Literature DB >> 16779291

Using an information warehouse to screen patients for clinical trials: a prototype.

Jyoti Kamal1, Kabardhi Pasuparthi, Patrick Rogers, Jason Buskirk, Hagop Mekhjian.   

Abstract

Success of a clinical trial recruitment process for drug discovery and new treatments depends on screening and identifying eligible patients in a timely manner. This can be a complex and tedious process that in many instances requires a research nurse to manually track patients that may be eligible. At the Ohio State University Medical Center (OSUMC) we have developed a web-based functional prototype that uses the data stored in the Information Warehouse (IW) to screen patients that meet the eligibility criteria for clinical trials. Using this prototype, a researcher can apply a set of inclusion/exclusion criteria pertinent to a research protocol and instantly find patients that may qualify for the clinical trial without revealing their identity. A researcher has access to all detailed historical clinical information such as lab results, diagnosis codes, pathology reports and clinical notes.

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Year:  2005        PMID: 16779291      PMCID: PMC1560829     

Source DB:  PubMed          Journal:  AMIA Annu Symp Proc        ISSN: 1559-4076


  1 in total

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Authors:  P M Nadkarni; C Brandt
Journal:  J Am Med Inform Assoc       Date:  1998 Nov-Dec       Impact factor: 4.497

  1 in total
  7 in total

1.  The design and implementation of an open-source, data-driven cohort recruitment system: the Duke Integrated Subject Cohort and Enrollment Research Network (DISCERN).

Authors:  Jeffrey M Ferranti; William Gilbert; Jonathan McCall; Howard Shang; Tanya Barros; Monica M Horvath
Journal:  J Am Med Inform Assoc       Date:  2011-09-23       Impact factor: 4.497

2.  Automated matching software for clinical trials eligibility: measuring efficiency and flexibility.

Authors:  Lynne Penberthy; Richard Brown; Federico Puma; Bassam Dahman
Journal:  Contemp Clin Trials       Date:  2010-03-15       Impact factor: 2.226

3.  Trend and Network Analysis of Common Eligibility Features for Cancer Trials in ClinicalTrials.gov.

Authors:  Chunhua Weng; Anil Yaman; Kuo Lin; Zhe He
Journal:  Smart Health (2014)       Date:  2014-07

4.  Design and multicentric implementation of a generic software architecture for patient recruitment systems re-using existing HIS tools and routine patient data.

Authors:  B Trinczek; F Köpcke; T Leusch; R W Majeed; B Schreiweis; J Wenk; B Bergh; C Ohmann; R Röhrig; H U Prokosch; M Dugas
Journal:  Appl Clin Inform       Date:  2014-03-19       Impact factor: 2.342

5.  Trial prospector: matching patients with cancer research studies using an automated and scalable approach.

Authors:  Satya S Sahoo; Shiqiang Tao; Andrew Parchman; Zhihui Luo; Licong Cui; Patrick Mergler; Robert Lanese; Jill S Barnholtz-Sloan; Neal J Meropol; Guo-Qiang Zhang
Journal:  Cancer Inform       Date:  2014-12-04

Review 6.  Employing computers for the recruitment into clinical trials: a comprehensive systematic review.

Authors:  Felix Köpcke; Hans-Ulrich Prokosch
Journal:  J Med Internet Res       Date:  2014-07-01       Impact factor: 5.428

7.  HEDEA: A Python Tool for Extracting and Analysing Semi-structured Information from Medical Records.

Authors:  Anshul Aggarwal; Sunita Garhwal; Ajay Kumar
Journal:  Healthc Inform Res       Date:  2018-04-30
  7 in total

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