Yearb Med Inform 2015; 24(01): 174-177
DOI: 10.15265/IY-2015-010
Original Article
Georg Thieme Verlag KG Stuttgart

Clinical Research Informatics: Recent Advances and Future Directions

M. Dugas
1   Institute of Medical Informatics, University of Münster, Germany
2   European Research Center for information systems (ERCIS)
› Institutsangaben
Weitere Informationen

Correspondence to:

Prof. Dr. Martin Dugas
Institute of Medical Informatics
University of Münster
Albert-Schweitzer-Campus 1 | A11
D-48149 Münster, Germany
Telefon: +49 251 83 55262   

Publikationsverlauf

13. August 2015

Publikationsdatum:
10. März 2018 (online)

 

Summary

Objectives: To summarize significant developments in Clinical Research Informatics (CRI) over the past two years and discuss future directions.

Methods: Survey of advances, open problems and opportunities in this field based on exploration of current literature.

Results: Recent advances are structured according to three use cases of clinical research: Protocol feasibility, patient identification/recruitment and clinical trial execution.

Discussion: CRI is an evolving, dynamic field of research. Global collaboration, open metadata, content standards with semantics and computable eligibility criteria are key success factors for future developments in CRI.


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* http://www.ercis.org


  • References

  • 1 Embi PJ. Clinical research informatics: survey of recent advances and trends in a maturing field. Yearb Med Inform 2013; 8 (01) 178-84.
  • 2 De Moor G, Sundgren M, Kalra D, Schmidt A, Dugas M, Claerhout B. et al. Using electronic health records for clinical research: the case of the EHR-4CR project. J Biomed Inform 2015; 53: 162-73.
  • 3 Coorevits P, Sundgren M, Klein GO, Bahr A, Claerhout B, Daniel C. et al. Electronic health records: new opportunityes for clinical research. J Intern Med 2013; 274 (06) 547-60.
  • 4 Doods J, Bache R, McGilchrist M, Daniel C, Dugas M, Fritz F. Work Package 7. Piloting the EHR4CR feasibility platform across Europe. Methods Inf Med 2014; 53 (04) 264-8.
  • 5 Bache R, Taweel A, Miles S, Delaney BC. An Eligibility Criteria Query Language for Heterogeneous Data Warehouses. Methods Inf Med 2015; 54 (01) 41-4.
  • 6 Hussain S, Sun H, Sinaci A, Erturkmen GB, Mead C, Gray AJ. et al. A framework for evaluating and utilizing medical terminology mappings. Stud Health Technol Inform 2014; 205: 594-8.
  • 7 McMurry AJ, Murphy SN, MacFadden D, Weber G, Simons WW, Orechia J. et al. SHRINE: enabling nationally scalable multi-site disease studies. PLoS One 2013; 8 (03) e55811.
  • 8 Doods J, Dugas M, Fritz F. on behalf of EHR4CR WP7. A European inventory of common EHR data elements for clinical trial feasibility. Trials 2014; 15: 18.
  • 9 Dugas M, Lange M, Berdel WE, Müller-Tidow C. Workflow to improve patient recruitment for clinical trials within hospital information systems - a case-study. Trials 2008; 9: 2.
