Evaluating clinical trial management systems: a simulation approach
Bibliographic record
Abstract
Purpose If the use of information technology (IT) supporting clinical trial projects offers opportunities to optimize the underlying information management process, the intricacy of the identification and evaluation of relevant IT options is generally seen as a complex task in healthcare. Hence, the purpose of this paper is to examine the problem of ex ante information system evaluation, and assess the impact of IT on the information management process underlying clinical trials. Design/methodology/approach Combining Unified Modeling Language (UML) and system dynamics modeling, a simulation model for evaluating IT was developed. This modeling effort relies on a case study conducted in a clinical research organization, which, at that time, faced an IT investment dilemma. Findings Some illustrative results of sensitivity analyzes conducted on error rates in clinical data transmission are presented. These simulation results allow for quantifying the impact of different IT options on human resources' efforts, time delays and costs of clinical trials projects. Notably, the results show that although the technology has no real influence on the duration of a clinical trial project, it impacts the number of projects that can be carried out simultaneously. Originality/value The research provides insights into the development of an innovative approach appropriate to the evaluation of IT supporting clinical trials, through the use of a mixed‐method based on qualitative and quantitative modeling. The results illustrate two critical issues addressed in the IS literature: the necessity to extend IT evaluation beyond the quantitative‐qualitative dichotomy; and the role of evaluation in organizational learning, and in learning about business dimensions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".