Use of Information Technology in Medication Reconciliation: A Scoping Review
Bibliographic record
Abstract
OBJECTIVE: To identify studies involving information technology (IT) in medication reconciliation (MedRec) and determine how IT is used to facilitate the MedRec process. DATA SOURCES: The search strategy included a database search of MEDLINE and Cumulative Index of Nursing and Allied Health Literature (CINAHL), hand-searching of collected material, and references from articles retrieved. The database search was limited to English-language papers. MEDLINE includes publications dating back to 1950 and CINAHL includes those dating back to 1982. The search included articles in both databases up to March 2009. Boolean queries were constructed using combinations of search terms for medication reconciliation, IT, and electronic records. STUDY SELECTION AND DATA EXTRACTION: Three inclusion criteria were used. The study had to (1) involve the MedRec process, (2) be a primary study, and (3) involve the use of IT. Selection was performed by 2 reviewers through consensus. Data related to study characteristics, focus, and IT use were extracted. DATA SYNTHESIS: The included studies described a range of IT used throughout the MedRec process, from basic email and databases to specialized MedRec tools. A generic MedRec workflow was created and types of IT found in the studies were mapped to the workflow activities as well as to a set of functionalities based on the Institute of Medicine's Key Capabilities of an Electronic Health Record System. In the studies reviewed, IT was mainly used to obtain medication information. Although there were only a few MedRec tools in the studies, those that did exist supported the central activities for MedRec: comparison of medications and clarification of discrepancies. CONCLUSIONS: MedRec is an important process to ensure patient medication safety. Evidence was found that IT can and has been used to facilitate some MedRec activities and new applications are being developed to support the entire MedRec process.
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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 | 0.046 | 0.145 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.040 | 0.039 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".