Clinical deployment of a medical expert system to increase accruals for clinical trials: Challenges
ABSTRACT Before new medical treatments become available to the public, clinicians must conduct extensive trials to determine the efficacy of the novel therapy. In order for the clinical trial to be successful, a significant number of patients with an appropriate set of medical conditions must be accrued. We have implemented a web-based expert system at the H. Lee Moffitt Cancer Center & Research Institute in the Gastrointestinal Tumor Clinic (GITC) to help physicians screen patients for phase II trials. Our system allows physicians to screen a patient for multiple trials simultaneously. Our 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.
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ABSTRACT: A clinical trial is a study conducted on a group of patients to evaluate a new treatment procedure. Usually, clinicians manually select patients for a clinical trial; the choice of eligible patients is a labor-intensive process, and clinicians are often unable to identify sufficient number of patients, which delays the evaluation of new treatments. We have developed a Web-based system that helps clinicians to determine the eligibility of patients for multiple clinical trials. It uses probabilistic techniques that minimize the amount of manual data entry, by ordering the related data-entry steps. We describe the developed system and give the results of applying it to retrospective data of breast cancer patients at the Moffitt Cancer Center.Computer-Based Medical Systems, 2004. CBMS 2004. Proceedings. 17th IEEE Symposium on; 07/2004
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ABSTRACT: Reasons for patient non-entry in randomized clinical trials (RCTs) assessing oncologic treatment modalities are not well documented in the literature. We have prospectively recorded reasons for RCT non-entry in breast cancer patients at St. Michael's Hospital, Toronto. From September 1984 to November 1989, 592 consecutive patients were evaluated through the clinical trials office. One hundred six out of the 592 patients were placed into a RCT (17.9%). Protocol ineligibilities accounted for 273 non-entries (46.1%) and protocol eligible but not entered patients accounted for 213 (36.0%) non-entries. The most common reason for protocol ineligibility was advanced age (94 patients). The most common reason for protocol eligible but not entered patients was patient refusal (148 patients). A total of 272 patients in both non-entered groups were identified as having reasons for non-entry that were potentially correctable. In summary, protocol ineligibilities account for the majority of non-entered patients, but patient refusal accounted for the single largest group of potentially correctable non-entries. More dissemination about the merit of RCTs in the lay press and amongst primary care physicians must take place if we are to expediently and efficiently answer important oncologic questions.Journal of Surgical Oncology 07/1992; 50(2):125-9. · 2.64 Impact Factor
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ABSTRACT: The task of determining patients' eligibility for clinical trials is knowledge and data intensive. In this paper, we present a model for the task of eligibility determination, and describe how a computer system can assist clinical researchers in performing that task. Qualitative and probabilistic approaches to computing and summarizing the eligibility status of potentially eligible patients are described. The two approaches are compared, and a synthesis that draws on the strengths of each approach is proposed. The result of applying these techniques to a database of HIV-positive patient cases suggests that computer programs such as the one described can increase the accrual rate of eligible patients into clinical trials. These methods may also be applied to the task of determining from electronic patient records whether practice guidelines apply in particular clinical situations.Methods of Information in Medicine 09/1993; 32(4):317-25. · 1.60 Impact Factor