  • 10 Trinczek B, Köpcke F, Leusch T, Majeed RW, Schreiweis B, Wenk J. et al. Design and multicentric Implementation of a generic Software Architecture for Patient Recruitment Systems reusing existing HIS tools and Routine Patient Data. Appl Clinl Inform 2014; 5 (01) 264-83.
  • 11 Schreiweis B, Trinczek B, Köpcke F, Leusch T, Majeed RW, Wenk J. et al. Comparison of Electronic Health Record System Functionalities to support the patient recruitment process in clinical trials. Int J Med Inform 2014; 83: 860-8.
  • 12 Köpcke F, Trinczek B, Majeed RW, Schreiweis B, Wenk J, Leusch T. et al. 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.
  • 13 Rahimi A, Liaw ST, Taggart J, Ray P, Yu H. Validating an ontology-based algorithm to identify patients with Type 2 Diabetes Mellitus in Electronic Health Records. Int J Med Inform 2014; 83: 768-78.
  • 14 Abhyankar S, Demner-Fushman D, Callaghan FM, McDonald CJ. Combining structured and unstructured data to identify a cohort of ICU patients who received dialysis. J Am Med Inform Assoc 2014; 21 (05) 801-7.
  • 15 Afrin LB, Oates JC, Kamen DL. Improving clinical trial accrual by streamlining the referral process. Int J Med Inform 2015; 84 (01) 15-23.
  • 16 Shivade C, Raghavan P, Fosler-Lussier E, Embi PJ, Elhadad N, Johnson SB. et al. A review of approaches to identifying patient phenotype cohorts using electronic health records. J Am Med Inform Assoc 2014; 21 (02) 221-30.
  • 17 Getz K. Protocol Design Trends and their Effect on Clinical Trial Performance. RAJ Pharma 2008; 5: 315-6.
  • 18 El Fadly A, Rance B, Lucas N, Mead C, Chatellier G, Lastic PY. et al. Integrating clinical research with the Healthcare Enterprise: from the RE-USE project to the EHR4CR platform. J Biomed Inform 2011; Dec 44 Suppl 1: S94-102.
  • 19 Bruland P, Forster C, Breil B, Ständer S, Dugas M, Fritz F. Does single-source create an added value? Evaluating the impact of introducing x4T into the clinical routine on workflow modifications, data quality and cost-benefit. Int J Med Inform 2014; Dec 83 (12) 915-28.
  • 20 Köpcke F, Kraus S, Scholler A, Nau C, Schüttler J, Prokosch HU. et al. Secondary use of routinely collected patient data in a clinical trial: An evaluation of the effects on patient recruitment and data acquisition. Int J Med Inform 2013; 82 (03) 185-92.
  • 21 Cannon CP, Brindis RG, Chaitman BR, Cohen DJ, Cross Jr JT, Drozda Jr JP. et al. 2013 ACCF/AHA key data elements and definitions for measuring the clinical management and outcomes of patients with acute coronary syndromes and coronary artery disease: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Clinical Data Standards. Circulation 2013; 127 (09) 1052-89.
  • 22 Embi PJ, Payne PR. Advancing methodologies in Clinical Research Informatics (CRI): Foundational work for a maturing field. J Biomed Inform 2014; 52: 1-3.
  • 23 Bhattacharya S, Cantor MN. Analysis of eligibility criteria representation in industry-standard clinical trial protocols. J Biomed Inform 2013; 46 (05) 805-13.
  • 24 Geissbuhler A, Safran C, Buchan I, Bellazzi R, Labkoff S, Eilenberg K. et al. Trustworthy reuse of health data: a transnational perspective. Int J Med Inform 2013; 82 (01) 1-9.
