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Record W2222933325 · doi:10.1377/hlthaff.2015.0278

Patients With High Mental Health Costs Incur Over 30 Percent More Costs Than Other High-Cost Patients

2016· article· en· W2222933325 on OpenAlexaffabout
Claire de Oliveira, Joyce Cheng, Simone N. Vigod, Jürgen Rehm, Paul Kurdyak

Bibliographic record

VenueHealth Affairs · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsWomen's College HospitalCentre for Addiction and Mental Health
Fundersnot available
KeywordsMental healthHealth careMedicinePsychological interventionTotal costPopulationEnvironmental healthFamily medicineNursingPsychiatryBusiness

Abstract

fetched live from OpenAlex

A small proportion of health care users, called high-cost patients, account for a disproportionately large share of health care costs. Most literature on these patients has focused on the entire population. However, high-cost patients whose use of mental health care services is substantial are likely to differ from other members of the population. We defined a mental health high-cost patient as someone for whom mental health-related services accounted for at least 50 percent of total health care costs. We examined these patients' health care utilization and costs in Ontario, Canada. We found that their average cost for health care, in 2012 Canadian dollars, was $31,611. In contrast, the cost was $23,681 for other high-cost patients. Mental health high-cost patients were younger, lived in poorer neighborhoods, and had different health care utilization patterns, compared to other high-cost patients. These findings should be considered when implementing policies or interventions to address quality of care for mental health patients so as to ensure that mental health high-cost patients receive appropriate care in a cost-effective manner. Furthermore, efforts to manage mental health patients' health care use should address their complex profile through integrated multidisciplinary health care delivery.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.003

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 head0.083
GPT teacher head0.359
Teacher spread0.276 · 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; both teacher heads agree on what is shown here.

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

Citations75
Published2016
Admission routes2
Has abstractyes

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