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...