Evaluation of GeoHAI Implementation

GeoHAI Implementation in IP Workflow

Geographic Information Systems (GIS) and spatial analysis have become important tools in public health informatics but have rarely been applied to the hospital setting. In this study we apply these tools to address the challenge of Hospital Acquired Infections (HAIs) by building, implementing, and evaluating a new computer application which incorporates mapping and geographic data to assist hospital epidemiologists in identifying HAI clusters and assessing transmission risk. We expect that incorporation of geographic information into the workflow of hospital epidemiologists will have a profound effect on our understanding of disease transmission and HAI risk factors in the hospital setting, radically altering the workflow and speed of response of infection preventionists and improving their ability to prevent HAIs.

Study Overview

• Status: Active, not recruiting
• Conditions: Healthcare Associated Infection; Clostridioides Difficile Infection
• Intervention / Treatment: Other: GeoHAI

Detailed Description

Hospital Acquired Infections are common, affecting 3.2% of acute care hospital admissions. Recent reports have shown an improvement in overall HAI rates, primarily driven by improvements in surgical site (SSI) and catheter associated urinary tract infections (CAUTI). Transmissible infections, such as Clostridium difficile (CDI), have not shown the same decrease over time. This may be because prevention of CDI requires a comprehensive hospital-wide approach addressing environmental and patient-level risk factors. Geographic Information Systems (GIS) and spatial analysis techniques have become an important tool in public health informatics because they can integrate a vast number of data sources and explore associations and patterns in the data not visible using traditional biostatistical methods. Applications of GIS and spatial analysis are wide ranging but have largely been ignored in the hospital setting. The objective of this research is to develop a HAI assessment tool, which incorporates geographic data on the hospital and patient-level data from the electronic health record system, that is useful for hospital infection preventionists in better identifying clusters of HAI and assessing potential risk. We bring together a multidisciplinary team of clinical, operational, and academic investigators with expertise in GIS and spatial analysis, patient safety, public health informatics, usability assessment, and mixed- methods evaluation. As part of a larger study, this aim will seek to implement a GeoHAI tool that uses spatio-temporal Bayesian models to identify clusters of NHSN-defined hospital onset CDI and multidrug resistant organisms (MDRO) and predict potential high risk areas given hospital and patient risk factors. Unique to our approach is an evaluation strategy that focuses on the reduction of hospital acquired infection, but also seeks to understand how the tool and the information derived from the tool impacts patient safety practices in the hospital. We expect the implementation of this tool to radically change the workflow and speed of response of infection preventionists, greatly improving their ability to prevent HAI instead of reacting after they have occurred.

• Study Type: Interventional
• Enrollment (Estimated): 25
• Phase: Not Applicable

Contacts and Locations

Study Locations

• United States
  • Ohio
    • Columbus, Ohio, United States, 43210
      • The Ohio State University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes

Description

Inclusion Criteria:

• Infection preventionist or physician involved in infection prevention at participating health system

Exclusion Criteria:

• Not an infection preventionist nor a physician involved in infection prevention
• Does not work at the participating health system

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Health Services Research
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: GeoHAI Use; Participants will use the GeoHAI tool	Other: GeoHAI; A Geographic healthcare-associated infection (HAI) visualization and assessment tool (GeoHAI) which uses spatio-temporal Bayesian models to identify clusters of National Healthcare Safety Network (NHSN)-defined hospital onset Clostridium difficile (CDI) and multidrug resistant organisms (MDRO), and predict potential high risk areas given hospital and patient risk factors.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Change from Baseline Healthcare-Associated Infection (HAI) Rate	HAI rate at healthcare system level before intervention and after	Baseline and 3 months post-implementation

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Knowledge of tool	Knowledge questions to assess understanding of how to use the GeoHAI tool, assessed after participants are trained on how to use the tool	Immediately post-training
Change from baseline skill confidence	Self-reported level of confidence on investigating HAI clusters	Baseline, 1 month post-implementation
Usability score	System Usability Scale score, and impacts of the tool on work and workflow (interruptions, workarounds, issues/challenges)	Immediately post training, 1 month post-implementation
GeoHAI Use	Self-reported frequency of use of the GeoHAI tool	1 month post-implementation
Change in Healthcare-Associated Infection (HAI) Investigation Process	Change in how infection preventionists investigate Healthcare-Associated Infections (HAIs)	Baseline, 1 month post-implementation
Number of months healthcare system is below goal HAI rate	Monthly HAI rate at healthcare system level before intervention and after	Baseline, 3 months post-implementation
Change in feasibility score	Score on feasibility, acceptability, and appropriateness domains of validated Implementation Outcome scale (minimum score = 1, maximum score = 5, where higher scores indicate better feasibility)	Immediately post training, 1 month post-implementation
Change in time to HAI cluster identification	Time from when an HAI test was ordered for the first positive patient ultimately contained in an identified HAI cluster, to when that HAI cluster is identified.	Baseline, 3 months post-implementation

Collaborators and Investigators

• Sponsor: Ohio State University
• Collaborators: University of Florida; Agency for Healthcare Research and Quality (AHRQ)
• Investigators: Principal Investigator: Courtney Hebert, MD, Ohio State University

Study record dates

Study Major Dates

• Study Start (Actual): February 13, 2023
• Primary Completion (Actual): April 1, 2026
• Study Completion (Estimated): September 1, 2026

Study Registration Dates

• First Submitted: April 18, 2022
• First Submitted That Met QC Criteria: November 4, 2022
• First Posted (Actual): November 10, 2022

Study Record Updates

• Last Update Posted (Actual): May 5, 2026
• Last Update Submitted That Met QC Criteria: April 29, 2026
• Last Verified: April 1, 2026

More Information

Terms related to this study

• Keywords: Geographic information system
• Additional Relevant MeSH Terms: Pathologic Processes; Disease Attributes; Infections; Gram-Positive Bacterial Infections; Bacterial Infections; Bacterial Infections and Mycoses; Iatrogenic Disease; Pathological Conditions, Signs and Symptoms; Clostridium Infections; Cross Infection
• Other Study ID Numbers: R01HS027200 (U.S. AHRQ Grant/Contract)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No