Characterizing Potential Medication Related Harm in the 30-Days Following Hospitalization Using Linked Provincial Administrative Health Data
Bibliographic record
Abstract
IntroductionPatients transitioning between hospital and home are at risk of experiencing adverse drug events. Hospital-based care teams require a better understanding of how therapeutic decisions and patient risk factors impact adverse health outcomes following discharge.
 Objectives and ApproachThe objective was to describe medications prescribed at discharge and quantify the association between these medication classes and risk of emergency department (ED) visits within 30 days. A retrospective, population-based cohort study was conducted using linked health administrative databases of adults discharged from hospital to the community between April 2016-March 2017, in Ontario, Canada. To distinguish medications prescribed at discharge from those by community providers, we examined medications dispensed in the 30-days following hospitalization but prior to another healthcare encounter (physician visit, ED visit, or rehospitalization). Adjusted logistic regression was used to determine the association between medication classes at discharge and ED visits in 30 days post discharge.
 ResultsOur study cohort included 214, 011 patients. Median age was 76 (IQR:66-82), 52% were females, 92% were discharged home and 8% were discharged to long term care. Patients were dispensed a median of 3 medications (IQR: 2-6). The most common medications included hydromorphone (16% of all patients, 71% of which represented new use), pantoprazole (15% of patients, 40% new use), furosemide (13% of patients, 34% new use), amlodipine (10% of patients, 35% new use) and oxycodone (9% of patients, 70% new use). To account for confounding by indication, we adjusted for age, sex, discharge service, discharge disposition, in hospital diagnoses and length of stay. Medications which were most highly associated with an increased risk of ED visits included antipsychotics, antiepileptics, anti-infectives, systemic corticosteroids, antihypertensives and cardiac stimulants.
 Conclusion / ImplicationsCharacterizing potential medication related harm in the 30-days following hospitalization is an important first step in understanding which patients may benefit most from hospital-based medication related interventions.
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.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".