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Record W3091025693 · doi:10.1542/hpeds.2020-0093

A Multidisciplinary Home Visiting Program for Children With Medical Complexity

2020· article· en· W3091025693 on OpenAlexaff
Elaine Lin, Kathryn Scharbach, Bian Liu, Maureen K. Braun, Candace Tannis, Karen Wilson, Joseph Truglio

Bibliographic record

VenueHospital Pediatrics · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsInstitute of Population and Public Health
Fundersnot available
KeywordsMedicineMultidisciplinary approachMedical homeEmergency departmentMedical recordDemographicsHealth careFamily medicineMEDLINEEmergency medicineMedical emergencyPrimary careNursingDemography

Abstract

fetched live from OpenAlex

OBJECTIVES: Given the high needs and costs associated with the care of children with medical complexity (CMC), innovative models of care are needed. Home-visiting care models are effective in subpopulations of pediatrics and medically complex adults, but there is no literature on this model for CMC. We describe the development and outcomes of a multidisciplinary program that provides comprehensive home-based primary care for CMC. METHODS: Medical records from our institution were reviewed for patients enrolled in our program from July 2013 through March 2019. Demographics, clinical characteristics, and health care use were collected. We compared the differences in pre- and postprogram enrollment health care use using Wilcoxon signed rank test. We applied Cox proportional hazard models to examine the association between the time-dependent postenrollment health care use and numbers of home visits. We collected total claims data for a subset of our patients to examine total costs of care. RESULTS: We reviewed data collected from 121 patients. With our findings, we demonstrate that enrollment in our program is associated with reductions in average length of stay. More home visits were associated with decreased emergency department visits and hospitalizations. We also observed in patients with available cost data that total costs of care decreased after enrollment into the program. CONCLUSIONS: Our model has the potential to improve health outcomes and be financially sustainable by providing home-based primary care to CMC.

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)0.000
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 score0.424
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.046
GPT teacher head0.285
Teacher spread0.239 · 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

Citations15
Published2020
Admission routes1
Has abstractyes

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