eHealth and mHealth Pipelines for Clinical Decision Support to Improve Medication Selection and Safety
Bibliographic record
Abstract
Although much work has been done over the past decade on developing personalized and evidence-based medicine, such as diagnostic tests based on genetics to better predict patients' responses to therapy, stumbling blocks remain that have prevented knowledge, tests, and other pertinent patient-centric data from becoming routine in clinical practice. The main problem with uptake and implementation centres on an absence of computational infrastructures to deliver an individualized approach at the point of care. This study focused on the creation of re-usable computer and mobile-based components of clinical decision support systems for patient-centric data. The goal was to develop eHealth and mHealth pipelines to not only create interactive delivery systems for medication selection and medication safety, but also to provide templates that can be re-used on provincial, pan-Canadian, and international scales. Through this research, a novel database structure for storing and analyzing data (CASTOR), augmented fast and frugal decision trees (FFTs), and mobile and web-based applications (AntiC), were developed to improve medication selection and medication safety. These projects were initially based on rationalizing, storing, and interpreting large genomic data sets, filtering the ensuing pharmacogenomically relevant data into automated decision trees for medication selection on a country-specific basis, and developing a work-frame template (BOW) to build clinical decision applications (apps) for smartphones and tablets. As a result of this project, ultimately, community and hospital pharmacists in Canada will be better informed about the safe handling and use of oral chemotherapeutics, preventing adverse drug reactions, and Ministries of Health in developing countries will have access to curated, pharmacogenomics-based decision trees specific to their countries to rationalize medication selection at no extra cost to local healthcare systems. The models we developed (CASTOR, augmented FFTs, and BOW) were deliberately designed and built with re-usable components that can be linked to future projects to generate targeted and focused clinical decision support and mHealth applications wherever needed. This will eventually allow for the implementation of rationalized clinical decision support applications and evidence-based knowledge translation at the point of care.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".