The effectiveness of integrated health information technologies across the phases of medication management: a systematic review of randomized controlled trials
Bibliographic record
Abstract
OBJECTIVE: The US Agency for Healthcare Research and Quality funded an evidence report to address seven questions on multiple aspects of the effectiveness of medication management information technology (MMIT) and its components (prescribing, order communication, dispensing, administering, and monitoring). MATERIALS AND METHODS: Medline and 11 other databases without language or date limitations to mid-2010. Randomized controlled trials (RCTs) assessing integrated MMIT were selected by two independent reviewers. Reviewers assessed study quality and extracted data. Senior staff checked accuracy. RESULTS: Most of the 87 RCTs focused on clinical decision support and computerized provider order entry systems, were performed in hospitals and clinics, included primarily physicians and sometimes nurses but not other health professionals, and studied process changes related to prescribing and monitoring medication. Processes of care improved for prescribing and monitoring mostly in hospital settings, but the few studies measuring clinical outcomes showed small or no improvements. Studies were performed most frequently in the USA (n=63), Europe (n=16), and Canada (n=6). DISCUSSION: Many studies had limited description of systems, installations, institutions, and targets of the intervention. Problems with methods and analyses were also found. Few studies addressed order communication, dispensing, or administering, non-physician prescribers or pharmacists and their MMIT tools, or patients and caregivers. Other study methods are also needed to completely understand the effects of MMIT. CONCLUSIONS: Almost half of MMIT interventions improved the process of care, but few studies measured clinical outcomes. This large body of literature, although instructive, is not uniformly distributed across settings, people, medication phases, or 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 | 0.049 | 0.145 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.020 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".