Abstract P200: Computerized Clinical Decision Support Systems for Therapeutic Drug Monitoring and Dosing: A Decision Maker--Researcher Partnership Systematic Review
Bibliographic record
Abstract
Background Optimization of the return on investments in information technology innovations requires that current best evidence be considered for an effect on care processes and health outcomes. Computerized clinical decisions support systems (CCDSSs) might improve therapeutic drug monitoring and dosing (TDMD) by providing patient-tailored clinical recommendations. We summarized current evidence from randomized controlled trials (RCTs) for the effect of CCDSSs on TDMD. Methods A decision-maker - researcher partnership systematic review was performed to optimize the practical implementation of results. Studies from a previous review on the effect of CCDSSs (Garg AX, 2005) were included if they addressed TDMD and were RCTs. Additional RCTs were sought until January 2010 in MEDLINE, EMBASE, Evidence-Based Medicine Reviews and Inspec databases. RCTs assessing the effect of a CCDSS on process of care or patient outcomes were selected by pairs of independent reviewers. Results In total, 33 RCTs were identified that assessed the effect of a CCDSS on management of vitamin K antagonists (14), insulin (6), theophylline/aminophylline (4), aminoglycosides (3), digoxin (2), lidocaine (1), or as part of a multifaceted approach (3). All studies combined enrolled 24,627 patients; 13,219 were in the largest study, and only 6 other studies enrolled over 500 patients. Most studies were performed in one center (63%) and cluster randomization, of either clinics or physicians, was rarely used (18%). CCDSSs were usually stand-alone systems (76%) primarily used by physicians (85%). Overall, 18 of 30 studies (60%) showed an improvement in the process of care and 4 of 19 (21%) an improvement in patient outcomes. All evaluable studies assessing insulin dosing for glycemic control showed an improvement. In meta-analysis, CCDSSs for vitamin K antagonist dosing improved the time that patients spent in the therapeutic range by 6.1% (95% confidence interval: 0.46-11.83; p=0.03). Conclusions CCDSSs have potential for improving process of care for TDMD, specifically insulin and vitamin K antagonist dosing, but effects on patient outcomes were uncertain. More potent CCDSSs are needed and should be evaluated using cluster randomization, primarily assessing patient outcomes.
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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 | 0.050 | 0.168 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".