Reasons for Non-use of Proven Interventions for Hospital Inpatients: Pharmacists’ Perspectives
Bibliographic record
Abstract
Background: Recently, health care institutions have been using performance indicators to measure and improve quality of care. One such indicator, the Ideal Medication Intervention Index, reflects the rate of implementation of proven pharmacologic interventions, which studies have shown are underutilized. Identifying the reasons why proven interventions are underused is essential to determining how their rate of use can be improved.Objective: To characterize the reasons for non-use of proven interventions from the perspective of clinical pharmacists within the authors’ health care organization.Methods: A survey of all clinical pharmacists within the organization was conducted. The survey used standardized, case-based scenarios involving pharmacologic interventions known to improve health outcomes. Respondents were asked to rank potential reasons why a patient might not receive a proven intervention.Results: Of the 115 pharmacists invited, 53 (46%) participated in the survey. Most of the respondents practised on medical wards. The 2 most common reasons for non-use of proven interventions were a team preference to defer management of such issues to the outpatient care provider and issues related to workload.Conclusions: Clinical pharmacists revealed that their perceptions of priorities, communication with their interdisciplinary teams, and workload issues contributed to non-use of proven pharmacologic interventions among patients in their care. Efforts to increase the utilization of the proven clinical interventions studied here should focus on changing pharmacists’ perceptions of priorities.RÉSUMÉ Contexte : Récemment, les établissements de santé ont utilisé des indicateurs de rendement pour évaluer et améliorer la qualité des soins. L’un de ces indicateurs, l’indice d’intervention pharmacologique idéale, reflète le taux de mise en oeuvre d’interventions pharmacologiques éprouvées, dont la sous-utilisation a été montrée par des études. La détermination des raisons pour lesquelles les interventions éprouvées sont sous-utilisées est essentielle pour définir comment on peut accroître leur utilisation.Objectif : Caractériser les raisons de la non-utilisation des interventions éprouvées, du point de vue des pharmaciens cliniciens de l’établissement de santé des auteurs.Méthodes : Un sondage de tous les pharmaciens cliniciens de l’établissement de santé a été effectué. Le sondage comportait des études de cas standardisées impliquant des interventions pharmacologiques connues pour améliorer les résultats cliniques. On a demandé aux répondants de classer les raisons potentielles de l’absence d’intervention éprouvée pour un patient.Résultats : Des 115 pharmaciens invités à participer, 53 (46 %) ont répondu au sondage. La plupart des répondants travaillaient dans des unités de médecine. Les deux raisons les plus courantes pour l’absence d’interventions éprouvées étaient la préférence de l’équipe de relayer la prise en charge de tels problèmes de santé au fournisseur de soins de santé externe, et les questions liées à la charge de travail.Conclusions : Les pharmaciens cliniciens ont révélé que leurs perceptions des priorités, la communication avec leurs équipes interdisciplinaires et les motifs liés à la charge de travail ont contribué à l’absence d’interventions pharmacologiques éprouvées dans les soins de leurs patients. Les efforts pour accroître le recours aux interventions cliniques éprouvées évaluées dans cette étude doivent s’attarder à changer les perceptions qu’ont les pharmaciens des priorités.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".