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Record W2465381438 · doi:10.36076/ppj.2007/10/607

Physician Payment 2008 for Interventionalists:Current State of Health Care Policy

2007· article· en· W2465381438 on OpenAlexaboutno aff
Laxmaiah Manchikanti

Bibliographic record

VenuePain Physician · 2007
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
FundersStars FoundationGeorgetown University
KeywordsMedicineHealth carePaymentMedical prescriptionSolvencyInflation (cosmology)Actuarial scienceFamily medicineFinanceBusinessNursingEconomic growthEconomics

Abstract

fetched live from OpenAlex

Physicians in the United States have been affected by significant changes in the pattern(s) of medical practice evolving over the last several decades. These changes include new measures to 1) curb increasing costs, 2) increase access to patient care, 3) improve quality of healthcare, and 4) pay for prescription drugs. Escalating healthcare costs have focused concerns about the financial solvency of Medicare and this in turn has fostered a renewed interest in the economic basis of interventional pain management practices. The provision and systemization of healthcare in North America and several European countries are difficult enterprises to manage irrespective of whether these provisions and systems are privatized (as in the United States) or nationalized or seminationalized (as in Great Britain, Canada, Australia and France). Consequently, while many management options have been put forth, none seem to be optimally geared toward affording healthcare as a maximized individual and social good, and none have been completely enacted. The current physician fee schedule (released on July 12, 2007) includes a 9.9% cut in payment rate. Since the Medicare program was created in 1965, several methods have been used to determine physicians' rate(s) for each covered service. The sustained growth rate (SGR) system, established in 1998, has evoked negative consequences on physician payment(s). Based on the current Medicare expenditure index, practice expenses are projected to increase by 34.5% from 2002 to 2016, whereas, if actual practice inflation is considered, this increase will be 90%. This is in contrast to projected physician payment cuts that are depicted to be 51%. No doubt, this scenario will be devastating to many practices and the US medical community at large. Resolutions to this problem have been offered by MedPAC, the Government Accountability Office, physician organizations, economists, and various other interested groups. In the past, temporary measures have been proposed (and sometimes implemented) to eliminate physician payment cuts. At present, the US Senate and House of Representatives are separately working on 2 different mechanisms to address and rectify these cost-payment discrepancies. The effects of both the problem and the potential solutions on interventional pain management may be somewhat greater than those on other specialties. Physician payments in interventional pain management may evidence cuts of 10% to 15%, whereas if procedures are performed in an office setting, such cuts may range from 29% to 39% over the period of the next 3 years if the proposed 9.9% cut is not reversed. Medicare cuts also impact other insurance payments, incurring a "ripple effect" such that many insurers will seek to pay at or around the Medicare rate. In this manuscript, we discuss universal healthcare systems, the CMS proposed ruling and its attendant ripple effect(s), historical aspects of the Medicare payment system, the Sustained Growth Rate system, and the potential consequences incurred by both proposed cuts and potential solutions to the discrepant cost-payment issue(s). As well, ethical issues of policy development upon the infrastructure and practice of interventional pain management are addressed.

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)0.016
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.038
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0020.005
Scholarly communication0.0090.005
Open science0.0030.003
Research integrity0.0100.008
Insufficient payload (model declined to judge)0.0070.002

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 head0.338
Teacher spread0.287 · 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 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

Citations82
Published2007
Admission routes1
Has abstractyes

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