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Record W4390957012 · doi:10.5334/ijic.icic23412

Implementing Measurement-Based Care (MBC) for Mental Health in a Paediatric Hospital Setting

2023· article· en· W4390957012 on OpenAlexaff
Suneeta Monga, Shivali Kapila, Samantha J. Anthony, Andrea Knight, Tala El Tal, Rachel Solomon, Jason Fischer, Natasha Saunders

Bibliographic record

VenueInternational Journal of Integrated Care · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsSickKids FoundationUniversity of Toronto
Fundersnot available
KeywordsMental healthMedicineHealth careIntegrated careNursingAnxietyPsychiatry

Abstract

fetched live from OpenAlex

Introduction: Measurement-Based Care (MBC) is the routine, systematic use of validated measures (e.g., patient-reported outcome measures [PROMs]) before or during clinical encounters to inform treatment decision-making. A large body of evidence, including randomized controlled trials, suggest that MBC outperforms usual care in response to treatment, remission rates, time to response and remission for individuals with depression, anxiety and other mental health conditions. MBC is a standard of care for most chronic conditions (e.g., diabetes, hypertension, asthma), however, it is underutilized in pediatric mental healthcare, despite well documented benefits and feasibility. As part of the hospital’s broader mandate to better integrate physical and mental healthcare, the MBC implementation team set out to develop an MBC system for evaluation of mental health concerns across the hospital with the goal to facilitate monitoring and response of mental health metrics at the patient, provider, organizational, and system levels. Methods: An environmental scan was conducted to understand the priority areas for health systems’ improvement. Stakeholders engaged in the process were physician leaders, clinical operational directors, clinical staff, Youth and Family Advisory Panel members across hospital departments. The environmental scan and follow-up focus groups (n=57) highlighted the need to prioritize implementation of standardized mental assessment tools hospital-wide for patients at each health system encounter as part of a vision to deliver holistic, integrated care and improve quality of patient care. Early identification of mental health needs of patients across the hospital and the importance of establishing standardized metrics for common mental health conditions were additional key priorities. Hospital surveys highlighted anxiety and depression as the top mental health concerns across the hospital. An extensive review was conducted to identify validated tools available and to understand implementation best practices. Learnings: Learnings from our review pointed to some key factors for successful implementation. These included the need for tools that are brief and easy to use, are validated and sensitive to change, are user friendly and visually appealing for the pediatric population and are integrated into the electronic health record with appropriate decision support tools to enable real time review. The need for extensive patient and provider education about the value of MBC and its implementation was also a key learning. Work in progress & next steps: Work is underway to map out clinical workflows, including ensuring a timely response to results, addressing implementation challenges such as virtual vs. in-person tool completion, and managing accessibility of sensitive data to the patient or their proxy in the electronic health record. Workflows will take an integrated, holistic care approach, whereby, a patient’s care team will action appropriate interventions based on a standardized algorithm that engages an interdisciplinary care team. Voxe, an external, patient-friendly platform to administer the two chosen assessment tools will be trialed. Discussions are underway with Information Management and Technology teams to integrate Voxe and clinical workflows in the hospital’s electronic health record. Evaluation of implementation of MBC in clinical settings will consider both process and outcome measures such as uptake, timely receipt of services, and clinical response.

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)0.033
metaresearch head score (Gemma)0.061
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: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.176

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.061
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0030.002
Scholarly communication0.0030.003
Open science0.0030.010
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.163
GPT teacher head0.420
Teacher spread0.258 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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