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Record W1566282614 · doi:10.3386/w20927

Patient Responses to Incentives in Consumer-directed Health Plans: Evidence from Pharmaceuticals

2015· article· en· W1566282614 on OpenAlexfundno aff
Peter J. Huckfeldt, Amelia M. Haviland, Ateev Mehrotra, Zachary Wagner, Neeraj Sood

Bibliographic record

VenueNational Bureau of Economic Research · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsnot available
FundersNational Institutes of HealthCommon FundVanderbilt UniversityMcGill UniversityCalifornia Health Care FoundationUniversity of MinnesotaUniversity of Southern CaliforniaNational Institute on AgingEmory University
KeywordsIncentiveBusinessMarketingPublic economicsHealth planHealth careEconomicsMicroeconomicsEconomic growth

Abstract

fetched live from OpenAlex

Prior studies suggest that consumer-directed health plans (CDHPs) -characterized by high deductibles and health care accounts-reduce health costs, but there is concern that enrollees indiscriminately reduce use of low-value services (e.g., unnecessary emergency department use) and high-value services (e.g., preventive care).We investigate how CDHP enrollees change use of pharmaceuticals for chronic diseases.We compare two large firms where nearly all employees were switched to CDHPs to firms with conventional health insurance plans.In the first firm's CDHP, pharmaceuticals were subject to the deductible, while in the second firm pharmaceuticals were exempt.Employees in the first firm shifted the timing of drug purchases to periods with lower cost sharing and were more likely to use lower-cost drugs, but the largest effect of the CDHP was to reduce utilization.Employees in the second firm also reduced utilization, but did not shift the timing or use of low cost drugs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.099
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.099
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.661
GPT teacher head0.571
Teacher spread0.090 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2015
Admission routes1
Has abstractyes

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