Complications in using automated methods to increase clinical trial accrual

This paper describes the issues involved in improving accruals for clinical trials using a web-based system. We installed a custom web-based expert system at the H. Lee Moffitt Cancer Center and Research Institute to help physicians screen patients for phase II clinical trials. The system allows physicians to screen a patient for multiple trials simultaneously. Experiments have shown that adaptation of the system into a clinical environment and the success of the system are related to the amount of time physicians are willing to spend entering data. We also found significant regulatory issues (HIPAA) that make implementation challenging.

[1]  Lawrence O. Hall,et al.  Clinical deployment of a medical expert system to increase accruals for clinical trials: Challenges , 2007, 2007 IEEE International Conference on Systems, Man and Cybernetics.

[2]  M. Wolf,et al.  Local perspective of the impact of the HIPAA privacy rule on research , 2006, Cancer.

[3]  Frans Coenen,et al.  Research and Development in Intelligent Systems XXII, Proceedings of AI-2005, the Twenty-fifth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, UK, 12-14 December 2005 , 2006, SGAI Conf..

[4]  Sophia Ananiadou,et al.  Text Mining for Biology And Biomedicine , 2005 .

[5]  R. Hornung,et al.  Effect of a clinical trial alert system on physician participation in trial recruitment. , 2005, Archives of internal medicine.

[6]  Peter J. Embi,et al.  Development of an Electronic Health Record-based Clinical Trial Alert System to Enhance Recruitment at the Point of Care , 2005, AMIA.

[7]  Eugene Fink,et al.  Selection of patients for clinical trials: an interactive web-based system , 2004, Artif. Intell. Medicine.

[8]  Using probabilistic methods to optimize data entry in accrual of patients to clinical trials , 2004, Proceedings. 17th IEEE Symposium on Computer-Based Medical Systems.

[9]  Robert J. Arens.  A Preliminary Look into the Use of Named Entity Information for Bioscience Text Tokenization , 2004, NAACL.

[10]  Padmini Srinivasan,et al.  Text mining: Generating hypotheses from MEDLINE , 2004, J. Assoc. Inf. Sci. Technol..

[11]  Eugene Fink,et al.  Experiments on the automated selection of patients for clinical trials , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[12]  Atul J Butte,et al.  Computerized recruiting for clinical trials in real time. , 2003, Annals of emergency medicine.

[13]  Lawrence B. Afrin,et al.  Leveraging of Open EMR Architecture for Clinical Trial Accrual , 2003, AMIA.

[14]  L.O. Hall,et al.  A cost-effective agent for clinical trial assignment , 2002, IEEE International Conference on Systems, Man and Cybernetics.

[15]  T. Ricketts,et al.  Clinical trial enrollment of rural patients with cancer. , 2002, Cancer practice.

[16]  Christian Borgelt,et al.  Induction of Association Rules: Apriori Implementation , 2002, COMPSTAT.

[17]  Savvas Nikiforou,et al.  Selection of clinical trials: Knowledge representation and acquisition , 2002 .

[18]  Jacques Bouaud,et al.  Using ONCODOC as a Computer-Based Eligibility Screening System to Improve Accrual onto Breast Cancer Clinical Trials , 2001, AIME.

[19]  Omolola Ogunyemi,et al.  Finding appropriate clinical trials: evaluating encoded eligibility criteria with incomplete data , 2001, AMIA.

[20]  Atul J. Butte,et al.  Enrolling patients into clinical trials faster using RealTime Recuiting , 2000, AMIA.

[21]  Madhu C. Reddy,et al.  Participatory design and an eligibility screening tool , 2000, AMIA.

[22]  Marti A. Hearst.  Untangling Text Data Mining , 1999, ACL.

[23]  Aziz A. Boxwala,et al.  Decision support for clinical trial eligibility determination in breast cancer , 1999, AMIA.

[24]  Sanjukta Bhanja,et al.  A Qualitative Expert System for Clinical Trial Assignment , 1998, FLAIRS.

[25]  S. Tu,et al.  A Methodology for Determining Patients’ Eligibility for Clinical Trials , 1993, Methods of Information in Medicine.

[26]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[27]  L J Mahoney,et al.  Reasons for non‐entry in randomized clinical trials for breast cancer: A single institutional study , 1992, Journal of surgical oncology.

[28]  M. Clement,et al.  PREVENTION AND TREATMENT , 1944 .