-293.