Evaluation of GeoHAI Implementation
- Conditions
- Clostridioides Difficile InfectionHealthcare Associated Infection
- Interventions
- Other: GeoHAI
- Registration Number
- NCT05612672
- Lead Sponsor
- Ohio State University
- 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.
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- All
- Target Recruitment
- 25
- Infection preventionist or physician involved in infection prevention at participating health system
- Not an infection preventionist nor a physician involved in infection prevention
- Does not work at the participating health system
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- SINGLE_GROUP
- Arm && Interventions
Group Intervention Description GeoHAI Use GeoHAI Participants will use the GeoHAI tool
- Primary Outcome Measures
Name Time Method Change from Baseline Healthcare-Associated Infection (HAI) Rate 6 months HAI rate at healthcare system level before intervention and after
- Secondary Outcome Measures
Name Time Method Feasibility score 6 months Score on feasibility, acceptability, and appropriateness domains of validated Implementation Outcome scale (minimum score = 1, maximum score = 5, where higher scores indicate better feasibility)
Knowledge of tool Immediately post-training Knowledge questions to assess understanding of how to use the GeoHAI tool, assessed after participants are trained on how to use the tool
Change from baseline skill confidence at one year 6 months Self-reported level of confidence on investigating HAI clusters
Usability score 6 months System Usability Scale score, and impacts of the tool on work and workflow (interruptions, workarounds, issues/challenges)
GeoHAI Use 6 months Self-reported frequency of use of the GeoHAI tool
Change in Healthcare-Associated Infection (HAI) Investigation Process 6 months Change in how infection preventionists investigate Healthcare-Associated Infections (HAIs)
Number of months healthcare system is below goal HAI rate 6 months Monthly HAI rate at healthcare system level before intervention and after
Time to HAI cluster identification 6 months 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.
Trial Locations
- Locations (1)
The Ohio State University
🇺🇸Columbus, Ohio, United States