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Record W2901946782 · doi:10.2215/cjn.05680518

Medicare’s New Prospective Payment System on Facility Provision of Peritoneal Dialysis

2018· article· en· W2901946782 on OpenAlexaff
Virginia Wang, Cynthia J. Coffman, Linda Sanders, Shoou-Yih D. Lee, Richard A. Hirth, Matthew L. Maciejewski

Bibliographic record

VenueClinical Journal of the American Society of Nephrology · 2018
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsInstitute of Population and Public Health
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsPeritoneal dialysisMedicineProspective payment systemDialysisProspective cohort studyHemodialysisPaymentEmergency medicineIntensive care medicineInternal medicineFinanceBusiness

Abstract

fetched live from OpenAlex

Background and objectives Peritoneal dialysis is a self-administered, home-based treatment for ESKD associated with equivalent mortality, higher quality of life, and lower costs compared with hemodialysis. In 2011, Medicare implemented a comprehensive prospective payment system that makes a single payment for all dialysis, medication, and ancillary services. We examined whether the prospective payment system increased dialysis facility provision of peritoneal dialysis services and whether changes in peritoneal dialysis provision were more common among dialysis facilities that are chain affiliated, located in nonurban areas, and in regions with high dialysis market competition. Design, setting, participants, & measurements We conducted a longitudinal retrospective cohort study of n =6433 United States nonfederal dialysis facilities before (2006–2010) and after (2011–2013) the prospective payment system using data from the US Renal Data System, Medicare, and Area Health Resource Files. The outcomes of interest were a dichotomous indicator of peritoneal dialysis service availability and a discrete count variable of dialysis facility peritoneal dialysis program size defined as the annual number of patients on peritoneal dialysis in a facility. We used general estimating equation models to examine changes in peritoneal dialysis service offerings and peritoneal dialysis program size by a pre– versus post-prospective payment system effect and whether changes differed by chain affiliation, urban location, facility size, or market competition, adjusting for 1-year lagged facility–, patient with ESKD–, and region-level demographic characteristics. Results We found a modest increase in observed facility provision of peritoneal dialysis and peritoneal dialysis program size after the prospective payment system (36% and 5.7 patients in 2006 to 42% and 6.9 patients in 2013, respectively). There was a positive association of the prospective payment system with peritoneal dialysis provision (odds ratio, 1.20; 95% confidence interval, 1.13 to 1.18) and PD program size (incidence rate ratio, 1.27; 95% confidence interval, 1.22 to 1.33). Post-prospective payment system change in peritoneal dialysis provision was greater among nonurban ( P <0.001), chain-affiliated ( P =0.002), and larger-sized facilities ( P <0.001), and there were higher rates of peritoneal dialysis program size growth in nonurban facilities ( P <0.001). Conclusions Medicare’s 2011 prospective payment system was associated with more facilities’ availability of peritoneal dialysis and modest growth in facility peritoneal dialysis program size. Podcast This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2018_11_19_CJASNPodcast_18_12_.mp3

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)0.005
metaresearch head score (Gemma)0.030
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 score0.041
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.005
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.029
GPT teacher head0.348
Teacher spread0.319 · 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

Citations24
Published2018
Admission routes1
Has abstractyes

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