Clinical trial NCT05612672: Evaluation of GeoHAI Implementation
Official title: GeoHAI Implementation in IP Workflow
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Clostridioides Difficile Infection
Healthcare Associated Infection
Keywords: Geographic information system
Lead sponsor: Ohio State University
Sponsor class: OTHER
Collaborators: name: Agency for Healthcare Research and Quality (AHRQ)
name: University of Florida
First posted: 2022-11-10
Enrollment: 25
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: NA
Intervention model: SINGLE_GROUP
Intervention model description: Participants will get the intervention and outcomes will be measured pre- and post-implementation
Primary purpose: HEALTH_SERVICES_RESEARCH

Brief 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.

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.

Eligibility criteria: 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

Primary outcomes:
measure: Change from Baseline Healthcare-Associated Infection (HAI) Rate
timeFrame: 6 months
Secondary outcomes:
measure: Knowledge of tool
timeFrame: Immediately post-training
measure: Change from baseline skill confidence at one year
timeFrame: 6 months
measure: Usability score
timeFrame: 6 months
measure: GeoHAI Use
timeFrame: 6 months
measure: Change in Healthcare-Associated Infection (HAI) Investigation Process
timeFrame: 6 months
measure: Number of months healthcare system is below goal HAI rate
timeFrame: 6 months
measure: Feasibility score
timeFrame: 6 months
measure: Time to HAI cluster identification
timeFrame: 6 months
Other outcomes:
Locations:
city: Columbus
country: United States
facility: The Ohio State University
state: Ohio
Study officials:
affiliation: Ohio State University
name: Courtney Hebert, MD
role: PRINCIPAL_INVESTIGATOR
References: