# Automated Surveillance and Intervention among Patients with Liver Cirrhosis

> **NIH VA I01** · VETERANS HEALTH ADMINISTRATION · 2020 · —

## Abstract

The number of patients with cirrhosis and advanced liver disease has been growing in the VA system and
general population of the US. As of 2008, the prevalence of chronic liver disease in the US reached 15%.
Complications of cirrhosis frequently require hospital admission, and each year, cirrhosis is responsible for
>150,000 hospitalizations at a cost of approximately $4 billion. Among patients who survive the initial
hospitalization, nearly half are rehospitalized within 1 year. The VA is facing an increasing burden of chronic
liver disease due to substance use disorders, chronic viral hepatitis, and increasing numbers of patients with
non-alcoholic steatohepatitis, and is the largest single provider of hepatitis C (HCV) care in the US. It was
recently estimated that cirrhosis prevalence among VHA HCV patients will exceed 50% over the next
decade.45 Alcohol use accelerates fibrosis progression, and current active high risk alcohol use among
consecutive HCV patients in some VA clinics has been shown to be 25-34%. The use of clinical decision
support (CDSS) in clinical dashboards has great potential for facilitating more robust risk stratification and
tailored clinical care interventions as well as providing a platform for effective use of NLP technologies in
clinical care, but has been disseminated into general clinical practice slowly because of the sophisticated
underlying data requirements and a lack of focus on clinical workflow and efficiency optimization.
The overall objective of this project is to develop the informatics infrastructure and tools to facilitate improved
evidence based quality care delivery to patients with cirrhosis that will impact readmission and mortality rates.
More specifically, we will 1) develop and validate near real-time natural language processing (NLP) tools in
order to extract information that is relevant for case finding and risk factor modification among these patients,
2) develop and validate a robust family of logistic regression prediction models for readmission and mortality
following hospitalization for use in the identification of high risk patients, 3) development of a clinical dashboard
with imbedded clinical decision support and patient data visualization tools to support clinical care delivery, and
4) conduct a pre-post clinical pilot to evaluate the efficacy and adoption of the dashboard when used.
This proposal will analyze national retrospective cohort data among adult hospitalized patients for Specific
Aims 1 and 2, beginning with all hospitalized patients and identifying the cohort of patients with cirrhosis in
order to develop predictive models for readmission and mortality. All variables will be extracted from structured
data in the CDW, Medical SAS, and Medicare files, with real time NLP used to extract risk factors from
unstructured data. Augmented case finding will be used in Aim 1 to detect additional cirrhotic patients, and the
discussed risk factors, social history factors, and modifiable...

## Key facts

- **NIH application ID:** 10038747
- **Project number:** 5I01HX001284-04
- **Recipient organization:** VETERANS HEALTH ADMINISTRATION
- **Principal Investigator:** Samuel Benjamin Ho
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2020
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2014-07-01 → 2019-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10038747

## Citation

> US National Institutes of Health, RePORTER application 10038747, Automated Surveillance and Intervention among Patients with Liver Cirrhosis (5I01HX001284-04). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10038747. Licensed CC0.

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