A European inventory of common electronic health record data elements for clinical trial feasibility

Trials

Volumn 15, Issue 1, 2014, Pages

  Doods, Justin ^a   Botteri, Florence ^b   Dugas, Martin ^a   Fritz, Fleur ^a  

^a UNIVERSITY OF MÜNSTER   (Germany)

^b NOVARTIS PHARMA AG   (Switzerland)

Author keywords

Clinical information system; Clinical trials; Data elements; Electronic health record; Feasibility criteria

Indexed keywords

ELEMENT;

ARTICLE; CLINICAL TRIAL (TOPIC); CONTROLLED STUDY; DRUG INDUSTRY; ELECTRONIC MEDICAL RECORD; EUROPE; FEASIBILITY STUDY; HEALTH CARE AVAILABILITY; HUMAN; MEDICAL INFORMATION SYSTEM; MEDICAL SPECIALIST; PATIENT CARE; UNIVERSITY HOSPITAL;

CLINICAL TRIALS AS TOPIC; DATA MINING; ELECTRONIC HEALTH RECORDS; EUROPE; FEASIBILITY STUDIES; HOSPITALS, UNIVERSITY; HUMANS; MEDICAL INFORMATICS; PATIENT SELECTION; RESEARCH DESIGN; SAMPLE SIZE;

EID: 84892414453     PISSN: None     EISSN: 17456215     Source Type: Journal    
DOI: 10.1186/1745-6215-15-18     Document Type: Article

References (23)

1. McDonald AM, Knight RC, Campbell MK, Entwistle VA, Grant AM, Cook JA, Elbourne DR, Francis D, Garcia J, Roberts I, Snowdon C. What influences recruitment to randomized controlled trials? A review of trials funded by two UK funding agencies. Trials 2006, 7:9. 10.1186/1745-6215-7-9, 1475627, 16603070.
2. Van der Wouden JC, Blankenstein AH, Huibers MJH, van der Windt D a WM, Stalman WAB, Verhagen AP. Survey among 78 studies showed that Lasagna's law holds in Dutch primary care research. J Clin Epidemiol 2007, 60:819-824. 10.1016/j.jclinepi.2006.11.010, 17606178.
3. Dugas M, Lange M, Müller-Tidow C, Kirchhof P, Prokosch H-U. Routine data from hospital information systems can support patient recruitment for clinical studies. Clinical Trials (London, England) 2010, 7:183-189.
4. Innovative Medicines Initiative. [http://www.imi.europa.eu/], Last accessed: November 2013.
5. Electronic Health Records for Clinical Research. [http://www.ehr4cr.eu], Last accessed: 15.11.2013.
6. International Organization for Standardization/International Electrotechnical Commission ISO/IEC 11179, Information Technology - Metadata Registries (MDR) - Part 1: Framework 2004, [http://standards.iso.org/ittf/PubliclyAvailableStandards/c035343_ISO_IEC_11179-1_2004(E).zip]; Last accessed: 29.12.2013, International Organization for Standardization/International Electrotechnical Commission.
7. Unified Medical Language System. [http://www.nlm.nih.gov/research/umls/], Last accessed: November 2013.
8. Doods J, Holzapfel K, Dugas M, Fritz F. Development of best practice principles for simplifying eligibility criteria. Stud Health Technol Inform 2013, 192:1153.
9. Microsoft. [http://www.microsoft.com]; Last accessed: 29.12.2013.
10. NCIm CUI. [http://ncimeta.nci.nih.gov/ncimbrowser/ConceptReport.jsp?dictionary=NCI%MetaThesaurus&code=C2348662], Last accessed: November 2013.
11. NCI Metathesaurus. [http://ncimeta.nci.nih.gov/ncimbrowser/], Last accessed: November 2013.
12. Weng C, Wu X, Luo Z, Boland MR, Theodoratos D, Johnson SB. EliXR: an approach to eligibility criteria extraction and representation. J Am Med Inform Assoc 2011, 18(Suppl 1):i116-i124. 10.1136/amiajnl-2011-000321, 3241167, 21807647.
13. Luo Z, Yetisgen-Yildiz M, Weng C. Dynamic categorization of clinical research eligibility criteria by hierarchical clustering. J Biomed Inform 2011, 44:927-935. 10.1016/j.jbi.2011.06.001, 3183114, 21689783.
14. Ammenwerth E, Spötl H-P. The time needed for clinical documentation versus direct patient care. Methods Inf Med 2009, 48:84-91.
15. Tipping M, Forth V. Where did the day go? - A time-motion study of hospitalists. J Hospital 2010, 5:323-328.
16. Tu S, Peleg M, Carini S, Rubin D, Sim I. ERGO: a template-based expression language for encoding eligibility criteria. 2008, Technical report.
17. Wang SJ, Ohno-machado L, Mar P, Boxwala AA, Greenes RA. Enhancing Arden Syntax for clinical trial eligibility criteria. 1999, 8280.
18. Weintraub WS, Karlsberg RP, Tcheng JE, Boris JR, Buxton AE, Dove JT, Fonarow GC, Goldberg LR, Heidenreich P, Hendel RC, Jacobs AK, Lewis W, Mirro MJ, Shahian DM, Bozkurt B, Jacobs JP, Peterson PN, Roger VL, Smith EE, Wang T. ACCF/AHA 2011 key data elements and definitions of a base cardiovascular vocabulary for electronic health records: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Clinical Data Standards. Circulation 2011, 124:103-123.
19. Häyrinen K, Saranto K. The core data elements of electronic health record in Finland. Stud Health Technol Inform 2005, 116:131-136.
20. 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. 10.1186/1472-6947-13-37, 3606452, 23514203.
21. Schreyögg J, Stargardt T, Tiemann O, Busse R. Methods to determine reimbursement rates for diagnosis related groups (DRG): a comparison of nine European countries. Health Care Manag Sci 2006, 9:215-223. 10.1007/s10729-006-9040-1, 17016927.
22. Weiskopf NG, Weng C. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J Am Med Inform Assoc 2013, 20:144-151. 10.1136/amiajnl-2011-000681, 3555312, 22733976.
23. Häyrinen K, Saranto K, Nykänen P. Definition, structure, content, use and impacts of electronic health records: a review of the research literature. Int J Med Inform 2008, 77:291-304. 10.1016/j.ijmedinf.2007.09.001, 17951106.