  • 25 REGULATION (EU) No 536/2014 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 16 April 2014 on clinical trials on medicinal products for human use. Available from: http://ec.europa.eu/health/human-use/clinicaltrials/regulation/ [accessed 2015 August 03]
  • 26 Dugas M. Why we need a large-scale open meta-data initiative in health informatics - a vision paper on open data models for clinical phenotypes. Stud Health Technol Inform 2013; 192: 899-902.
  • 27 Dugas M, Jöckel KH, Friede T, Gefeller O, Kieser M, Marschollek M. et al. Memorandum “Open Metadata”. Open Access to Documentation Forms and Item Catalogs in Healthcare. Methods Inf Med 2015 Jun 25;54(4).
  • 28 Breil B, Kenneweg J, Fritz F, Bruland P, Doods J, Trinczek B. et al. Multilingual Medical Data Models in ODM Format. Appl Clin Inform 2012; 3: 276-89.
  • 29 Newton KM, Peissig PL, Kho AN, Bielinski SJ, Berg RL, Choudhary V. et al. Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network. J Am Med Inform Assoc 2013; 20 e1 e147-54.
  • 30 McMurry AJ, Murphy SN, MacFadden D, Weber G, Simons WW, Orechia J. et al. SHRINE: enabling nationally scalable multi-site disease studies. PLoS One 2013; 8 (03) e55811.
  • 31 Pathak J, Bailey KR, Beebe CE, Bethard S, Carrell DC, Chen PJ. et al. Normalization and standardization of electronic health records for high-throughput phenotyping: the SHARPn consortium. J Am Med Inform Assoc 2013; 20 e2 e341-8.
  • 32 Dugas M. Missing semantic annotation in databases - the root cause for data integration and migration problems in information systems. Methods Inf Med 2014; 53 (06) 516-7.
  • 33 Dugas M, Fritz F, Krumm R, Breil B. Automated UMLS-based Comparison of Medical Forms. PLoS One 2013; 8 (07) e67883.
  • 34 Krumm R, Semjonow A, Tio J, Duhme H, Bürkle T, Haier J. et al. The Need for Harmonized Structured Documentation and Chances of Secondary Use -Results of a Systematic Analysis with Automated Form Comparison for Prostate and Breast Cancer. J Biomed Inform 2014; 51: 86-99.
  • 35 Cimino JJ, Ayres EJ, Remennik L, Rath S, Freedman R, Beri A. et al. The National Institutes of Health’s Biomedical Translational Research Information System (BTRIS): Design, contents, functionality and experience to date. J Biomed Inform 2014; 52: 11-27.
  • 36 Zhu Q, Freimuth RR, Lian Z, Bauer S, Pathak J, Tao C. et al. Harmonization and semantic annotation of data dictionaries from the Pharmacogenomics Research Network: a case study. J Biomed Inform 2013; 46 (02) 286-93.
  • 37 Miotto R, Weng C. Unsupervised mining of frequent tags for clinical eligibility text indexing. J Biomed Inform 2013; 46 (06) 1145-51.
  • 38 Hao T, Rusanov A, Boland MR, Weng C. Clustering clinical trials with similar eligibility criteria features. J Biomed Inform 2014; 52: 112-20.
  • 39 Varghese J, Dugas M. Most Frequent Medical Concepts in Clinical Trial Eligibility Criteria and their Coverage in MeSH and SNOMED-CT. Methods Inf Med 2015; 54 (01) 83-92.

