# Enhance Arthroplasty Research through Electronic Health Records and Nlp-Enabled Informatics

> **NIH NIH R01** · MAYO CLINIC ROCHESTER · 2020 · $592,632

## Abstract

ABSTRACT
Total joint arthroplasty (TJA) is the most common and fastest growing surgical procedure in the
nation. Despite the high procedure volume, the evidence base for TJA procedures and
associated interventions are limited. This is mainly due to lack of high quality data sources and
the logistical difficulties associated with manually extracting TJA information from the
unstructured text of the Electronic Health Records (EHR). Meanwhile, the rapid adoption of EHR
and the advances in health information technology offer the potential to transform unstructured
EHR notes into structured, codified format that can then be analyzed and shared with local and
national arthroplasty registries and other agencies.
We therefore propose to leverage unique data resources and natural language processing
(NLP) technologies to build an informatics infrastructure for automated EHR data extraction and
analysis. We will (1) develop a high performance, externally validated and user centric NLP-
enabled algorithm for extraction of complex TJA-specific data elements from the structured and
unstructured text of the EHR, (2) validate the algorithm externally in multiple EHR platforms and
hospital settings, and (3) conduct a demonstration project focused on prediction of prosthetic
joint infections using data elements collected by the NLP-enabled algorithm. Our overarching
goal is to develop valid, open source and portable NLP-enabled data collection and risk
prediction tools and disseminate them widely to hospitals participating in regional and national
TJA registries.
This research is significant as it leverages strong data resources and expertise to tackle the
pressing need for high quality data and accurate prediction models in TJA. Automated data
collection and processing capabilities will lead to an upsurge in secondary use of EHR to
advance scientific knowledge on TJA risk factors, healthcare quality and patient outcomes.
Accurate prediction of high risk patients for prosthetic joint infections will guide prevention and
treatment decisions resulting in significant health benefits to TJA patients. The research is
innovative because TJA-specific bioinformatics technology will shift TJA research from current
under-powered, single-center studies to large, multi-center registry-based observational studies
and clinical trials. Our deliverables have the potential to exert a sustained downstream effect on
future TJA research, practice and policy.

## Key facts

- **NIH application ID:** 9880374
- **Project number:** 5R01AR073147-03
- **Recipient organization:** MAYO CLINIC ROCHESTER
- **Principal Investigator:** Hilal Maradit Kremers
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $592,632
- **Award type:** 5
- **Project period:** 2018-03-01 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9880374

## Citation

> US National Institutes of Health, RePORTER application 9880374, Enhance Arthroplasty Research through Electronic Health Records and Nlp-Enabled Informatics (5R01AR073147-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9880374. Licensed CC0.

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