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Record W2790500775 · doi:10.5430/jha.v7n2p18

What’s in it for me? Incentive compensation in hospital medicine

2018· article· en· W2790500775 on OpenAlexvenueno aff
A. Charlotta Weaver, Nita Shrikant Kulkarni, Rachel Cyrus

Bibliographic record

VenueJournal of Hospital Administration · 2018
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsIncentiveProductivityReimbursementSalaryCompensation (psychology)PaymentQuality (philosophy)Resource-based relative value scaleMedicaidWork (physics)Incentive programBusinessMedicineActuarial scienceOperations managementHealth carePsychologyEconomicsFinance

Abstract

fetched live from OpenAlex

Objective: Though salary models vary, a portion of physician compensation is often provided as a bonus, or incentive payment, based on clinical productivity measured by the relative value unit (RVU). However, many hospitalists are involved in activities beyond clinical work, either administrative or educational, that may be difficult to measure and recognize in bonus payments. Furthermore, the changing nature of physician and hospital reimbursement necessitates a focus on quality measures not incentivized in the traditional RVU model.Methods: The authors describe a compensation model in an academic hospital medicine program that was initially developed in 2010 and modified in 2013. The model incents clinical productivity and adherence to quality metrics while also promoting nonclinical academic and administrative activities.Results: Implementation of this compensation model impacted the division’s quality goals by increasing completion of discharge summaries, reconciliation of discharge medications, and placement of follow-up appointment orders upon discharge. The impact on clinical and academic productivity is less clear.Conclusions: A compensation model that accounts for academic productivity and quality goals along with clinical productivity may be useful in incentivizing hospitalists in both academic and community-based hospital medicine practices. Future work should focus on whether such a model can be used effectively to address additional targets such as reducing readmissions and preventing hospital-acquired conditions.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.113
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.021
GPT teacher head0.335
Teacher spread0.315 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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