Correspondence to:

Prof. Dr. Martin Dugas
Institute of Medical Informatics
University of Münster
Albert-Schweitzer-Campus 1 | A11
D-48149 Münster, Germany
Telefon: +49 251 83 55262   

  • References

  • 1 Embi PJ. Clinical research informatics: survey of recent advances and trends in a maturing field. Yearb Med Inform 2013; 8 (01) 178-84.
  • 2 De Moor G, Sundgren M, Kalra D, Schmidt A, Dugas M, Claerhout B. et al. Using electronic health records for clinical research: the case of the EHR-4CR project. J Biomed Inform 2015; 53: 162-73.
  • 3 Coorevits P, Sundgren M, Klein GO, Bahr A, Claerhout B, Daniel C. et al. Electronic health records: new opportunityes for clinical research. J Intern Med 2013; 274 (06) 547-60.
  • 4 Doods J, Bache R, McGilchrist M, Daniel C, Dugas M, Fritz F. Work Package 7. Piloting the EHR4CR feasibility platform across Europe. Methods Inf Med 2014; 53 (04) 264-8.
  • 5 Bache R, Taweel A, Miles S, Delaney BC. An Eligibility Criteria Query Language for Heterogeneous Data Warehouses. Methods Inf Med 2015; 54 (01) 41-4.
  • 6 Hussain S, Sun H, Sinaci A, Erturkmen GB, Mead C, Gray AJ. et al. A framework for evaluating and utilizing medical terminology mappings. Stud Health Technol Inform 2014; 205: 594-8.
  • 7 McMurry AJ, Murphy SN, MacFadden D, Weber G, Simons WW, Orechia J. et al. SHRINE: enabling nationally scalable multi-site disease studies. PLoS One 2013; 8 (03) e55811.
  • 8 Doods J, Dugas M, Fritz F. on behalf of EHR4CR WP7. A European inventory of common EHR data elements for clinical trial feasibility. Trials 2014; 15: 18.
  • 9 Dugas M, Lange M, Berdel WE, Müller-Tidow C. Workflow to improve patient recruitment for clinical trials within hospital information systems - a case-study. Trials 2008; 9: 2.
  • 10 Trinczek B, Köpcke F, Leusch T, Majeed RW, Schreiweis B, Wenk J. et al. Design and multicentric Implementation of a generic Software Architecture for Patient Recruitment Systems reusing existing HIS tools and Routine Patient Data. Appl Clinl Inform 2014; 5 (01) 264-83.
  • 11 Schreiweis B, Trinczek B, Köpcke F, Leusch T, Majeed RW, Wenk J. et al. Comparison of Electronic Health Record System Functionalities to support the patient recruitment process in clinical trials. Int J Med Inform 2014; 83: 860-8.
  • 12 Köpcke F, Trinczek B, Majeed RW, Schreiweis B, Wenk J, Leusch T. et al. 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.
  • 13 Rahimi A, Liaw ST, Taggart J, Ray P, Yu H. Validating an ontology-based algorithm to identify patients with Type 2 Diabetes Mellitus in Electronic Health Records. Int J Med Inform 2014; 83: 768-78.
  • 14 Abhyankar S, Demner-Fushman D, Callaghan FM, McDonald CJ. Combining structured and unstructured data to identify a cohort of ICU patients who received dialysis. J Am Med Inform Assoc 2014; 21 (05) 801-7.
  • 15 Afrin LB, Oates JC, Kamen DL. Improving clinical trial accrual by streamlining the referral process. Int J Med Inform 2015; 84 (01) 15-23.
  • 16 Shivade C, Raghavan P, Fosler-Lussier E, Embi PJ, Elhadad N, Johnson SB. et al. A review of approaches to identifying patient phenotype cohorts using electronic health records. J Am Med Inform Assoc 2014; 21 (02) 221-30.
  • 17 Getz K. Protocol Design Trends and their Effect on Clinical Trial Performance. RAJ Pharma 2008; 5: 315-6.
  • 18 El Fadly A, Rance B, Lucas N, Mead C, Chatellier G, Lastic PY. et al. Integrating clinical research with the Healthcare Enterprise: from the RE-USE project to the EHR4CR platform. J Biomed Inform 2011; Dec 44 Suppl 1: S94-102.
  • 19 Bruland P, Forster C, Breil B, Ständer S, Dugas M, Fritz F. Does single-source create an added value? Evaluating the impact of introducing x4T into the clinical routine on workflow modifications, data quality and cost-benefit. Int J Med Inform 2014; Dec 83 (12) 915-28.
  • 20 Köpcke F, Kraus S, Scholler A, Nau C, Schüttler J, Prokosch HU. et al. Secondary use of routinely collected patient data in a clinical trial: An evaluation of the effects on patient recruitment and data acquisition. Int J Med Inform 2013; 82 (03) 185-92.
