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Record W2953517112 · doi:10.4236/ojpsych.2019.93017

Estimation of Costs-Savings and Improved Patient Outcomes of Implementing a Consultation-Liaison Service at Health Sciences North

2019· article· en· W2953517112 on OpenAlexafffund
Elendu Okoronkwo

Bibliographic record

VenueOpen Journal of Psychiatry · 2019
Typearticle
Languageen
FieldPsychology
TopicPsychiatric care and mental health services
Canadian institutionsNOSM UniversityLaurentian UniversityInstitute for Clinical Evaluative SciencesHealth Sciences North
FundersInstitute for Clinical Evaluative SciencesNorthern Ontario Academic Medicine Association
KeywordsReferralService (business)Patient satisfactionMedicineCohortFamily medicineService qualityNursingBusinessMarketing

Abstract

fetched live from OpenAlex

Objective: The study was conducted to assess the implementation of a psychiatric consultation-liaison service (C-L) from the perspective of cost-savings, staff satisfaction, patient satisfaction and to assess the general features of patients referred to the C-L service. Methodology: Cost-savings were evaluated using a large cohort of referrals to the hospital were identified using data derived from the Institute of Clinical Evaluative Sciences (N = 2246); these data were divided into pre and post periods with respect to imitation of the C-L service. To evaluate staff satisfaction, 170 nurses and physicians completed an online survey. Patient satisfaction was assessed through a survey assessing various aspects of their experiences with the C-L service that was completed by each patient (N = 40). Finally referrals to the C-L service (N = 445) were analyzed to discern indicators of the C-L service’s efficacy (i.e. reasons for referral, time to accommodate referral). Results: The data indicated: 1) a reduction in the number of re-admissions and length of stay after the initiation of the C-L service translating into significant cost-savings for the hospital, 2) that increased staff satisfaction was associated with providing confidence, support, and improved communication, and 3) that the C-L service accommodated approximately 90% of patients within 1 day. Conclusion: The results of this study support stakeholders’ decisions to implement C-L services and also indicate areas of improvement that may improve the quality of C-L services within other institutions.

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)0.001
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 score0.060
Threshold uncertainty score0.805

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.020
GPT teacher head0.375
Teacher spread0.355 · 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".

Quick stats

Citations6
Published2019
Admission routes2
Has abstractyes

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