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The Impact of Payment Reform on Utilization of Reconstructive Surgery

2020· article· en· W3160414651 on OpenAlexaboutno aff
Pooja Yesantharao, Pathik Aravind, Pragna N. Shetty, Amy Quan, Oluseyi Aliu

Bibliographic record

VenuePlastic & Reconstructive Surgery Global Open · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsMedicaidReconstructive surgeryMedicineConfidence intervalQuarter (Canadian coin)Health careSpecialtyAmbulatoryEmergency medicineFamily medicineGeneral surgerySurgeryPolitical scienceInternal medicineGeography

Abstract

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PURPOSE: Medicaid beneficiaries systematically face challenges in accessing healthcare, especially with regard to specialty services such as reconstructive surgery. In January 2014, the Affordable Care Act (ACA) took effect, allowing states to expand Medicaid eligibility. Concurrently, Maryland also launched statewide global budgeting of hospitals, intended to control healthcare costs.1 However, the impact of such reform on utilization of reconstructive procedures has not been characterized. This study evaluated the impact of Medicaid expansion and global hospital budgeting on utilization of 3 common reconstructive procedures (reconstructive breast surgery, maxillofacial surgery, and hand surgery) by marginalized populations (Medicaid/uninsured patients). MATERIALS AND METHODS: Adults in New Jersey (Medicaid expansion state), Maryland (expansion state with global hospital budgeting), and Florida (non-expansion state) undergoing the selected reconstructive procedures between 2012-2016 were tabulated using Healthcare Costs and Utilization Project State Ambulatory Surgery and Services and State Inpatient Databases. Interrupted time-series analyses were used to evaluate the impact of policy reform on reconstructive surgery utilization by marginalized patients. RESULTS: During the study period, 96,662 Medicaid/uninsured patients underwent the selected reconstructive procedures in the 3 states. The likelihood of Medicaid being listed as the primary payer for patients undergoing reconstructive surgery significantly increased in expansion states (Maryland absolute policy effect: 0.02% per quarter, 95% confidence interval: 0.01% to 0.02% per quarter; New Jersey absolute policy effect: 0.04% per quarter, 95% confidence interval 0.02%–0.05%) when compared to Florida (non-expansion state). There was also an immediate policy effect: within 1 year of ACA implementation, there was a significant increase in the proportion of Medicaid beneficiaries undergoing the reconstructive procedures (Maryland, 0.03%, 95% CI, 0.01%–0.05%; New Jersey, 0.01%, 95% CI, 0.01%–0.02%), whereas there was a significant decline in the proportion of uninsured patients (Maryland, −0.01%, 95% CI, −0.01% to 0.0%; New Jersey, −0.008%, 95% CI, −0.01% to −0.006%). Trends in Maryland versus New Jersey were compared with understand the impact of global hospital budgeting. Global budgeting did not significantly impact overall utilization of reconstructive procedures amongst Medicaid beneficiaries, though there was an increase in utilization of emergent/urgent reconstructive procedures that reached borderline significance (0.03% per quarter; 95% CI, 0.0%–0.05%). CONCLUSIONS: Medicaid beneficiaries experienced an increased utilization of reconstructive surgery post-ACA in expansion states when compared with nonexpansion states, mirroring trends in other areas of healthcare. Increased utilization by Medicare beneficiaries was not completely offset by decreases in utilization by uninsured patients, suggesting that the ACA expanded access to reconstructive surgery. It was encouraging that global hospital budgeting did not limit utilization of reconstructive procedures by Medicaid beneficiaries. In fact, utilization of emergent/urgent procedures among marginalized patients in globally budgeted hospitals increased, perhaps as a result of greater incentives for hospitals to connect vulnerable/high-risk patients to the care they need under this system. REFERENCE: 1. Rajkumar R, Patel A, Murphy K, et al. Maryland’s all-payer approach to delivery-system reform. N Engl J Med. 2014;370:493.

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)0.004
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 score0.232
Threshold uncertainty score0.795

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.079
GPT teacher head0.330
Teacher spread0.251 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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