Electronic Screening Improves Efficiency in Clinical Trial Recruitment

Thadani, Samir R.; Weng, Chunhua; Bigger, J. Thomas; Ennever, John F.; Wajngurt, David

doi:10.1197/jamia.m3119

Cited by 116 publications

(96 citation statements)

References 3 publications

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“…Authors in one study compared an e-screening strategy with investigator review for recruitment into a clinical trial with a low prevalence of eligible patients. 36 The e-screening approach showed an over fivefold improvement on the investigator review approach (13% compared to 2.4% PPV). …”

Section: Discussionmentioning

confidence: 98%

A collaborative approach to developing an electronic health record phenotyping algorithm for drug-induced liver injury

Overby

Pathak

Gottesman

et al. 2013

J Am Med Inform Assoc

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Section: Discussionmentioning

confidence: 98%

A collaborative approach to developing an electronic health record phenotyping algorithm for drug-induced liver injury

Overby

Pathak

Gottesman

et al. 2013

J Am Med Inform Assoc

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“…Study 7 did apply the system from the beginning of the study; therefore, there is no comparison to previous values. [16] reported recently about improved efficiency in clinical trial recruitment using electronic screening. We conducted a PubMed query (MeSH terms 'patient selection' and 'hospital information systems', manual review of search results) and found relatively few reports of clinical trial alert systems, mainly in the outpatient setting.…”

Section: Resultsmentioning

confidence: 99%

Routine data from hospital information systems can support patient recruitment for clinical studies

et al. 2010

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Background Delayed patient recruitment is a common problem in clinical studies. Hospital information systems (HIS) contain data items relevant for inclusion or exclusion criteria of these studies. Purpose We developed and assessed a system to support patient recruitment using HIS data. Methods We developed a workflow integrated in our HIS to notify study physicians about potential trial subjects. Automatic HIS database queries based on inclusion and exclusion criteria for each clinical study are performed regularly and generate e-mail notifications via a communication server. Study physicians can verify eligibility with a specific HIS study module. The system performance was assessed with a survey addressing utility, usability, stability, change in recruitment rate, and estimated time savings. Results During 10 months of operation, 1328 notifications were generated and 329 enrollments (25%) were documented for seven studies. Precision of alerts depends on availability of appropriate HIS items. Utility and usability were assessed as good, and stability as excellent. Users reported an increased patient recruitment rate for three studies. Three studies reported an estimated time saving of 10 min per recruited patient. The main perceived benefit was systematic identification of potentially eligible patients without time-consuming patient screening procedures in the different parts of the hospital. Limitations Notifications about potentially eligible patients depend on HIS data quality regarding inclusion/exclusion criteria, in particular, completeness, timeliness, and validity. Conclusions Routine HIS data can support patient recruitment for clinical studies by means of an automated notification workflow and efficient access to clinical data.

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“…Almost all CTRSS “perform ‘pre-screening’ for clinical research staff” [115] instead of trying to determine the actual eligibility of a patient. They do not replace manual chart review, but act as a filter that limits the number of patients who require such by selecting the most likely candidates.…”

Section: Resultsmentioning

confidence: 99%

Employing Computers for the Recruitment into Clinical Trials: A Comprehensive Systematic Review

Köpcke

Prokosch²

2014

J Med Internet Res

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BackgroundMedical progress depends on the evaluation of new diagnostic and therapeutic interventions within clinical trials. Clinical trial recruitment support systems (CTRSS) aim to improve the recruitment process in terms of effectiveness and efficiency.ObjectiveThe goals were to (1) create an overview of all CTRSS reported until the end of 2013, (2) find and describe similarities in design, (3) theorize on the reasons for different approaches, and (4) examine whether projects were able to illustrate the impact of CTRSS.MethodsWe searched PubMed titles, abstracts, and keywords for terms related to CTRSS research. Query results were classified according to clinical context, workflow integration, knowledge and data sources, reasoning algorithm, and outcome.ResultsA total of 101 papers on 79 different systems were found. Most lacked details in one or more categories. There were 3 different CTRSS that dominated: (1) systems for the retrospective identification of trial participants based on existing clinical data, typically through Structured Query Language (SQL) queries on relational databases, (2) systems that monitored the appearance of a key event of an existing health information technology component in which the occurrence of the event caused a comprehensive eligibility test for a patient or was directly communicated to the researcher, and (3) independent systems that required a user to enter patient data into an interface to trigger an eligibility assessment. Although the treating physician was required to act for the patient in older systems, it is now becoming increasingly popular to offer this possibility directly to the patient.ConclusionsMany CTRSS are designed to fit the existing infrastructure of a clinical care provider or the particularities of a trial. We conclude that the success of a CTRSS depends more on its successful workflow integration than on sophisticated reasoning and data processing algorithms. Furthermore, some of the most recent literature suggest that an increase in recruited patients and improvements in recruitment efficiency can be expected, although the former will depend on the error rate of the recruitment process being replaced. Finally, to increase the quality of future CTRSS reports, we propose a checklist of items that should be included.

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Electronic Screening Improves Efficiency in Clinical Trial Recruitment

Cited by 116 publications

References 3 publications

A collaborative approach to developing an electronic health record phenotyping algorithm for drug-induced liver injury

A collaborative approach to developing an electronic health record phenotyping algorithm for drug-induced liver injury

Routine data from hospital information systems can support patient recruitment for clinical studies

Employing Computers for the Recruitment into Clinical Trials: A Comprehensive Systematic Review

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