# Collaborative Research: Statistical Algorithms for Anomaly Detection and Patterns Recognition in Patient Care and Safety Event Reports

> **NIH NIH R01** · NORTH CAROLINA STATE UNIVERSITY RALEIGH · 2020 · $74,963

## Abstract

Project Summary
Medical errors have been shown to be the third leading cause of death in the United States. The Institute of
Medicine and several state legislatures have recommended the use of patient safety event reporting systems
(PSRS) to better understand and improve safety hazards. A patient safety event (PSE) report generally consists
of both structured and unstructured data elements. Structured data are pre-defined, fixed fields that solicit
specific information about the event. The unstructured data fields generally include a free text field where the
reporter can enter a text description of the event. The text descriptions are often a rich data source in that the
reporter is not constrained to limited categories or selection options and is able to freely describe the details of
the event.
The goal of this project is to develop novel statistical methods to analyze unstructured text like patient safety
event reports arising in healthcare, which can lead to significant improvements to patient safety and enable
timely intervention strategies. We address three problems: (a) Building realistic and meaningful baseline models
for near misses, and detecting systematic deterioration of adverse outcomes relative to such baselines; (b)
Understanding critical factors that lead to near misses & quantifying severity of outcomes; and (c) Identifying
document groups of interest. We will use novel statistical approaches that combine Natural Language
Processing with Statistical Process Monitoring, Statistical Networks Analysis, and Spatio-temporal Modeling to
build a generalizable toolbox that can address these issues in healthcare. We will also release open source
software via R packages & GitHub, which will enable healthcare staff and researchers to execute our methods
on their datasets.
The COVID-19 pandemic has resulted in increased patient volumes and increased patient acuity, leading to an
excessive burden on many healthcare facilities across the United States. This greatly increases the risk of patient
safety consequences arising from malfunctioning medical equipment or adverse reaction to medication. To
ensure patient safety and the highest quality of healthcare during this crisis, we need a rapid response system to
model and analyze COVID-specific safety issues at scale, and quickly disseminate the results to healthcare
facilities, so that these risks can be mitigated at the point of care. In this supplement, we propose to do this by
(a) mining public databases and EHRs to identify devices/medication being used for treating COVID and (b)
applying our methods (based on NLP, SPC, and SPM) to understand risks associated with these items. This
information will be disseminated nationally to all healthcare facilities so that it can be integrated into the EHR
at the point of care to alert clinicians.

## Key facts

- **NIH application ID:** 10254593
- **Project number:** 3R01LM013309-02S2
- **Recipient organization:** NORTH CAROLINA STATE UNIVERSITY RALEIGH
- **Principal Investigator:** Allan Fong
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $74,963
- **Award type:** 3
- **Project period:** 2020-09-16 → 2021-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10254593

## Citation

> US National Institutes of Health, RePORTER application 10254593, Collaborative Research: Statistical Algorithms for Anomaly Detection and Patterns Recognition in Patient Care and Safety Event Reports (3R01LM013309-02S2). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10254593. Licensed CC0.

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