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Record W2107024141 · doi:10.1345/aph.1l190

Medication Reconciliation at Hospital Discharge: Evaluating Discrepancies

2008· article· en· W2107024141 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnnals of Pharmacotherapy · 2008
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsPrincess Margaret Cancer CentreToronto Western HospitalUniversity of TorontoToronto General HospitalUniversity Health Network
Fundersnot available
KeywordsMedicineMedical prescriptionEmergency medicineHospital dischargeAdverse effectPatient dischargeMEDLINEPediatricsIntensive care medicineInternal medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Hospital discharge is an interface of care when patients are at a high risk of medication discrepancies as they transition from hospital to home. These discrepancies are important, as they may contribute to drug-related problems, medication errors, and adverse drug events. OBJECTIVE: To identify, characterize, and assess the clinical impact of unintentional medication discrepancies at hospital discharge. METHODS: All consecutive general internal medicine patients admitted for at least 72 hours to a tertiary care teaching hospital were prospectively assessed. Patients were excluded if they were discharged with verbal prescriptions; died during hospitalization; or transferred from or to a nursing home, another institution, or another unit within the same hospital. The primary endpoint was to determine the number of patients with at least one unintended medication discrepancy on hospital discharge. Medication discrepancies were assessed through comparison of a best possible medication discharge list with the actual discharge prescriptions. Secondary objectives were to characterize and assess the potential clinical impact of the unintentional discrepancies. RESULTS: From March 14, 2006, to June 2, 2006, 430 patients were screened for eligibility; 150 patients were included in the study. Overall, 106 (70.7%) patients had at least one actual or potential unintentional discrepancy. Sixty-two patients (41.3%) had at least one actual unintentional medication discrepancy at hospital discharge and 83 patients (55.3%) had at least one potential unintentional discrepancy. The most common unintentional discrepancies were an incomplete prescription requiring clarification, which could result in a patient delay in obtaining medications (49.5%), and the omission of medications (22.9%). Of the 105 unintentional discrepancies, 31(29.5%) had the potential to cause possible or probable patient discomfort and/or clinical deterioration. CONCLUSIONS: Medication discrepancies occur commonly on hospital discharge. Understanding the type and frequency of discrepancies can help clinicians better understand ways to prevent them. Structured medication reconciliation may help to prevent discharge medication discrepancies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.201
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.378
GPT teacher head0.516
Teacher spread0.138 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it