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Record W4401921019 · doi:10.1192/j.eurpsy.2024.349

Mental Health in Medicine: A novel stepped care model in medical psychiatry and the implementation of measurement-based care

2024· article· en· W4401921019 on OpenAlexaffabout
David Wiercigroch, Shannon Wright, N. Bangloy, Shane S. Bush, Abraham Ka Chung Wai, Deepa Koshy, Lauren Thomson, N. Gawad, Susan Abbey, Michael Kaye, M. Kelsey, Adrienne Tan, Justin Delwo, Katie Jane Sheehan

Bibliographic record

VenueEuropean Psychiatry · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicMental Health and Psychiatry
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMental health careMental healthPsychiatryHealth careMedical careMedicinePsychologyNursingPolitical science

Abstract

fetched live from OpenAlex

Introduction Individuals with co-occurring mental and physical health issues have worse health outcomes in both domains. Integration improves outcomes and aligns with patient preference, but health services tend to be siloed. The Mental Health in Medicine Clinic (MHiM) supports patients receiving inpatient or outpatient medical or surgical care at a tertiary academic hospital in Toronto, Canada. The predominantly virtual clinic has an interdisciplinary team offering services via stepped care, matching patient need with service intensity. Measurement-based care (MBC), the systematic evaluation of patient reported outcomes, was not initially used routinely in the clinic, but its implementation may improve treatment decision-making and may be useful in allocating patients within a stepped care model. Objectives 1) To describe the stepped care model, referral patterns, diagnoses, and level of care provided since implementation of stepped care. 2) To conduct a quality improvement initiative to implement MBC in the clinic, with a goal of 50% of patients completing at the time of first assessment and prior to discharge from the clinic. Methods We reviewed the electronic medical record for referral source, diagnoses, and level of stepped care within the clinic. We conducted semi-structured interviews with stakeholders (clinicians, administrative staff, patients) to explore barriers to implementation of MBC. Interviews were analyzed for themes around barriers and facilitators to MBC. Plan, Do, Study, Act cycles were carried out around change concepts informed by stakeholder interviews and relevant literature. Results The MHiM clinic began operations in August 2020. The clinic operated on a physician-only model until March 2022 and then shifted to a stepped care model with an interdisciplinary team. The most frequent referral sources were internal medicine, COVID19 clinics, consultation-liaison psychiatry, red blood cell disorders clinic and cardiology. Since the implementation of stepped care, 250 referrals were assessed. 58% of new referrals were assessed by the psychiatrist, 42% were managed by the NP, and 25% consulted with the social worker. Referrals consisted of trauma and stress-related disorders (32%), depression (21%) or anxiety disorders (20%). Personality, substance use, and psychotic disorders accounted for less than 10% of referrals combined. Some patients did not have any diagnosis (6%). Results from the quality improvement initiative to implement MBC will also be presented. Conclusions The MHiM clinic provides an integrated care pathway addressing comorbid mental and physical health conditions. We describe a novel stepped care model and the implementation of MBC. Future directions include ongoing quality improvement of MBC and its integration within the clinic to assess and re-assess service intensity. Disclosure of Interest None Declared

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)0.017
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0050.018
Scholarly communication0.0130.009
Open science0.0040.012
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0030.000

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 head0.036
GPT teacher head0.326
Teacher spread0.290 · 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".

Quick stats

Citations2
Published2024
Admission routes2
Has abstractyes

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