Preventable sources of medication related morbidity and mortality at transitions in care for hospitalized patients
Bibliographic record
Abstract
With the aging of the population and advances in medical care and public health, a growing proportion of individuals have multiple chronic conditions. Indeed, in Canada, almost half of those aged 65 years and older are living with multiple co-existing chronic conditions. The impact of multimorbidity is multifaceted, leading to lower quality of life, declines in functional status, higher rates of disability and increased mortality. For healthcare systems, multimorbidity leads to greater healthcare utilization, especially of high-cost services such as hospital stays and emergency department visits. One of the most complex aspects of providing optimal care to patients with multimorbidity is their use of many different medications. Approximately 60% of multimorbid patients use five or more medications which in turn increases their risk of drug-drug and drug-disease interactions, and adverse drug events (ADEs). Despite the clinical challenges associated with treating complex patients with many medications, evidence-based guidance for safe and effective prescribing in the context of multimorbidity remains limited. Patients with multiple chronic conditions are likely to require repeated admission to hospital, and once discharged, over one-third will be re-admitted to hospital within 90-days. However, reducing re-admissions in these complex patients has remained challenging since the reason for returning to hospital can include a number of interlinked patient-, provider- and healthcare system-level factors. The impact of patient medications is of significant interest since a large proportion of re-admissions are related to ADEs. When patients are hospitalized, they are often discharged on substantially different medications than those prior to admission. One might expect that discontinuations, additions or modifications to patient drug regimens during hospitalization would reduce the likelihood of adverse health outcomes after discharge. However, the extent to which patients actually adhere to hospital medication changes or the appropriateness of these changes is not known. The impact of these factors on short-term patient health outcomes after discharge is also unknown.The overarching goal of this thesis was to explore potentially preventable sources of medication-related morbidity and mortality in hospitalized patients in the transition between hospital and home. Three studies were completed to accomplish this goal.Objective Study 1: Estimate the incidence and determinants of non-adherence to hospital medication changes in the 30-days after hospitalizationObjective Study 2: Estimate the association between non-adherence to medication changes made at hospital discharge on the risk of re-admissions, emergency department visits and death in the 30-days post dischargeObjective Study 3: Estimate the incidence of potentially inappropriate medications prescribed at hospital discharge and their impact on re-admissions, emergency department visits and death as well as drug-related adverse events in the 30-days after discharge
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".