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Automated Selection of Patients for Clinical Trials

A system for automatic patient selection in clinical trials, improving eligibility identification, increasing participation, reducing costs. Enhances trial efficiency. The system employs a knowledge base with tests, questions, and criteria, utilizing logical expressions for eligibility assessment. Experiment results from breast cancer trials show a threefold increase in trial participants. Cost reduction achieved through optimal ordering of medical tests. The future plan includes system deployment, satisfaction evaluation, and probabilistic reasoning addition.

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Automated Selection of Patients for Clinical Trials

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  1. Automated Selection of Patients for Clinical Trials Eugene FinkLawrence O. HallDmitry B. GoldgofBhavesh D. GoswamiMatthew Boonstra Jeffrey P. Krischer

  2. Clinical trials A clinical trial is an evaluationof a new treatment procedure. When physicians conduct a trial, theyrecruit patients with matching healthproblems and medical histories.

  3. Selection of patients The selection of trial participants isa manual procedure, and physiciansmay miss eligible patients. • Gotay [1991] demonstrated that physicians choose only 39% of the eligible patients • Fallowfield et al. [1997] showed that they choose less than 50% of the eligible patients

  4. Expert system We have developed a system thatautomatically selects prospectivetrial participants.

  5. Outline • Knowledge base • Selection results • Cost reduction

  6. Knowledge base • Tests and questions • Eligibility criteria

  7. Tests and questions The knowledge base includes a list of medical tests. A test descriptionincludes a dollar cost and relatedquestions about a patient’s health. Example Mammogram, $150What is the cancer stage?Does the patient have invasive cancer? Biopsy, $400How many lymph nodes have tumor cells?What is the greatest tumor diameter?

  8. Example AND cancer-stage  {II, III} lymph-nodes  3 OR invasive-cancer = NO tumor-diameter  2.5 Eligibility criteria The knowledge base also includeslogical expressions that representeligibility for the available trials.

  9. Selection process The system collects data until it candetermine whether the eligibilityexpressions are TRUE or FALSE. If patient records do not provideenough data, the system identifiesthe required medical tests.

  10. Outline • Knowledge base • Selection results • Cost reduction

  11. Experiments We have used fifteen breast-cancertrials at the Moffitt Cancer Center. • Past data from 187 patients • Current data from 169 patients

  12. Finding eligible patients Past data for 187 patients

  13. Finding eligible patients Current data for 169 patients

  14. Participation in other trials Current data for 169 patients

  15. Summary The results suggest that the systemcan increase the number of trialparticipants by a factor of three.

  16. Outline • Knowledge base • Selection results • Cost reduction

  17. Medical tests The selection of trial participantsmay require medical tests. • The total cost of tests may depend on their ordering • Finding the right ordering is often a complex problem

  18. Ordering of tests The system chooses the ordering of teststhat reduces their expected total cost. After getting the results of the first test, itrevises the ordering of the remaining tests. The ordering is based on the test costs,number of trials that require each test,and structure of eligibility expressions.

  19. Cost reduction Past data for 187 patients

  20. Cost reduction Past data for 187 patients $250 W/O Reordering $200 With Reordering $150 $100 $50 $0 11072 11378 10822

  21. Cost reduction Current data for 169 patients

  22. Cost reduction Current data for 169 patients $250 W/O Reordering $200 With Reordering $150 $100 $50 $0 12601 12757 11971

  23. Conclusions We have developed a system thatselects prospective trial participants. • Helps physicians to identify eligible patients • Can increase the number of trial participants • Can reduce the cost of the selection process

  24. Future work • Deploy the system at the Moffitt Cancer Center • Evaluate the satisfaction of physicians and nurses • Add probabilistic reasoning

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