Expenditure mapping of pediatric imaging costs using a resource utilization band analysis of claims data.

Cost reduction Imaging costs Value

Journal

Current problems in diagnostic radiology
ISSN: 1535-6302
Titre abrégé: Curr Probl Diagn Radiol
Pays: United States
ID NLM: 7607123

Informations de publication

Date de publication:
18 Jul 2024
Historique:
received: 24 06 2024
accepted: 17 07 2024
medline: 26 7 2024
pubmed: 26 7 2024
entrez: 24 7 2024
Statut: aheadofprint

Résumé

To segregate imaging expenditures from claims data by resource utilization bands (RUBs) and underlying conditions to create an "expenditure map" of pediatric imaging costs. A Claims data for children enrolled in a commercial value-based plan were categorized by RUB 0 non-user, 1 healthy user, 2 low morbidity, 3 moderate morbidity, 4 high morbidity, & 5 very high morbidity. The per member per year (PMPY) expense, total imaging spend, and imaging modality with the highest spend were assessed for each RUB. Diagnosis categories associated with high imaging costs were also evaluated. There were 40,022 pediatric plan members. 14% had imaging-related claims accounting for approximately $2.8 million in expenditures. Member distribution and mean PMPY expenditure RUB was respectively: RUB 0 (3,037, $0), RUB 1 (6,604, $7), RUB 2 - 13,698, $27), RUB 3 - 13,341, $87), RUB 4 (2,810, $268), RUB 5 (532, $841). RUB 3 had the largest total imaging costs at $1,159,523. The imaging modality with the greatest mean PMPY expense varied by RUB with radiography highest in lower RUBs and MRI highest in higher RUBs. The top 3 diagnoses associated with the highest total imaging costs were developmental disorders ($443,980), asthma ($388,797), and congenital heart disease ($294,977) and greatest mean PMPY imaging expenditures malignancy/leukemia ($3,100), transplant ($2,639), and tracheostomy ($1,661). Expense mapping using claims data allows for a better understanding of the distribution of imaging costs across a covered pediatric population. This tool may assist organizations in planning effective cost-reduction initiatives and learning how imaging utilization varies by patient complexity in their system.

Identifiants

pubmed: 39048500
pii: S0363-0188(24)00129-4
doi: 10.1067/j.cpradiol.2024.07.018
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024. Published by Elsevier Inc.

Déclaration de conflit d'intérêts

Declarations of Competing Interest None.

Auteurs

Danika Baskar (D)

Department of Radiology, University of North Carolina School of Medicine, Chapel Hill, NC, USA.

Jamie A Jarmul (JA)

University of North Carolina Health Alliance, Morrisville, NC, USA.

Lane F Donnelly (LF)

Department of Radiology, University of North Carolina School of Medicine, Chapel Hill, NC, USA; Department of Pediatrics, University of North Carolina School of Medicine, Chapel Hill, NC, USA; University of North Carolina Health Alliance, Morrisville, NC, USA. Electronic address: lane_donnelly@med.unc.edu.

Classifications MeSH