Integrating personalized medical test contents with XML and XSL-FO

Background: In 2004 the adoption of a modular curriculum at the medical faculty in Muenster led to the introduction of centralized examinations based on multiple-choice questions (MCQs). We report on how organizational challenges of realizing faculty-wide personalized tests were addressed by implementation of a specialized software module to automatically generate test sheets from individual test registrations and MCQ contents. Methods: Key steps of the presented method for preparing personalized test sheets are (1) the compilation of relevant item contents and graphical media from a relational database with database queries, (2) the creation of Extensible Markup Language (XML) intermediates, and (3) the transformation into paginated documents. Results: The software module by use of an open source print formatter consistently produced high-quality test sheets, while the blending of vectorized textual contents and pixel graphics resulted in efficient output file sizes. Concomitantly the module permitted an individual randomization of item sequences to prevent illicit collusion. Conclusions: The automatic generation of personalized MCQ test sheets is feasible using freely available open source software libraries, and can be efficiently deployed on a faculty-wide scale

Evaluating predictive modeling algorithms to assess patient eligibility for clinical trials from routine data

Background The necessity to translate eligibility criteria from free text into decision rules that are compatible with data from the electronic health record (EHR) constitutes the main challenge when developing and deploying clinical trial recruitment support systems. Recruitment decisions based on case-based reasoning, i.e. using past cases rather than explicit rules, could dispense with the need for translating eligibility criteria and could also be implemented largely independently from the terminology of the EHR’s database. We evaluated the feasibility of predictive modeling to assess the eligibility of patients for clinical trials and report on a prototype’s performance for different system configurations. Methods The prototype worked by using existing basic patient data of manually assessed eligible and ineligible patients to induce prediction models. Performance was measured retrospectively for three clinical trials by plotting receiver operating characteristic curves and comparing the area under the curve (ROC-AUC) for different prediction algorithms, different sizes of the learning set and different numbers and aggregation levels of the patient attributes. Results Random forests were generally among the best performing models with a maximum ROC-AUC of 0.81 (CI: 0.72-0.88) for trial A, 0.96 (CI: 0.95-0.97) for trial B and 0.99 (CI: 0.98-0.99) for trial C. The full potential of this algorithm was reached after learning from approximately 200 manually screened patients (eligible and ineligible). Neither block- nor category-level aggregation of diagnosis and procedure codes influenced the algorithms’ performance substantially. Conclusions Our results indicate that predictive modeling is a feasible approach to support patient recruitment into clinical trials. Its major advantages over the commonly applied rule-based systems are its independency from the concrete representation of eligibility criteria and EHR data and its potential for automation