# Advancing Knowledge Discovery for Postoperative Pain Management

> **NIH NIH R01** · STANFORD UNIVERSITY · 2020 · $662,684

## Abstract

ABSTRACT
Surgery is common and appropriate postoperative pain management is critical as poor management can impair
recovery and lead to adverse events, including prolonged opioid use and transition to chronic pain. Literature
suggests significant disparities exist with regard to pain management and its quality-of-life impacts, particularly
among vulnerable populations (e.g. depressed, obese and diabetics). However, there lacks risk stratification
tools to identify individuals at high risk for these disparate pain outcomes. Although pain scores are routinely
collected in electronic health records (EHRs), shared algorithms to utilize them for care improvement are limited.
To advance the efficient and effective use of the abundant amount of electronic data now available, a common
data model (CDM) is necessary: standardized structures, terminologies, and rules to represent EHR data. Using
a CMD for postoperative pain research would facilitate timely evidence generation across multiple populations
and settings, which can provide critical evidence to stakeholders and move the field away from pain treatment
for the ‘average’ patient to pain treatment for an individual. In this grant, we propose an innovative approach to
advance the systematic analysis of postoperative pain across populations. Our approach will leverage the
Observational Medical Outcomes Partnership (OMOP) CDM to develop tools that use standardize data formats
and naming conventions; OMOP has over 140 collaborating sites gloablly. We will further utilize analytical tools
developed by Observational Health Data Sciences and Informatics (OHDSI) on this CDM to facilitate disseminate
across the research community. Our approach will develop scalable, open source risk stratification tools for
adverse pain outcomes across diverse populations. We will accomplish this work in three aims. First, we will
develop clinical phenotypes to identify and extract key discriminating features necessary to assess postoperative
pain using EHRs. Next, we will develop pain risk stratification models using machine learning, including deep
learning, methods and tools based on phenotypes developed in Aim 1. Finally, we will validate our models
externally at the VA and disseminate our work through open source libraries and public websites. This project
will deliver validated risk-stratification tools derived from real world evidence to identify patients at high risk for
adverse pain outcomes following surgery, which can potentially reduce prescribed opioids circulating in the
community– a key to curbing the opioid epidemic.

## Key facts

- **NIH application ID:** 10019592
- **Project number:** 5R01LM013362-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Tina Hernandez-Boussard
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $662,684
- **Award type:** 5
- **Project period:** 2019-09-17 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10019592

## Citation

> US National Institutes of Health, RePORTER application 10019592, Advancing Knowledge Discovery for Postoperative Pain Management (5R01LM013362-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10019592. Licensed CC0.

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