Related Experiment Video
Updated: Sep 17, 2025
A Modified Sonographic Algorithm for Image Acquisition in Life-Threatening Emergencies in the Critically Ill Newborn
Published on: April 7, 2023
Network Analysis to Define Pediatric Acute Care Regions in Wisconsin
Kenneth A Michelson1, Katherine E Remick2, Emily M Bucholz3
1Division of Emergency Medicine, Ann & Robert Lurie Children's Hospital, Chicago, Illinois, USA.
Insights
New pediatric regions (PERRs) were developed using network analysis of Wisconsin child healthcare utilization. These PERRs better fit pediatric patient referral patterns than adult regions, suggesting improved pediatric healthcare planning.
Area of Science:
- Health Services Research
- Network Analysis in Healthcare
- Pediatric Healthcare Systems
Background:
- Defining optimal geographic regions for healthcare delivery is crucial for resource allocation and outcome assessment.
- Existing healthcare regions are often based on adult utilization patterns, potentially not reflecting pediatric needs.
- A data-driven approach is needed to delineate pediatric-specific service areas.
Purpose of the Study:
- To pilot a novel system for deriving pediatric healthcare regions.
- To compare the fit of these derived pediatric regions with existing adult-based regions using pediatric utilization data.
- To evaluate the effectiveness of network analysis in defining pediatric emergency service areas (PESAs) and pediatric emergency referral regions (PERRs).
Main Methods:
- Cross-sectional study of acute care encounters for children under 16 in Wisconsin (2021-2022) using Healthcare Cost and Utilization Project (HCUP) data.
- Network analysis, specifically community detection, was applied to ZIP code-level patient-hospital connections to form PESAs.
- A second community detection iteration on inter-PESA referrals defined PERRs, with modularity used as the primary fit measure.
Main Results:
- Analysis of 587,886 encounters resulted in 24 PESAs and 4 PERRs.
- PERRs demonstrated superior fit for interhospital pediatric referral patterns (modularity 0.53) compared to hospital referral regions (HRRs) (0.46).
- PERRs were significantly larger, encompassed more children, and included more hospitals than HRRs and other benchmark regions.
Conclusions:
- A network analysis approach successfully derived pediatric-specific acute care regions (PERRs) with strong alignment to pediatric utilization data in Wisconsin.
- These PERRs offer a better fit for pediatric referral patterns than traditional adult-based regions.
- Further validation across the United States is recommended to enable robust comparisons of costs and outcomes between pediatric regions.
Objective:
To pilot a system for deriving borders of pediatric regions, and to compare these to adult markets based on fit with pediatric utilization data.
Study Setting And Design:
In this cross-sectional study, we studied all acute care encounters (emergency department visits and hospitalizations) for children less than 16 years old in Wisconsin 2021-2022.
Data Sources And Analytic Sample:
We used the Healthcare Cost and Utilization Project State Emergency Department and Inpatient Databases. We first counted how many patients from each ZIP code visited each hospital and mapped ZIP-hospital connections. Using a network analysis technique called community detection that clustered hospitals by their common connections, we grouped ZIP codes to form pediatric emergency service areas (PESAs). We counted patient referrals within and between PESAs and repeated the community detection procedure, resulting in pediatric emergency referral regions (PERRs). The primary outcome was modularity, a common network fit measure ranging from -1 to 1 (1 represents perfect clustering). We also compared demographics and network quality measures between PERRs, hospital referral regions (HRRs), core-based statistical areas, and Pittsburgh Trauma Atlas regions.
Principal Findings:
We analyzed 587,886 encounters, from which ZIP codes grouped into 24 PESAs. Based on referral patterns, there were 4 PERRs. PERRs had modestly higher modularity for interhospital referral patterns than all other systems (0.53, 95% confidence interval [CI] 0.52, 0.54 compared to 0.46, 95% CI 0.46, 0.47 for HRRs). PERRs were larger (median 11,361 mile2 vs. 3957 for HRRs), contained more children (median 265,222 vs. 49,667 for HRRs), and contained more hospitals (median 35 vs. 7 for HRRs) than all other systems.
Conclusions:
Using Wisconsin HCUP data, we derived pediatric acute care regions with a strong fit for pediatric utilization data. Future work should test this approach across the whole US, which would allow between-region cost and outcomes comparison.
More Related Videos
Related Concept Videos
01:30Specialized Care Centers and Settings-II
Rural health centers are specialized care facilities in remote locations with very few medical personnel. The primary care providers who run the centers are mostly Registered Nurse Practitioners. Here, emergency treatment is provided to critically ill or injured patients before they are transferred to the closest hospital. Fortunately, due to advancement in technology, many rural healthcare facilities and professionals have easy access to diagnostic and treatment...
01:30Specialized Care Centers and Settings-I
Daycare centers
They provide several functions. Some facilities care for healthy newborns and children whose parents work, while others are medically focused and care for...
01:20Acute Kidney Injury V: Interprofessional Care
01:21Manipulation and Analysis
01:27Nursing Clinical Information System
A Nursing Clinical Information System (NCIS) is a specialized type of healthcare information system tailored to meet the unique needs of nursing practice. It incorporates the principles of nursing informatics to streamline information management and improve the quality of care delivery.
Critical attributes of NCIS include: