Mobile electronic versus paper case report forms in clinical trials: a randomized controlled trial

Robert Fleischmann, Anne-Marie Decker, Antje Kraft, Knut Mai, Sein Schmidt, Robert Fleischmann, Anne-Marie Decker, Antje Kraft, Knut Mai, Sein Schmidt

Abstract

Background: Regulations, study design complexity and amounts of collected and shared data in clinical trials render efficient data handling procedures inevitable. Recent research suggests that electronic data capture can be key in this context but evidence is insufficient. This randomized controlled parallel group study tested the hypothesis that time efficiency is superior when electronic (eCRF) instead of paper case report forms (pCRF) are used for data collection. We additionally investigated predictors of time saving effects and data integrity.

Methods: This study was conducted on top of a clinical weight loss trial performed at a clinical research facility over six months. All study nurses and patients participating in the clinical trial were eligible to participate and randomly allocated to enter cross-sectional data obtained during routine visits either through pCRF or eCRF. A balanced randomization list was generated before enrolment commenced. 90 and 30 records were gathered for the time that 27 patients and 2 study nurses required to report 2025 and 2037 field values, respectively. The primary hypothesis, that eCRF use is faster than pCRF use, was tested by a two-tailed t-test. Analysis of variance and covariance were used to evaluate predictors of entry performance. Data integrity was evaluated by descriptive statistics.

Results: All randomized patients were included in the study (eCRF group n = 13, pCRF group n = 14). eCRF, as compared to pCRF, data collection was associated with significant time savings across all conditions (8.29 ± 5.15 min vs. 10.54 ± 6.98 min, p = .047). This effect was not defined by participant type, i.e. patients or study nurses (F(1,112) = .15, p = .699), CRF length (F(2,112) = .49, p = .609) or patient age (Beta = .09, p = .534). Additional 5.16 ± 2.83 min per CRF were saved with eCRFs due to data transcription redundancy when patients answered questionnaires directly in eCRFs. Data integrity was superior in the eCRF condition (0 versus 3 data entry errors).

Conclusions: This is the first study to prove in direct comparison that using eCRFs instead of pCRFs increases time efficiency of data collection in clinical trials, irrespective of item quantity or patient age, and improves data quality.

Trial registration: Clinical Trials NCT02649907 .

Keywords: Data handling; Electronic case report form; REDCap; Time efficiency.

Conflict of interest statement

Authors’ information

Not applicable.

Ethics approval and consent to participate

This study was approved by the local ethics committee review board and complied with local data protection regulations Study nurses gave oral informed consent while patients had to provide written informed consent prior to data collection.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Summary of the study design. A nutritional assessment study served as use case to test our hypotheses regarding data collection methods in clinical trials. In this study, patient information was collected in two ways. Patients either entered information themselves into standardized instruments such as questionnaires or information about the patient was obtained by a study nurse and then entered. Entering patient information could be done in two ways that included either direct access to the digital database through an electronic case report form (eCRF) or indirect access by filling a paper based case report form (pCRF) that was subsequently transferred to the database (pCRF to eCRF). Precise records were taken for times required for data entry by either CRF type, subject type performing data entry and instrument type being used
Fig. 2
Fig. 2
CONSORT 2010 Flow Diagram. This diagram illustrates that all patients that were assessed for eligibility to participate in this study also agreed to participate. Non of the included patients withdrew consent or decided not to use the randomized data entry method. Given the cross-sectional design without follow-up there was also no loss to follow-up. Study nurses were not included in this diagram since they were not allocated one particular intervention, i.e. data entry method, but changed methods between visits
Fig. 3
Fig. 3
Plot of average time consumption for data entry procedures. Patients and study nurses could enter data to the database in two ways; they either entered data directly through eCRF or first to a pCRF whose content was subsequently transferred from pCRF to eCRF resulting in a pCRF total time. Results show that entering data through eCRF is significantly faster than the complete pCRF procedure (‑2.25 ± .99 min, p = .047). Errors bars represent standard deviation

