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Record W2981352504 · doi:10.1093/eurheartj/ehz748.0003

1109Enhancing transition from hospital to home for people with cardiovascular disease

2019· article· en· W2981352504 on OpenAlexaboutno aff
Mary Boyde, R. Peters, Elizabeth A. McGlynn

Bibliographic record

VenueEuropean Heart Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicHealthcare Systems and Public Health
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineReferralHeart failureTransitional careDisease managementAnginaHealth careTertiary referral hospitalDiseaseEmergency medicineMedical emergencyCanadian Cardiovascular SocietyIntensive care medicineNursingMyocardial infarctionRetrospective cohort studyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Health care services have a responsibility to ensure transition from hospital to home is seamless as this is a vulnerable time for patients. In 2013 the Cardiac Rehabilitation (CR) and Heart Failure (HF) Management Programs at a large tertiary referral hospital in our city, were re-configured to become one cardiac service. This model of care incorporated management of patients along the continuum of cardiac illness from initial diagnosis to end stage disease including end stage heart failure and intractable angina. This model included nurse-led in-patient consultations, post-discharge case management, CR and HF education and exercise programs, HF titration clinics, and home visits. Within health-care, organisational design can be considered a variable and evolving tool for improving the quality of patient care. The redesign of two clinical services provided an opportunity to improve health-care delivery for patients with cardiovascular disease. Purpose To evaluate a patient centred post-discharge model of care for patients admitted to hospital for an acute cardiovascular event. Methods A retrospective analysis of routinely collected data was undertaken using standard descriptive statistics to report the number of in-patient consultations, referrals generated, patients case-managed and all cause unplanned readmissions within 28 days of hospital discharge. Results Data was analysed from 1 January 2014 to 31 December 2018. Over the 5 years 11929 in-patients with cardiovascular disease received consultations from specialised nursing staff employed in the amalgamated cardiac service and 10598 (89%) patients were referred to appropriate post discharge CR and HF programs. From the in-patient consultations, 2254 (21%) patients who lived within our geographical area were case-managed by our specialist nursing staff. Post-discharge follow-up within 14 days was achieved for 1917 (85%) of these patients. Of the 2254 patients, 322 (14%) had an unplanned all cause readmission within 28 days of hospital discharge. Conclusion The evaluation data indicates that our model of integrated care provided effective post discharge management for patients with cardiac disease. Developing a model of care for these patients is challenging as they need to engage in self-care and adhere to a complex medication regime often with changing dosages. However effective case management post discharge including secondary prevention programs can decrease readmissions and improve outcomes for these patients.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.172
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.272
Teacher spread0.253 · 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 teacher head, 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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Citations0
Published2019
Admission routes1
Has abstractyes

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