# Homecare-CONCERN: Building risk models for preventable hospitalizations and emergency department visits in homecare

> **NIH AHRQ R01** · VISITING NURSE SERVICE OF NEW YORK · 2022 · $367,985

## Abstract

Project Summary/Abstract
Every year, more than 11,000 homecare agencies across the United States provide care to more than 5 million
older adults. Currently, about one in three homecare patients are hospitalized or visit an emergency
department (ED) during the 30-60 day homecare episode. Up to 40% of these events are preventable with
appropriate and timely care. In our pilot work, we developed a risk prediction model (called Homecare-
CONCERN) that accurately identified patients at risk for hospital admission and ED visits solely from homecare
clinical notes using NLP. This study brings together an interdisciplinary team of experts in homecare, data
science, nursing and risk model development to explore whether cutting-edge data science approaches can
improve timely identification of patients at risk in homecare. Our specific aims are to:
1. Further develop and validate a preventable hospitalization or ED visit risk prediction model (Homecare-
 CONCERN). We will apply traditional (time varying Cox regression) and cutting-edge time-sensitive
 analytical methods (Deep Survival Analysis and Long-Short Term Memory Neural Network) for risk model
development.
2. Prepare Homecare-CONCERN for clinical trial via pilot testing. We will apply user centered design to
 develop Homecare-CONCERN clinical decision support tool and pilot test the tool for clinical validity and
acceptability.
3. Inform the future implementation of Homecare-CONCERN clinical decision support tool in the homecare
 setting. We will examine if all risk elements can be mapped to a data standard (Fast Healthcare
 Interoperability Resources - FHIR) and conduct interviews with key informants across the US about current
 readiness, barriers and facilitators, and implementation strategies for adopting such tools in homecare
setting.
This proposal addresses the AHRQ program announcement (PA-18-795) to harness data to improve
healthcare quality and patient outcomes. The study will build a first-of-a-kind clinical decision support
system to trigger timely and personalized alerts about concerning patient trends that activate appropriate and
timely care to prevent avoidable hospitalizations and ED visits from homecare.

## Key facts

- **NIH application ID:** 10440317
- **Project number:** 5R01HS027742-03
- **Recipient organization:** VISITING NURSE SERVICE OF NEW YORK
- **Principal Investigator:** Maxim Topaz
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2022
- **Award amount:** $367,985
- **Award type:** 5
- **Project period:** 2020-09-30 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10440317

## Citation

> US National Institutes of Health, RePORTER application 10440317, Homecare-CONCERN: Building risk models for preventable hospitalizations and emergency department visits in homecare (5R01HS027742-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10440317. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*