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Record W2151341027 · doi:10.1001/jama.2014.11492

Effect of a Postdischarge Virtual Ward on Readmission or Death for High-Risk Patients

2014· article· en· W2151341027 on OpenAlexafffundabout
Irfan A. Dhalla, Tara O’Brien, Dante Morra, Kevin E. Thorpe, Brian M. Wong, Rajin Mehta, David Frost, Howard Abrams, Françoise Ko, Patrick Van Rooyen, Chaim M. Bell, Andrea Gruneir, Geraint Lewis, Stacey Daub, Geoff Anderson, Gillian Hawker, Paula A. Rochon, Andreas Laupacis

Bibliographic record

VenueJAMA · 2014
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreInstitute for Clinical Evaluative SciencesHome and Community Care Support ServicesTrillium Health CentreWomen's College HospitalUniversity Health NetworkUniversity of TorontoSt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsMedicinePsychological interventionMedical prescriptionIntervention (counseling)Emergency medicineRandomized controlled trialHospital readmissionMEDLINEFamily medicineMedical emergencyNursing

Abstract

fetched live from OpenAlex

IMPORTANCE: Hospital readmissions are common and costly, and no single intervention or bundle of interventions has reliably reduced readmissions. Virtual wards, which use elements of hospital care in the community, have the potential to reduce readmissions, but have not yet been rigorously evaluated. OBJECTIVE: To determine whether a virtual ward-a model of care that uses some of the systems of a hospital ward to provide interprofessional care for community-dwelling patients-can reduce the risk of readmission in patients at high risk of readmission or death when being discharged from hospital. DESIGN, SETTING, AND PATIENTS: High-risk adult hospital discharge patients in Toronto were randomly assigned to either the virtual ward or usual care. A total of 1923 patients were randomized during the course of the study: 960 to the usual care group and 963 to the virtual ward group. The first patient was enrolled on June 29, 2010, and follow-up was completed on June 2, 2014. INTERVENTIONS: Patients assigned to the virtual ward received care coordination plus direct care provision (via a combination of telephone, home visits, or clinic visits) from an interprofessional team for several weeks after hospital discharge. The interprofessional team met daily at a central site to design and implement individualized management plans. Patients assigned to usual care typically received a typed, structured discharge summary, prescription for new medications if indicated, counseling from the resident physician, arrangements for home care as needed, and recommendations, appointments, or both for follow-up care with physicians as indicated. MAIN OUTCOMES AND MEASURES: The primary outcome was a composite of hospital readmission or death within 30 days of discharge. Secondary outcomes included nursing home admission and emergency department visits, each of the components of the primary outcome at 30 days, as well as each of the outcomes (including the composite primary outcome) at 90 days, 6 months, and 1 year. RESULTS: There were no statistically significant between-group differences in the primary or secondary outcomes at 30 or 90 days, 6 months, or 1 year. The primary outcome occurred in 203 of 959 (21.2%) of the virtual ward patients and 235 of 956 (24.6%) of the usual care patients (absolute difference, 3.4%; 95% CI, -0.3% to 7.2%; P = .09). There were no statistically significant interactions to indicate that the virtual ward model of care was more or less effective in any of the prespecified subgroups. CONCLUSIONS AND RELEVANCE: In a diverse group of high-risk patients being discharged from the hospital, we found no statistically significant effect of a virtual ward model of care on readmissions or death at either 30 days or 90 days, 6 months, or 1 year after hospital discharge. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT01108172.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.825
Threshold uncertainty score0.281

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.010
GPT teacher head0.276
Teacher spread0.267 · 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 designOther design
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".

Quick stats

Citations136
Published2014
Admission routes3
Has abstractyes

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