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Record W1901287588 · doi:10.5430/jha.v5n1p1

Impact of incentive driven medical home approach on use of preventive services use among healthcare system employees: A case study

2015· article· en· W1901287588 on OpenAlexvenueno aff
Sandra E. Brooks, Yehia H. Khalil, Allison M. Ledford, Tina M. Hembree

Bibliographic record

VenueJournal of Hospital Administration · 2015
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsnot available
Fundersnot available
KeywordsIncentivePreventive careIntervention (counseling)MedicineHealth careDescriptive statisticsFamily medicineIncentive programEmergency departmentNursingMedical emergency

Abstract

fetched live from OpenAlex

Objective: To describe the initial outcomes of an incentive driven medical home and navigation program on preventive services among healthcare system employees.Methods: Quasi-experimental design examining participation, use of preventive services and adherence to medical guidelines and emergency room use in a five hospital integrated health system. Employees were required to complete a health risk assessment (HRA), visit a Primary Care Provider (PCP) and submit PCP visit screening and biometric results in order to be eligible for the financial incentives. Subsidized lifestyle change intervention and navigation programs were also offered to participants. Descriptive statistics and Chi Square were used to analyze results for the 5,435 employee participants and 3,623 non-participants during thee 1-year intervention.Results: Preventive care visits for participants increased by 35% compared to an increase of 3% for non-participants. Nonadherence to medical guidelines decreased 7% for participants and increased 18% for non-participants. Inappropriate emergency room use overall decreased from 20% to 14%.Conclusions: One year after introduction of the wellness program, preventive visits increased, compliance with medical care increased and inappropriate emergency room visits were reduced.

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)0.002
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 score0.195
Threshold uncertainty score0.716

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.056
GPT teacher head0.408
Teacher spread0.352 · 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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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