Evaluation of GeoHAI Implementation

Clostridioides Difficile Infection Clinical Trial

Official title:

GeoHAI Implementation in IP Workflow

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05612672
• Other study ID #: R01HS027200
• Secondary ID: R01HS027200
• Status: Recruiting
• Phase: N/A
• Start date: February 13, 2023
• Est. completion date: September 30, 2024

Study information

• Verified date: April 2024
• Source: Ohio State University
• Contact: Ohio State University Office of Responsible Research Practices
• Phone: 1-800-678-6251
• Email: biomedicalirbinfo[@]osu.edu
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 25
• Est. completion date: September 30, 2024
• Est. primary completion date: September 30, 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Clostridioides Difficile Infection
• Clostridium Infections
• Communicable Diseases
• Cross Infection
• Healthcare Associated Infection
• Infections

Intervention

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.

Locations

Country	Name	City	State
United States	The Ohio State University	Columbus	Ohio

Sponsors (2)

Lead Sponsor	Collaborator
Ohio State University	Agency for Healthcare Research and Quality (AHRQ)

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Change from Baseline Healthcare-Associated Infection (HAI) Rate	HAI rate at healthcare system level before intervention and after	1 year
Secondary	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
Secondary	Change from baseline skill confidence at one year	Self-reported level of confidence on investigating HAI clusters	1 year
Secondary	Usability score	System Usability Scale score, and impacts of the tool on work and workflow (interruptions, workarounds, issues/challenges)	1 year
Secondary	GeoHAI Use	Self-reported frequency of use of the GeoHAI tool	1 year
Secondary	Change in Healthcare-Associated Infection (HAI) Investigation Process	Change in how infection preventionists investigate Healthcare-Associated Infections (HAIs)	1 year
Secondary	Number of months healthcare system is below goal HAI rate	Monthly HAI rate at healthcare system level before intervention and after	1 year
Secondary	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)	1 year
Secondary	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.	1 year