Medication Use by Alternate Level of Care Patients: A Descriptive Analysis
Bibliographic record
Abstract
ABSTRACTBackground: The population of patients designated as alternate level of care (ALC) consists predominantly of frail older adults who are medically stable and awaiting discharge from hospital. They have complex medica-tion regimens, often including potentially inappropriate medications (PIMs). There has been increasing emphasis on managing the burden that ALC patients place on the health care system, but little is known about their health care needs. Objective: To characterize the medication regimens, including use of PIMs, of ALC patients at the study institution. Methods: A cross-sectional chart audit of ALC patients was conducted between May and July 2017. For all patients in the sample, each medication was categorized by therapeutic class, and PIMs were categorized according to the Beers criteria, the STOPP/START criteria, and an established list of high-alert medications. Results: A total of 82 patients met the audit criteria, for whom the mean number of chronic conditions was 6.4 (standard deviation [SD] 3.3) and the mean number of prescribed medications was 12.8 (SD 6.9). Twenty-four (29%) of the patients were receiving at least 1 drug from 7 different drug classes. All but one of the patients had PIMs in their regimen; the frequency of PIMs was highest according to the Beers criteria (mean 3.9 [SD 2.6] medications per patient). Conclusions: At the study institution, ALC patients had on average more than 6 chronic conditions managed with at least 12 medications, of which one-quarter were PIMs. These data will be used to inform next steps in making recommendations to simplify, reduce, or discontinue medications for which there is an unclear indication, lack of effectiveness, or evidence of potential harm.RÉSUMÉContexte : La population de patients désignés comme « niveaux de soins alternatifs » (NSA) se compose majoritairement d’aînés faibles, médicalement stables et en attente de leur congé hospitalier. Ils suivent des traitements médicamenteux complexes qui comprennent souvent des médicaments potentiellement contre-indiqués (MPCI). L’accent a été progressivement mis sur la gestion du fardeau que les patients NSA font peser sur le système de soins de santé, mais on connait peu de choses sur leurs besoins en matière de soins de santé. Objectif : Décrire les traitements médicamenteux, y compris l’utilisation des MPCI, des patients NSA dans l’institution où s’est déroulée l’étude. Méthodes : Une vérification transversale des dossiers de patients NSA a été menée entre mai et juillet 2017. Chaque médicament pris par les patients de l’échantillon a été classé selon sa catégorie thérapeutique, et les MPCI ont été catégorisés selon les critères de Beers, les critères STOPP/START ainsi qu’une liste établie de médicaments dont le niveau d’alerte est élevé.Résultats : Au total, 82 patients remplissaient les critères de l’audit, car le nombre moyen de maladies chroniques était de 6,4 (écart type [ET] 3,3) et le nombre moyen de médicaments prescrits se montait à 12,8 (ET 6,9). Vingt-quatre (29 %) patients recevaient au moins un médicament de sept classes médicamenteuses différentes. Tous les patients sauf un avaient des MPCI dans leur programme. La fréquence des MPCI était plus élevée selon les critères de Beers (moyenne de MPCI par patient de 3,9 [ET 2,6]). Conclusions : Sur le lieu de l’étude, les patients NSA avaient en moyenne plus de six maladies chroniques gérées à l’aide d’au moins 12 médicaments, dont un quart était des MPCI. Ces données seront utilisées pour informer les cliniciens sur les étapes suivantes et formuler des recommandations afin de simplifier, de réduire ou d’arrêter les médicaments pour lesquels l’indication n’est pas claire, dont l’efficacité est insuffisante ou sur lesquels il existe des données probantes faisant état de dangers potentiels.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".