# Improving the Diagnosis of Liver Disease in Primary Care Patients with Abnormal Liver Function

> **NIH NIH K23** · MEDICAL UNIVERSITY OF SOUTH CAROLINA · 2021 · $172,490

## Abstract

Abstract
Improving the Diagnosis of Liver Disease in Primary Care Patients with Abnormal Liver Function Tests
Through Predictive Modeling
Reducing diagnostic error has been identified by the Institute of Medicine as a top national priority. Diagnostic
errors pervade all of healthcare, with the average individual experiencing one major error during their lifetime.
Therefore, improving the diagnostic process and reducing diagnostic error is not only highly appropriate for all
patients, but will play a crucial role in optimizing the quality and value of healthcare delivery in the United
States.1
Liver disease, with complications including acute liver failure, cirrhosis, and liver cancer ranks as a leading
cause of death in America and over recent years has had a significant climb in age-adjusted mortality, while
death rates from heart disease and cancer have fallen.2 Despite the increasing preventability of liver-related
conditions through early recognition and treatment, the toll of chronic and end stage liver disease continues to
rise.3
The traditional diagnostic process, a synthesis of information gathered from history, physical exam, and
laboratory testing, performs poorly in the detection of early liver disease.4,5 Instead, clinicians rely more heavily
on laboratory studies, and liver function tests (LFTs) in particular.6 Abnormal LFTs are among the most
frequently encountered findings in medicine.7,8 Currently, primary care clinicians currently lack the ability to
consistently identify liver-related disease from these abnormalities.9-12
Preliminary data in primary care emphasize the immense scope of the problem; in studies from Europe, LFTs
have been found elsewhere to be abnormal in nearly 1 in 5 people.13,14 In our preliminary studies, we have up
to 40% of patients seen in an academic primary care clinic possessed at least one abnormal LFT. Further,
these abnormal liver tests are inappropriately or inadequately followed-up. These data and our own
experience indicate that primary care physicians (PCPs) lack the resources to reliably identify and accurately
diagnose liver-related diseases amongst these many abnormal LFTs.
In this proposal, the candidate and his mentorship team seek to harness inter-professional teamwork and
information technology to reduce diagnostic error. They will identify clinical and demographic variables of
patients with abnormal LFTs associated with specific liver-related diagnoses in primary care (Aim 1).
Additionally, they will develop and validate a predictive model to identify patients with abnormal LFTs at risk for
liver-related diagnoses (Aim 2). Lastly, they will create a decision support tool application to aid PCPs
confronted with abnormal LFTs to promptly and accurately diagnose liver disease (Aim 3).

## Key facts

- **NIH application ID:** 10163178
- **Project number:** 5K23DK118200-04
- **Recipient organization:** MEDICAL UNIVERSITY OF SOUTH CAROLINA
- **Principal Investigator:** Andrew David Schreiner
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $172,490
- **Award type:** 5
- **Project period:** 2018-07-17 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10163178

## Citation

> US National Institutes of Health, RePORTER application 10163178, Improving the Diagnosis of Liver Disease in Primary Care Patients with Abnormal Liver Function (5K23DK118200-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10163178. Licensed CC0.

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