# Leveraging electronic health records to identify risky alcohol use prior to surgery

> **NIH NIH R21** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2020 · $199,125

## Abstract

Project Summary/Abstract
Patients who consume more than two drinks a day prior to elective surgery are at increased risk of
experiencing a myriad of surgical complications, readmissions, and prolonged hospital stays. Fortunately,
short-term pre-operative abstinence from alcohol mitigates many surgical risks, and carefully timed
interventions can prevent complications and alcohol withdrawal syndrome. However, implementation of pre-
operative alcohol interventions requires accurate identification of patients with risky alcohol use at least four
weeks prior to surgery. Pre-operative clinics frequently fail to screen for alcohol use or do so too close to the
surgery date to allow time for intervention. Electronic health records (EHRs) offer an unprecedented amount of
accessible clinical data that can be leveraged to identify risky alcohol use early in the surgical episode of care.
Innovative methods are needed to identify data elements and create algorithms to capture risky alcohol use
from structured and unstructured EHR data. Natural language processing (NLP) and other machine learning
(ML)-based approaches are best suited to extract and analyze alcohol-related clinical narratives, and to
synthesize heterogeneous alcohol-related data through computer-assisted methods. The proposed study will
leverage EHR data to identify and characterize risky alcohol use among surgical patients to identify cohorts
who could benefit from pre-operative alcohol intervention. The study aims are to: 1) develop an electronic,
automated computable phenotype to classify risky alcohol use prior to surgery using NLP and ML; 2) validate
the algorithm through prospective data collection; and 3) longitudinally evaluate the association between risky
alcohol use phenotypes and adverse surgical outcomes including complications and hospital readmissions.
Innovative applications of NLP and ML will support evaluation of unstructured EHR data (e.g. clinical notes)
and will enable integration of heterogeneous alcohol use data to create the computable phenotype. The aims
will be achieved through collaboration of experts in key clinical domains and advanced methodologies. This
study will create and validate the first alcohol-specific phenotype-based algorithm for surgical patients, which
will support future clinical applications and research into alcohol-related surgical interventions and health
outcomes. Study outcomes are expected to have immediate value for identifying cohorts for future
implementation research and lead to a new clinical tool for surgical clinics.

## Key facts

- **NIH application ID:** 9953526
- **Project number:** 1R21AA028315-01
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Anne Christie Fernandez
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $199,125
- **Award type:** 1
- **Project period:** 2020-07-10 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9953526

## Citation

> US National Institutes of Health, RePORTER application 9953526, Leveraging electronic health records to identify risky alcohol use prior to surgery (1R21AA028315-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9953526. Licensed CC0.

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