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Record W2143214680 · doi:10.1377/hlthaff.2014.0855

Reduced Acute Inpatient Care Was Largest Savings Component Of Geisinger Health System’s Patient-Centered Medical Home

2015· article· en· W2143214680 on OpenAlexaff
Daniel Maeng, Nazmul Khan, Janet Tomcavage, Thomas Graf, Duane E. Davis, Glenn Steele

Bibliographic record

VenueHealth Affairs · 2015
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsPricewaterhouseCoopers (Canada)
Fundersnot available
KeywordsMedicineMedical homeComponent (thermodynamics)Emergency medicinePatient-centered careHealth careAcute careMedical emergencyFamily medicinePrimary care

Abstract

fetched live from OpenAlex

Early evidence suggests that the patient-centered medical home has the potential to improve patient outcomes while reducing the cost of care. However, it is unclear how this care model achieves such desirable results, particularly its impact on cost. We estimated cost savings associated with Geisinger Health System's patient-centered medical home clinics by examining longitudinal clinic-level claims data from elderly Medicare patients attending the clinics over a ninety-month period (2006 through the first half of 2013). We also used these data to deconstruct savings into its main components (inpatient, outpatient, professional, and prescription drugs). During this period, total costs associated with patient-centered medical home exposure declined by approximately 7.9 percent; the largest source of this savings was acute inpatient care ($34, or 19 percent savings per member per month), which accounts for about 64 percent of the total estimated savings. This finding is further supported by the fact that longer exposure was also associated with lower acute inpatient admission rates. The results of this study suggest that patient-centered medical homes can lead to sustainable, long-term improvements in patient health outcomes and the cost of care.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.051
GPT teacher head0.388
Teacher spread0.337 · 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.

Study designNot applicable
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

Citations49
Published2015
Admission routes1
Has abstractyes

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