# Real-time Infection Prediction in Inpatient Postoperative Care

> **NIH VA I21** · VETERANS ADMIN PALO ALTO HEALTH CARE SYS · 2021 · —

## Abstract

Postoperative infections are the most common surgical complication, affecting upwards of 40 Veterans each day
and adding an estimated $25,000 to the total cost of care per infection. More than half of all postoperative SSIs
are diagnosed after the patient has been discharged from the hospital. Current decision support tools for
postoperative infections suffer from poor predictive accuracy. This makes it particularly hard to identify patients
at risk for developing an SSI after hospital discharge which complicates discharge planning. Accurately
identifying patients with a high risk of infection at discharge could help to improve discharge planning, identify
adequate timing for post-discharge follow up, and better target resource-expensive post-discharge surveillance.
Vital signs, such as temperature or pain, have consistently been shown to predict infections. They are routinely
collected during inpatient stays but have remained an untapped source of information for infection risk prediction
models. We hypothesize that we can improve the accuracy of existing infection risk prediction models
by including this real-time vital sign data. While planning our larger study, we have identified several feasibility
concerns that should be addressed before we embark on developing the tool and testing it in a clinical
environment. Thus, we are seeking HSR&D Pilot funding to finalize the protocol of our planned HSR&D
Investigator-Initiated Research study.
Our study aims are as follows:
1. Perform a developmental formative evaluation assessing the feasibility, acceptability, potential usefulness
 and initial design of a decision support tool for predicting infection risk at discharge.
2. Examine the completeness of inpatient vital sign data collected in the VA Corporate Data Warehouse.
We have planned our study using a person-centric design thinking framework. For aim 1, we will use qualitative
methods to analyze data from 20 structured observations at two VA facilities and 15 semi-structured interviews
across at least six VA facilities. These qualitative analyses will verify that our proposed tool can be incorporated
into clinical care at the VA, that clinical providers will accept the tool, and that it will be perceived as useful. We
will also begin to develop an initial prototype for the tool’s user interface and deployment within VA clinical care.
Aim 2 addresses a feasibility concern about building our model with potentially incomplete vital sign data. The
VA Corporate Data Warehouse (CDW) is the standard location for accessing inpatient vital signs collected across
the entire VA. Unfortunately, we have discovered that not all local vital sign data make it into the CDW and some
vital sign data are only captured in VISN-specific databases. This leads us to concerns about the impact of
missing data when using national CDW data to refine existing models with vital sign data. For aim 2, we will use
quantitative methods to analyze an existing database of all pat...

## Key facts

- **NIH application ID:** 10187074
- **Project number:** 1I21HX003217-01A1
- **Recipient organization:** VETERANS ADMIN PALO ALTO HEALTH CARE SYS
- **Principal Investigator:** Laura A Graham
- **Activity code:** I21 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2021
- **Award amount:** —
- **Award type:** 1
- **Project period:** 2021-08-01 → 2023-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10187074

## Citation

> US National Institutes of Health, RePORTER application 10187074, Real-time Infection Prediction in Inpatient Postoperative Care (1I21HX003217-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10187074. Licensed CC0.

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