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Record W2027524489 · doi:10.1377/hlthaff.2011.0216

Integrated Telehealth And Care Management Program For Medicare Beneficiaries With Chronic Disease Linked To Savings

2011· article· en· W2027524489 on OpenAlexaboutno aff
Laurence C. Baker, Scott J. Johnson, Dendy Macaulay, Howard G. Birnbaum

Bibliographic record

VenueHealth Affairs · 2011
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsTelehealthMedicineHealth careDisease managementIntervention (counseling)Quarter (Canadian coin)Chronic diseaseDiseaseTelemedicineFamily medicineNursing

Abstract

fetched live from OpenAlex

Treatment of chronically ill people constitutes nearly four-fifths of US health care spending, but it is hampered by a fragmented delivery system and discontinuities of care. We examined the impact of a care coordination approach called the Health Buddy Program, which integrates a telehealth tool with care management for chronically ill Medicare beneficiaries. We evaluated the program's impact on spending for patients of two clinics in the US Northwest who were exposed to the intervention, and we compared their experience with that of matched controls. We found significant savings among patients who used the Health Buddy telehealth program, which was associated with spending reductions of approximately 7.7-13.3 percent ($312-$542) per person per quarter. These results suggest that carefully designed and implemented care management and telehealth programs can help reduce health care spending and that such programs merit continued attention by Medicare. Meanwhile, mortality differences in the treatment and control groups suggest that the intervention may have produced noticeable changes in health outcomes, but we leave it to future research to explore these effects fully.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.875
Threshold uncertainty score1.000

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 head0.032
GPT teacher head0.322
Teacher spread0.291 · 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 designOther design
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

Citations115
Published2011
Admission routes1
Has abstractyes

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