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Expenditure mapping of pediatric imaging costs using a resource utilization band analysis of claims data
Danika Baskar1, Jamie A Jarmul2, Lane F Donnelly3
1Department of Radiology, University of North Carolina School of Medicine, Chapel Hill, NC, USA.
Insights
Mapping pediatric imaging costs using claims data reveals significant spending variations by patient complexity. This analysis helps identify high-cost conditions and optimize resource utilization for cost-reduction initiatives.
Area of Science:
- Health Economics
- Pediatric Healthcare Analytics
- Medical Imaging Utilization
Background:
- Understanding pediatric imaging expenditure is crucial for healthcare cost management.
- Claims data offers a valuable resource for analyzing healthcare spending patterns.
- Resource Utilization Bands (RUBs) provide a framework for stratifying patient complexity.
Purpose of the Study:
- To create an "expenditure map" of pediatric imaging costs.
- To segregate imaging expenditures by Resource Utilization Bands (RUBs) and underlying conditions.
- To analyze imaging cost distribution across a pediatric population.
Main Methods:
- Claims data from a commercial value-based plan for pediatric members were analyzed.
- Members were categorized into six Resource Utilization Bands (RUBs) based on morbidity.
- Per member per year (PMPY) expense, total imaging spend, and top imaging modalities were assessed per RUB.
- Diagnosis categories linked to high imaging costs were evaluated.
Main Results:
- 14% of 40,022 pediatric members incurred approximately $2.8 million in imaging costs.
- Moderate morbidity (RUB 3) had the highest total imaging costs ($1,159,523).
- Imaging modality spend varied by RUB, with radiography dominant in lower RUBs and MRI in higher RUBs.
- Top diagnoses for total imaging costs included developmental disorders, asthma, and congenital heart disease.
Conclusions:
- Expense mapping using claims data enhances understanding of pediatric imaging cost distribution.
- This approach aids in planning cost-reduction initiatives.
- It provides insights into how imaging utilization correlates with patient complexity.
Objective:
To segregate imaging expenditures from claims data by resource utilization bands (RUBs) and underlying conditions to create an "expenditure map" of pediatric imaging costs.
Methods:
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.
Results:
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).
Discussion:
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.
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