  • 21 Cannon CP, Brindis RG, Chaitman BR, Cohen DJ, Cross Jr JT, Drozda Jr JP. et al. 2013 ACCF/AHA key data elements and definitions for measuring the clinical management and outcomes of patients with acute coronary syndromes and coronary artery disease: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Clinical Data Standards. Circulation 2013; 127 (09) 1052-89.
  • 22 Embi PJ, Payne PR. Advancing methodologies in Clinical Research Informatics (CRI): Foundational work for a maturing field. J Biomed Inform 2014; 52: 1-3.
  • 23 Bhattacharya S, Cantor MN. Analysis of eligibility criteria representation in industry-standard clinical trial protocols. J Biomed Inform 2013; 46 (05) 805-13.
  • 24 Geissbuhler A, Safran C, Buchan I, Bellazzi R, Labkoff S, Eilenberg K. et al. Trustworthy reuse of health data: a transnational perspective. Int J Med Inform 2013; 82 (01) 1-9.
  • 25 REGULATION (EU) No 536/2014 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 16 April 2014 on clinical trials on medicinal products for human use. Available from: http://ec.europa.eu/health/human-use/clinicaltrials/regulation/ [accessed 2015 August 03]
  • 26 Dugas M. Why we need a large-scale open meta-data initiative in health informatics - a vision paper on open data models for clinical phenotypes. Stud Health Technol Inform 2013; 192: 899-902.
  • 27 Dugas M, Jöckel KH, Friede T, Gefeller O, Kieser M, Marschollek M. et al. Memorandum “Open Metadata”. Open Access to Documentation Forms and Item Catalogs in Healthcare. Methods Inf Med 2015 Jun 25;54(4).
  • 28 Breil B, Kenneweg J, Fritz F, Bruland P, Doods J, Trinczek B. et al. Multilingual Medical Data Models in ODM Format. Appl Clin Inform 2012; 3: 276-89.
  • 29 Newton KM, Peissig PL, Kho AN, Bielinski SJ, Berg RL, Choudhary V. et al. Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network. J Am Med Inform Assoc 2013; 20 e1 e147-54.
  • 30 McMurry AJ, Murphy SN, MacFadden D, Weber G, Simons WW, Orechia J. et al. SHRINE: enabling nationally scalable multi-site disease studies. PLoS One 2013; 8 (03) e55811.
  • 31 Pathak J, Bailey KR, Beebe CE, Bethard S, Carrell DC, Chen PJ. et al. Normalization and standardization of electronic health records for high-throughput phenotyping: the SHARPn consortium. J Am Med Inform Assoc 2013; 20 e2 e341-8.
  • 32 Dugas M. Missing semantic annotation in databases - the root cause for data integration and migration problems in information systems. Methods Inf Med 2014; 53 (06) 516-7.
  • 33 Dugas M, Fritz F, Krumm R, Breil B. Automated UMLS-based Comparison of Medical Forms. PLoS One 2013; 8 (07) e67883.
  • 34 Krumm R, Semjonow A, Tio J, Duhme H, Bürkle T, Haier J. et al. The Need for Harmonized Structured Documentation and Chances of Secondary Use -Results of a Systematic Analysis with Automated Form Comparison for Prostate and Breast Cancer. J Biomed Inform 2014; 51: 86-99.
  • 35 Cimino JJ, Ayres EJ, Remennik L, Rath S, Freedman R, Beri A. et al. The National Institutes of Health’s Biomedical Translational Research Information System (BTRIS): Design, contents, functionality and experience to date. J Biomed Inform 2014; 52: 11-27.
  • 36 Zhu Q, Freimuth RR, Lian Z, Bauer S, Pathak J, Tao C. et al. Harmonization and semantic annotation of data dictionaries from the Pharmacogenomics Research Network: a case study. J Biomed Inform 2013; 46 (02) 286-93.
  • 37 Miotto R, Weng C. Unsupervised mining of frequent tags for clinical eligibility text indexing. J Biomed Inform 2013; 46 (06) 1145-51.
  • 38 Hao T, Rusanov A, Boland MR, Weng C. Clustering clinical trials with similar eligibility criteria features. J Biomed Inform 2014; 52: 112-20.
  • 39 Varghese J, Dugas M. Most Frequent Medical Concepts in Clinical Trial Eligibility Criteria and their Coverage in MeSH and SNOMED-CT. Methods Inf Med 2015; 54 (01) 83-92.