Optimizing Clinical Research Participant Selection with Informatics.

Clinical research participants are often not reflective of real-world patients due to overly restrictive eligibility criteria. Meanwhile, unselected participants introduce confounding factors and reduce research efficiency. Biomedical informatics, especially Big Data increasingly made available from electronic health records, offers promising aids to optimize research participant selection through data-driven transparency.

[1]  Chunhua Weng,et al.  Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research , 2013, J. Am. Medical Informatics Assoc..

[2]  S. Tu,et al.  Analysis of Eligibility Criteria Complexity in Clinical Trials , 2010, Summit on translational bioinformatics.

[3]  Chunhua Weng,et al.  Sick Patients Have More Data: The Non-Random Completeness of Electronic Health Records , 2013, AMIA.

[4]  N. Sharma Patient centric approach for clinical trials: Current trend and new opportunities , 2015, Perspectives in clinical research.

[5]  Harlan M Krumholz,et al.  Most hospitalized older persons do not meet the enrollment criteria for clinical trials in heart failure. , 2003, American heart journal.

[6]  G Hripcsak,et al.  A Distribution-based Method for Assessing The Differences between Clinical Trial Target Populations and Patient Populations in Electronic Health Records , 2014, Applied Clinical Informatics.

[7]  Tianyong Hao,et al.  Clustering clinical trials with similar eligibility criteria features , 2014, J. Biomed. Informatics.

[8]  W. A. Gool,et al.  The age gap between patients in clinical studies and in the general population: a pitfall for dementia research , 2004, The Lancet Neurology.

[9]  Lawrence M. Fagan,et al.  Knowledge engineering for a clinical trial advice system: uncovering errors in protocol specification. , 1987, Bulletin du cancer.

[10]  Edward S. Kim,et al.  Modernizing Eligibility Criteria for Molecularly Driven Trials. , 2015, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[11]  Chunhua Weng,et al.  Formal representation of eligibility criteria: A literature review , 2010, J. Biomed. Informatics.

[12]  Daniel L. Rubin,et al.  Knowledge representation and tool support for critiquing clinical trial protocols , 2000, AMIA.

[13]  A. Kiss,et al.  Eligibility criteria of randomized controlled trials published in high-impact general medical journals: a systematic sampling review. , 2007, JAMA.

[14]  Chunhua Weng,et al.  Visual aggregate analysis of eligibility features of clinical trials , 2015, J. Biomed. Informatics.