References

    1. Number of Registered Studies Over Time. In: Trends, Charts, and Maps. 2016. . Accessed 11 Dec 2016.
    1. Jones CE, Munoz FM, Kochhar S, Vergnano S, Cutland CL, Steinhoff M, Black S, Heininger U, Bonhoeffer J, Heath PT. Guidance for the collection of case report form variables to assess safety in clinical trials of vaccines in pregnancy. Vaccine. 2016;34(49):6007–6014. doi: 10.1016/j.vaccine.2016.07.007.
    1. Jha AK, Burke MF, DesRoches C, Joshi MS, Kralovec PD, Campbell EG, Buntin MB. Progress toward meaningful use: hospitals’ adoption of electronic health records. Am J Manag Care. 2011;17(12 Spec No.):SP117–SP124.
    1. Holroyd-Leduc JM, Lorenzetti D, Straus SE, Sykes L, Quan H. The impact of the electronic medical record on structure, process, and outcomes within primary care: a systematic review of the evidence. J Am Med Inform Assoc. 2011;18(6):732–737. doi: 10.1136/amiajnl-2010-000019.
    1. Fleischmann R, Duhm J, Hupperts H, Brandt SA. Tablet computers with mobile electronic medical records enhance clinical routine and promote bedside time: a controlled prospective crossover study. J Neurol. 2015;262(3):532–540. doi: 10.1007/s00415-014-7581-7.
    1. Duhm J, Fleischmann R, Schmidt S, Hupperts H, Brandt SA. Mobile electronic medical records promote workflow: Physicians’ perspective from a survey. JMIR mHealth uHealth. 2016;4(2):e70. doi: 10.2196/mhealth.5464.
    1. Jones WS, Roe MT, Antman EM, Pletcher MJ, Harrington RA, Rothman RL, Oetgen WJ, Rao SV, Krucoff MW, Curtis LH, et al. The changing landscape of randomized clinical trials in cardiovascular disease. J Am Coll Cardiol. 2016;68(17):1898–1907. doi: 10.1016/j.jacc.2016.07.781.
    1. Welker JA. Implementation of electronic data capture systems: barriers and solutions. Contemp Clin Trials. 2007;28(3):329–336. doi: 10.1016/j.cct.2007.01.001.
    1. Cleland J, Caldow J, Ryan D. A qualitative study of the attitudes of patients and staff to the use of mobile phone technology for recording and gathering asthma data. J Telemed Telecare. 2007;13(2):85–89. doi: 10.1258/135763307780096230.
    1. Borlawsky TB, Lele O, Jensen D, Hood NE, Wewers ME. Enabling distributed electronic research data collection for a rural Appalachian tobacco cessation study. J Am Med Inform Assoc. 2011;18(Suppl 1):i140–i143. doi: 10.1136/amiajnl-2011-000354.
    1. Azad TD, Kalani M, Wolf T, Kearney A, Lee Y, Flannery L, Chen D, Berroya R, Eisenberg M, Park J, et al. Building an electronic health record integrated quality of life outcomes registry for spine surgery. J Neurosurg Spine. 2016;24(1):176–185. doi: 10.3171/2015.3.SPINE141127.
    1. Pace WD, Staton EW. Electronic data collection options for practice-based research networks. Ann Fam Med. 2005;3(Suppl 1):S21–S29. doi: 10.1370/afm.270.
    1. Bushnell DM, Martin ML, Parasuraman B. Electronic versus paper questionnaires: a further comparison in persons with asthma. J Asthma. 2003;40(7):751–762. doi: 10.1081/JAS-120023501.
    1. Farnell DJ, Routledge J, Hannon R, Logue JP, Cowan RA, Wylie JP, Barraclough LH, Livsey JE, Swindell R, Davidson SE. Efficacy of data capture for patient-reported toxicity following radiotherapy for prostate or cervical cancer. Eur J Cancer. 2010;46(3):534–540. doi: 10.1016/j.ejca.2009.11.017.
    1. Jamison RN, Raymond SA, Levine JG, Slawsby EA, Nedeljkovic SS, Katz NP. Electronic diaries for monitoring chronic pain: 1-year validation study. Pain. 2001;91(3):277–285. doi: 10.1016/S0304-3959(00)00450-4.
    1. Dupont A, Wheeler J, Herndon JE, 2nd, Coan A, Zafar SY, Hood L, Patwardhan M, Shaw HS, Lyerly HK, Abernethy AP. Use of tablet personal computers for sensitive patient-reported information. J Support Oncol. 2009;7(3):91–97.
    1. Haller G, Haller DM, Courvoisier DS, Lovis C. Handheld vs. laptop computers for electronic data collection in clinical research: a crossover randomized trial. J Am Med Inform Assoc. 2009;16(5):651–659. doi: 10.1197/jamia.M3041.
    1. Dillon DG, Pirie F, Rice S, Pomilla C, Sandhu MS, Motala AA, Young EH. African Partnership for Chronic Disease R: open-source electronic data capture system offered increased accuracy and cost-effectiveness compared with paper methods in Africa. J Clin Epidemiol. 2014;67(12):1358–1363. doi: 10.1016/j.jclinepi.2014.06.012.
    1. Nahm M, Shepherd J, Buzenberg A, Rostami R, Corcoran A, McCall J, Pietrobon R. Design and implementation of an institutional case report form library. Clin Trials. 2011;8(1):94–102. doi: 10.1177/1740774510391916.
    1. Schweitzer M, Oberbichler S. Requirements for evidence-based templates in electronic case report forms. Stud Health Technol Inform. 2016;223:142–149.
    1. Njuguna HN, Caselton DL, Arunga GO, Emukule GO, Kinyanjui DK, Kalani RM, Kinkade C, Muthoka PM, Katz MA, Mott JA. A comparison of smartphones to paper-based questionnaires for routine influenza sentinel surveillance, Kenya, 2011–2012. BMC Med Inform Decis Making. 2014;14:107. doi: 10.1186/s12911-014-0107-5.
    1. Le Jeannic A, Quelen C, Alberti C, Durand-Zaleski I, CompaRec I. Comparison of two data collection processes in clinical studies: electronic and paper case report forms. BMC Med Res Methodol. 2014;14:7. doi: 10.1186/1471-2288-14-7.
    1. Pavlovic I, Kern T, Miklavcic D. Comparison of paper-based and electronic data collection process in clinical trials: costs simulation study. Contemp Clin Trials. 2009;30(4):300–316. doi: 10.1016/j.cct.2009.03.008.
    1. Kinnula S, Renko M, Tapiainen T, Pokka T, Uhari M. Post-discharge follow-up of hospital-associated infections in paediatric patients with conventional questionnaires and electronic surveillance. J Hosp Infect. 2012;80(1):13–16. doi: 10.1016/j.jhin.2011.09.005.
    1. Campbell N, Ali F, Finlay AY, Salek SS. Equivalence of electronic and paper-based patient-reported outcome measures. Qual Life Res. 2015;24(8):1949–1961. doi: 10.1007/s11136-015-0937-3.
    1. Rorie DA, Flynn RWV, Grieve K, Doney A, Mackenzie I, MacDonald TM, et al. Electronic case report forms and electronic data capture within clinical trials and pharmacoepidemiology. British journal of clinical pharmacology. 2017;83(9):1880-95.
    1. Eisenstein EL, Collins R, Cracknell BS, Podesta O, Reid ED, Sandercock P, Shakhov Y, Terrin ML, Sellers MA, Califf RM, et al. Sensible approaches for reducing clinical trial costs. Clin Trials. 2008;5(1):75–84. doi: 10.1177/1740774507087551.
    1. World Medical A World medical association declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA. 2013;310(20):2191–2194. doi: 10.1001/jama.2013.281053.
    1. Salaffi F, Gasparini S, Ciapetti A, Gutierrez M, Grassi W. Usability of an innovative and interactive electronic system for collection of patient-reported data in axial spondyloarthritis: comparison with the traditional paper-administered format. Rheumatology (Oxford) 2013;52(11):2062–2070. doi: 10.1093/rheumatology/ket276.
    1. Walther B, Hossin S, Townend J, Abernethy N, Parker D, Jeffries D. Comparison of electronic data capture (EDC) with the standard data capture method for clinical trial data. PLoS One. 2011;6(9):e25348. doi: 10.1371/journal.pone.0025348.
    1. Booth M. Assessment of physical activity: an international perspective. Res Q Exerc Sport. 2000;71(2 Suppl):S114–S120. doi: 10.1080/02701367.2000.11082794.
    1. Brazier JE, Harper R, Jones NM, O'Cathain A, Thomas KJ, Usherwood T, Westlake L. Validating the SF-36 health survey questionnaire: new outcome measure for primary care. BMJ. 1992;305(6846):160–164. doi: 10.1136/bmj.305.6846.160.
    1. Roenneberg T, Wirz-Justice A, Merrow M. Life between clocks: daily temporal patterns of human chronotypes. J Biol Rhythm. 2003;18(1):80–90. doi: 10.1177/0748730402239679.
    1. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377–381. doi: 10.1016/j.jbi.2008.08.010.
    1. Ali S, Powers R, Beorse J, Noor A, Naureen F, Anjum N, Ishaq M, Aamir J, Anderson R. ODK scan: digitizing data collection and impacting data management processes in Pakistan’s tuberculosis control program. Futur Internet. 2016;8(4):51. doi: 10.3390/fi8040051.
    1. Schmidt M, Shwarz-Boeger U, Harbeck N, Harzendorf N, Paepke S, Kiechle M, et al. EHR and EDC Integration in Reality. Applied Clinical Trials [Internet]. 2009. Available from: . Accessed 11 Dec 2016.
    1. Thriemer K, Ley B, Ame SM, Puri MK, Hashim R, Chang NY, Salim LA, Ochiai RL, Wierzba TF, Clemens JD, et al. Replacing paper data collection forms with electronic data entry in the field: findings from a study of community-acquired bloodstream infections in Pemba, Zanzibar. BMC Res Notes. 2012;5:113. doi: 10.1186/1756-0500-5-113.
    1. Robotham D, Satkunanathan S, Doughty L, Wykes T. Do we still have a digital divide in mental health? A five-year survey follow-up. J Med Internet Res. 2016;18(11):e309. doi: 10.2196/jmir.6511.
    1. Food and Drug Administration (FDA) Guidance for industry. Silver Spring: U.S. Department of Health and Human Services; 2013. Electronic source data in clinical investigations; pp. 1–11.
    1. El Fadly A, Lucas N, Rance B, Verplancke P, Lastic PY, Daniel C. The REUSE project: EHR as single datasource for biomedical research. Stud Health Technol Inform. 2010;160(Pt 2):1324–1328.

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