Experiments on the automated selection of patients for clinical trials

Fink, Eugene; Hall, Lawrence O.; Goldgof, Dmitry B.; Goswami, B.D.; Boonstra, Matthew; Krischer, Jeffrey P.

doi:10.1109/icsmc.2003.1245699

Cited by 8 publications

(10 citation statements)

References 9 publications

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“…Fink et al classified criteria into three types of questions: the first type takes yes/no response, the second takes multiple choices, and the third requires a numeric answer [14–16]. In a more sophisticated manner, in a review study of six representative computer-based clinical guidelines (EON, Asbru, PROforma, GUIDE, GLIF, PRODIGY) [49], Peleg et al categorized eligibility criteria into presence criteria, template-based criteria, firstorder logic criteria, temporal criteria, “if-then-else” statements, and context-dependent expressions.…”

Section: Resultsmentioning

confidence: 99%

“…Templates for ad hoc expressions of eligibility criteria are very popular and have been used broadly in OaSIS, OncoLink, caMatch, ASPIRE, CRFQ, EON, SAGE, and ERGO and the clinical trial screen system developed by Fink et al at the University of South Florida [14–16]. Ad hoc expressions for eligibility criteria were further translated to SQL database queries in T-Helper.…”

Section: Resultsmentioning

confidence: 99%

“…Many eligibility criteria contain temporal constraints and require abstraction or reasoning of temporal patient conditions. Some representations for eligibility criteria do not explicitly support temporal queries [14, 15, 20], while many others only support query over time-stamped data [12, 30, 40, 65]. …”

Section: Resultsmentioning

confidence: 99%

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Formal representation of eligibility criteria: A literature review

Weng

Sim

et al. 2010

Journal of Biomedical Informatics

148

116

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Standards-based, computable knowledge representations for eligibility criteria are increasingly needed to provide computer-based decision support for automated research participant screening, clinical evidence application, and clinical research knowledge management. We surveyed the literature and identified five aspects of eligibility criteria knowledge representations that contribute to the various research and clinical applications: the intended use of computable eligibility criteria, the classification of eligibility criteria, the expression language for representing eligibility rules, the encoding of eligibility concepts, and the modeling of patient data. We consider three of them (expression language, codification of eligibility concepts, and patient data modeling), to be essential constructs of a formal knowledge representation for eligibility criteria. The requirements for each of the three knowledge constructs vary for different use cases, which therefore should inform the development and choice of the constructs toward cost-effective knowledge representation efforts. We discuss the implications of our findings for standardization efforts toward sharable knowledge representation of eligibility criteria.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Formal representation of eligibility criteria: A literature review

Weng

Sim

et al. 2010

Journal of Biomedical Informatics

148

116

View full text Add to dashboard Cite

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“…Lane et al [ 71 ], Tu et al [ 119 ], and a research group from the University of South Florida [ 55 , 56 , 68 , 88 ] ran their respective CTRSS on legacy patient data and evaluated how many of those patients found potentially eligible by their system were actually enrolled in the past. These works only showed an upper limit of CTRSS effectiveness because it was unclear whether “physicians actually missed the matches, rather than having undocumented reasons for omitting them” [ 56 ].…”

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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“…On the other hand, the decision-tree system could check a patient's eligibility for only one trial, and it did not scale to the use of multiple trials. To address this problem, we developed an analytical rule-based system, which efficiently processed multiple clinical trials [1][2][3]; however, it was unable to estimate the probably of a patient's eligibility for available trials in the absence of complete information. We have then combined the rule-based system with probabilistic techniques, described in this paper.…”

Section: Introductionmentioning

confidence: 99%