Efficiency and effectiveness evaluation of an automated multi-country patient count cohort system

BackgroundWith the increase of clinical trial costs during the last decades, the design of feasibility studies has become an essential process to reduce avoidable and costly protocol amendments. This design includes timelines, targeted sites and budget, together with a list of eligibility criteria that potential participants need to match.The present work was designed to assess the value of obtaining potential study participant counts using an automated patient count cohort system for large multi-country and multi-site trials: the Electronic Health Records for Clinical Research (EHR4CR) system.MethodsThe evaluation focuses on the accuracy of the patient counts and the time invested to obtain these using the EHR4CR platform compared to the current questionnaire based process. This evaluation will assess the patient counts from ten clinical trials at two different sites. In order to assess the accuracy of the results, the numbers obtained following the two processes need to be compared to a baseline number, the “alloyed” gold standard, which was produced by a manual check of patient records.ResultsThe patient counts obtained using the EHR4CR system were in three evaluated trials more accurate than the ones obtained following the current process whereas in six other trials the current process counts were more accurate. In two of the trials both of the processes had counts within the gold standard’s confidence interval.In terms of efficiency the EHR4CR protocol feasibility system proved to save approximately seven calendar days in the process of obtaining patient counts compared to the current manual process.ConclusionsAt the current stage, electronic health record data sources need to be enhanced with better structured data so that these can be re-used for research purposes. With this kind of data, systems such as the EHR4CR are able to provide accurate objective patient counts in a more efficient way than the current methods.Additional research using both structured and unstructured data search technology is needed to assess the value of unstructured data and to compare the amount of efforts needed for data preparation.

[1]  A Chao,et al.  The applications of capture‐recapture models to epidemiological data , 2001, Statistics in medicine.

[2]  George Hripcsak,et al.  Caveats for the use of operational electronic health record data in comparative effectiveness research. , 2013, Medical care.

[3]  Robert E. Hoyt,et al.  Health Informatics: Practical Guide for Healthcare and Information Technology Professionals , 2010 .

[4]  E. B. Wilson.  Probable Inference, the Law of Succession, and Statistical Inference , 1927 .

[5]  Kenneth A Getz,et al.  Assessing the Impact of Protocol Design Changes on Clinical Trial Performance , 2008, American journal of therapeutics.

[6]  S. Hussain,et al.  The Electronic Healthcare Record for Clinical Research (EHR4CR) information model and terminology. , 2012, Studies in health technology and informatics.

[7]  D Kalra,et al.  Electronic health records: new opportunities for clinical research , 2013, Journal of internal medicine.

[8]  Ergonomic requirements for office work with visual display terminals ( VDTs ) — Part 11 : Guidance on usability , 1998 .

[9]  Douglas MacFadden,et al.  Application of Information Technology The Shared Health Research Information Network ( SHRINE ) : A Prototype Federated Query Tool for Clinical Data Repositories , 2014 .

[10]  J. Powell,et al.  Electronic Health Records Should Support Clinical Research , 2005, Journal of medical Internet research.

[11]  G. Hartvigsen,et al.  Secondary Use of EHR: Data Quality Issues and Informatics Opportunities , 2010, Summit on translational bioinformatics.

[12]  Martin Dugas,et al.  Estimation of Patient Accrual Rates in Clinical Trials Based on Routine Data from Hospital Information Systems , 2009, Methods of Information in Medicine.

[13]  Stuart Speedie,et al.  The Electronic Primary Care Research Network (ePCRN): a new era in practice-based research. , 2006, Journal of the American Board of Family Medicine : JABFM.

[14]  M. Campbell,et al.  What is a pilot or feasibility study? A review of current practice and editorial policy , 2010, BMC medical research methodology.

[15]  Deborah Neville,et al.  Assessing and improving EHR Data Quality (Updated). , 2013, Journal of AHIMA.

[16]  Benjamin Trinczek,et al.  Protocol Feasibility Workflow Using an Automated Multi-country Patient Cohort System , 2014, MIE.

[17]  D Spiegelman,et al.  Measurement error correction for logistic regression models with an "alloyed gold standard". , 1997, American journal of epidemiology.

[18]  V. Rajadhyaksha.  Conducting Feasibilities in Clinical Trials: An Investment to Ensure a Good Study , 2010, Perspectives in clinical research.

[19]  De MoorGeorges,et al.  Using electronic health records for clinical research , 2015 .

[20]  Richard Bache,et al.  An adaptable architecture for patient cohort identification from diverse data sources. , 2013, Journal of the American Medical Informatics Association : JAMIA.

[21]  Iain E. Buchan,et al.  Preserving consent-for-consent with feasibility-assessment and recruitment in clinical studies: FARSITE architecture , 2009, HealthGrid.

[22]  Kenneth Getz,et al.  Improving Protocol Design Feasibility to Drive Drug Development Economics and Performance , 2014, International journal of environmental research and public health.

[23]  I. Cockburn,et al.  Bridging the gap: improving clinical development and the regulatory pathways for health products for neglected diseases , 2010, Clinical trials.

[24]  C M F Kneepkens,et al.  ['Gold standard,' not 'golden standard']. , 2006, Nederlandsch tijdschrift voor geneeskunde.

[25]  Minal Thakkar,et al.  Risks, barriers, and benefits of EHR systems: a comparative study based on size of hospital. , 2006, Perspectives in health information management.

[26]  Hww Potts,et al.  Case Report from the EHR4CR Project—A European Survey on Electronic Health Records Systems for Clinical Research , 2011 .

[27]  Hans-Ulrich Prokosch,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 , 2013, BMC Medical Informatics and Decision Making.