# Segmenting High-Need, High-Cost Veterans into Potentially Actionable Subgroups

> **NIH VA I21** · PHILADELPHIA VA MEDICAL CENTER · 2020 · —

## Abstract

SUMMARY ABSTRACT
Background
One compelling strategy for improving patient outcomes while reducing healthcare costs is to focus on
veterans that account for the vast majority of poor outcomes, health utilization, and VA spending (i.e., HNHC
veterans). However, successfully managing HNHC veterans is challenging because these patients are
heterogeneous, each requiring a different management strategy. Veterans who intensely use services are of
particular interest, especially those with chronic conditions since 20% of them will experience a hospitalization
and readmission within 30 days after discharge. Hospitalization, emergency room visits, and re-hospitalization
rates are even higher for socioeconomically disadvantaged populations, minorities, and veterans with disability.
However, much of this utilization is preventable and could be averted with better longitudinal care. The VA has
increased its efforts in identifying HNHC veterans through development of the Care Assessment Needs (CAN)
score and care management programs, but without greater detail enabling tailoring of clinical programs HNHC
veteran subgroups, linking these scores to strategies to improve care is difficult.
Objectives
The objectives of this study are to: (1) apply statistical and machine learning clustering methods to classify
HNHC veterans into clinically actionable subgroups based on detailed clinical information extending beyond
diagnosis codes, (2) compare the HNHC subgroups to veterans with similar diagnoses who were not HNHC,
and (3) describe the characteristics of the HNHC subgroups (i.e., CAN Scores) and changes over time.
Methods
To achieve these objectives, we will analyze patient-level data from the National Patient Care Database (2013-
2015) using the VA Informatics and Computing Infrastructure (VINCI) platform to develop models that cluster
HNHC veterans into subgroups based on demographic, clinical, and social characteristics. We will utilize a
combination of statistical (latent class analysis) and machine learning clustering (e.g. k-means clustering)
algorithms. Our definition of a HNHC veteran will comprise the highest quartiles of predicted risk of death or
acute hospitalization (i.e., CAN score > 75). Subgroups and characteristics to compare HNHC and non-HNHC
veterans will be constructed using 3 approaches: 1) cluster veterans who are HNHC in 2014, 2) cluster
veterans who are HNHC in 2014 and 2015 (persistently HNHC), and 3) cluster all non-HNHC veterans into
subgroups.
Anticipated Impacts on Veterans Health Care
This project aims to identify clinically actionable subgroups of high-need, high-cost (HNHC) veterans using
data-driven techniques rather than expert opinion. We hypothesize that distinct clinical characteristics will
define subgroups of HNHC veterans and that these subgroups of veterans likely require different management
strategies. Thus, the categorization of HNHC veterans into discrete types of patients will support nurse care
managers and primary ca...

## Key facts

- **NIH application ID:** 10176376
- **Project number:** 5I21HX002564-02
- **Recipient organization:** PHILADELPHIA VA MEDICAL CENTER
- **Principal Investigator:** Amol S Navathe
- **Activity code:** I21 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2020
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2018-10-01 → 2020-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10176376

## Citation

> US National Institutes of Health, RePORTER application 10176376, Segmenting High-Need, High-Cost Veterans into Potentially Actionable Subgroups (5I21HX002564-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10176376. Licensed CC0.

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