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Record W2782302716 · doi:10.4212/cjhp.v70i6.1711

Care Gaps in the Electronic Discharge Medication Reconciliation Process at an Acute Care Facility

2017· article· en· W2782302716 on OpenAlexafffundvenueabout
Kelly S. MacDonald, Marsha Cusack, Su Qiong Rebecca Liang, Kilby Rinco

Bibliographic record

VenueThe Canadian Journal of Hospital Pharmacy · 2017
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsHillsborough HospitalHealth PEIUniversity of Prince Edward Island
FundersDalhousie University
KeywordsAcute careProcess (computing)Medical emergencyMedication ReconciliationMedicineNursingHealth careComputer sciencePolitical science

Abstract

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ABSTRACT Background: Many patients experience adverse events at the time of discharge from hospital, and most of these events are medication-related. To improve patient safety, Health PEI (the health authority for Prince Edward Island) has made medication reconciliation a priority. The Queen Elizabeth Hospital in Charlottetown is one of the few Canadian hospitals with an electronic discharge process. A discharge report has been developed to provide pertinent information to patients at discharge, including a final medication list to be shared with the community pharmacy at the patient’s discretion. Objective: To identify care gaps related to the transfer of information for the medication reconciliation part of the electronic discharge process at the Queen Elizabeth Hospital. Methods: The study was conducted on 4 nursing units offering medical and surgical services. Data for the 8-week prospective study (June to August 2016) were collected using a study-specific discharge evaluation checklist and hospital-to-community pharmacy feedback form. All inpatients 65 years of age or older with a hospital stay longer than 4 days who were receiving more than 5 medications on discharge were eligible to participate. Results: During the study period, data were compiled for the 72 of 154 eligible patients who provided consent. Of these, 69 (96%) had a change in medications. Follow-up showed that 12 (17%) of the 72 discharge reports had reached the patient’s community pharmacy; of these, 5 had been sent from a community care or long-term care facility. Fifty-four patients were discharged home, of whom 50 presented to the community pharmacy after discharge, 37 (74%) of these on the day of discharge. Conclusions: Most community pharmacies did not receive a discharge report from the patient or from the patient’s community care or long term care facility. This represented the largest care gap in the electronic discharge medication reconciliation process at the study hospital. RÉSUMÉ Contexte : Lors de leur congé de l’hôpital, bon nombre de patients subissent des événements indésirables, dont la plupart sont de nature pharmacothérapeutique. Afin d’améliorer la sécurité des patients, Santé Î.-P.-É. (la régie de santé de l’Île-du-Prince-Édouard) a fait du bilan comparatif des médicaments une priorité. L’Hôpital Queen Elizabeth, à Charlottetown, est l’un des rares établissements canadiens dotés d’un processus de congé électronique. Un rapport a été mis au point afin d’offrir de l’information pertinente aux patients au moment du congé, y compris une liste définitive des médicaments à faire parvenir à la pharmacie communautaire au gré du patient. Objectif : Identifier les lacunes dans les soins liées au transfert d’information relative au bilan comparatif des médicaments faisant partie du processus de congé électronique à l’Hôpital Queen Elizabeth. Méthodes : L’étude a été menée dans quatre unités de soins offrant des services médicaux et chirurgicaux. Les données pour l’étude prospective d’une durée de huit semaines (de juin à août 2016) ont été recueillies à l’aide d’une liste de vérification conçue spécialement pour procéder à l’évaluation après le congé durant cette étude et d’un formulaire de rétroaction sur le transfert d’information de l’hôpital à la pharmacie communautaire. Tous les patients hospitalisés de 65 ans et plus don’t le séjour était de plus de quatre jours et qui recevaient plus de cinq médicaments au moment du congé étaient admissibles à l’étude. Résultats : Pendant la période de l’étude, des données ont été recueillies auprès de 72 des 154 patients admissibles ayant donné leur accord. Parmi ceux-ci, 69 (96 %) ont connu un changement à leur pharmacothérapie. Un suivi a montré que 12 (17 %) des 72 rapports de congé ont été reçus par la pharmacie communautaire du patient; parmi ceux-ci, cinq ont été envoyés à partir d’un établissement de soins communautaires ou de soins de longue durée. Cinquante-quatre patients sont retournés à leur domicile après leur congé; 50 d’entre eux se sont présentés à la pharmacie communautaire et 37 (74%) de ce groupe ont fait cette visite le jour du congé. Conclusion : La plupart des pharmacies communautaires n’ont pas reçu le rapport de congé de la part du patient ou de son établissement de soins communautaires ou de soins de longue durée. Il s’agissait de la plus mportante lacune dans les soins concernant le processus électronique de bilan comparatif des médicaments au moment du congé à l’hôpital où s’est déroulée l’étude.

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 imitation

Not 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.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.089
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.089
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0020.003
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.064
GPT teacher head0.407
Teacher spread0.342 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations2
Published2017
Admission routes4
Has abstractyes

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