Evaluation of data completeness in the electronic health record for the purpose of patient recruitment into clinical trials: a retrospective analysis of element presence.

Felix Köpcke,Raphael W Majeed,Hans-Ulrich Prokosch,Rainer Röhrig,Thomas Leusch,Thomas Ganslandt,Joachim Wenk,Benjamin Trinczek,Martin Dugas,Björn Schreiweis,Björn Bergh,Christian Ohmann

doi:10.1186/1472-6947-13-37

Abstract

BackgroundComputerized clinical trial recruitment support is one promising field for the application of routine care data for clinical research. The primary task here is to compare the eligibility criteria defined in trial protocols with patient data contained in the electronic health record (EHR). To avoid the implementation of different patient definitions in multi-site trials, all participating research sites should use similar patient data from the EHR. Knowledge of the EHR data elements which are commonly available from most EHRs is required to be able to define a common set of criteria. The objective of this research is to determine for five tertiary care providers the extent of available data compared with the eligibility criteria of randomly selected clinical trials.MethodsEach participating study site selected three clinical trials at random. All eligibility criteria sentences were broken up into independent patient characteristics, which were then assigned to one of the 27 semantic categories for eligibility criteria developed by Luo et al. We report on the fraction of patient characteristics with corresponding structured data elements in the EHR and on the fraction of patients with available data for these elements. The completeness of EHR data for the purpose of patient recruitment is calculated for each semantic group.Results351 eligibility criteria from 15 clinical trials contained 706 patient characteristics. In average, 55% of these characteristics could be documented in the EHR. Clinical data was available for 64% of all patients, if corresponding data elements were available. The total completeness of EHR data for recruitment purposes is 35%. The best performing semantic groups were ‘age’ (89%), ‘gender’ (89%), ‘addictive behaviour’ (74%), ‘disease, symptom and sign’ (64%) and ‘organ or tissue status’ (61%). No data was available for 6 semantic groups.ConclusionsThere exists a significant gap in structure and content between data documented during patient care and data required for patient eligibility assessment. Nevertheless, EHR data on age and gender of the patient, as well as selected information on his disease can be complete enough to allow for an effective support of the manual screening process with an intelligent preselection of patients and patient data.

Highlights

Computerized clinical trial recruitment support is one promising field for the application of routine care data for clinical research
After manual assignment of each patient characteristic to one of the semantic categories we found a quantitative distribution very similar to that described by Luo et al
We were not able to relate 22 (3%) of our patient characteristics to the proposed categories, mainly because they did not focus on the patient, but on the cause of a symptom (‘organ dysfunction not explained by any chronic disease’), on the outcome (‘failed conservative therapy’) or on specifics of the treatment or the environment of the patient (‘[method of] contraception results in a failure rate less than 1% per year’)

Summary

Introduction

Computerized clinical trial recruitment support is one promising field for the application of routine care data for clinical research. The primary task here is to compare the eligibility criteria defined in trial protocols with patient data contained in the electronic health record (EHR). To avoid the implementation of different patient definitions in multi-site trials, all participating research sites should use similar patient data from the EHR. The objective of this research is to determine for five tertiary care providers the extent of available data compared with the eligibility criteria of randomly selected clinical trials. [2] One important application of secondary use is the identification of patients for recruitment into clinical trials [3]. The primary task here is to compare the eligibility criteria defined in study protocols with patient data contained in the electronic health record (EHR). Systems for automated or semiautomated transformation of eligibility criteria into these computable formats have been developed [6,8]

Objectives

Methods

Results

Discussion

Conclusion