Clostridioides Difficile InfectionNCT05612672
Evaluation of GeoHAI Implementation
A study testing GeoHAI in people with Clostridioides Difficile Infection and Healthcare Associated Infection, run by Ohio State University at 1 site in United States.
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… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Clostridioides Difficile Infection, Healthcare Associated Infection
- What is being tested?
- GeoHAI
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 25 participants
- Where
- 1 siteUnited States
- Study dates
- 13 Feb 2023to 1 Sep 2026
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Infection preventionist or physician involved in infection prevention at participating health system
Who cannot join
- Not an infection preventionist nor a physician involved in infection prevention
- Does not work at the participating health system
Trial locations and contacts
1 site in 1 country.
About this study
Official title: 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.
Read the 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.
- Being tested
- GeoHAI
- Keywords
- Geographic information system
- Medical subject headings
- Clostridium Infections, Cross Infection
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
GeoHAIOther
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.
| Study group | Role | What they receive |
|---|---|---|
| GeoHAI Use | Experimental | Participants will use the GeoHAI tool Receives: Other: GeoHAI |
What is being measured
The primary outcome is the main question the study is designed to answer.
Change from Baseline Healthcare-Associated Infection (HAI) Rate
HAI rate at healthcare system level before intervention and after
Measured: Baseline and 3 months post-implementation
Secondary outcomes (8)
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
Measured: Immediately post-training
Change from baseline skill confidence
Self-reported level of confidence on investigating HAI clusters
Measured: Baseline, 1 month post-implementation
Usability score
System Usability Scale score, and impacts of the tool on work and workflow (interruptions, workarounds, issues/challenges)
Measured: Immediately post training, 1 month post-implementation
GeoHAI Use
Self-reported frequency of use of the GeoHAI tool
Measured: 1 month post-implementation
Change in Healthcare-Associated Infection (HAI) Investigation Process
Change in how infection preventionists investigate Healthcare-Associated Infections (HAIs)
Measured: 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
Measured: 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)
Measured: 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.
Measured: Baseline, 3 months post-implementation
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Nov 2022
- Study start13 Feb 2023
- Primary completion1 Apr 2026
- Study completion1 Sep 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Clostridioides Difficile InfectionStudies of the same condition, open and under way.
- More trials run by Ohio State UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Not applicable
- Intervention model
- Single group
- Model description
- Participants will get the intervention and outcomes will be measured pre- and post-implementation
- Masking
- None (open label)
- Enrolment
- 25 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Ohio State University
- Sponsor type
- University, hospital or other
- Collaborators
- Agency for Healthcare Research and Quality (AHRQ), University of Florida
- Responsible party
- Principal investigator, Courtney Hebert, Ohio State University
- Investigators
- Courtney Hebert, MD, Principal investigator, Ohio State University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05612672
- Sponsor's protocol ID
- R01HS027200
- Other IDs
- R01HS027200
- Status last verified
- Apr 2026
- First submitted
- 18 Apr 2022
- Last updated
- 5 May 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 5 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- GeoHAI Implementation in IP Workflow
- ClinicalTrials.gov ID
- NCT05612672
- Acronym / sponsor ID
- R01HS027200
- Keywords
- Geographic information system
- Study type
- Interventional
- Recruitment status
- Active, not recruiting
- Enrolment
- 25 participants (estimated)
- Lead sponsor
- Ohio State UniversityUniversity, hospital or other
- Collaborators
- Agency for Healthcare Research and Quality (AHRQ), University of Florida
- Study start
- 13 Feb 2023 (actual)
- Primary completion
- 1 Apr 2026 (actual)
- Study completion
- 1 Sep 2026 (estimated)
- First posted
- 10 Nov 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Health services research
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
- Model description
- Participants will get the intervention and outcomes will be measured pre- and post-implementation
| Arm | Type | Receives |
|---|---|---|
| GeoHAI Use | Experimental | Other: GeoHAIParticipants will use the GeoHAI tool |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- Infection preventionist or physician involved in infection prevention at participating health system
Key exclusion criteria (2 listed)
- Not an infection preventionist nor a physician involved in infection prevention
- Does not work at the participating health system
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| GeoHAIA 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. | Other | GeoHAI Use |
Primary outcome
Change from Baseline Healthcare-Associated Infection (HAI) Rate
Time frame: Baseline and 3 months post-implementation
HAI rate at healthcare system level before intervention and after
8 secondary outcomes
Knowledge of tool
Time frame: Immediately post-training
Change from baseline skill confidence
Time frame: Baseline, 1 month post-implementation
Usability score
Time frame: Immediately post training, 1 month post-implementation
GeoHAI Use
Time frame: 1 month post-implementation
Change in Healthcare-Associated Infection (HAI) Investigation Process
Time frame: Baseline, 1 month post-implementation
Number of months healthcare system is below goal HAI rate
Time frame: Baseline, 3 months post-implementation
Change in feasibility score
Time frame: Immediately post training, 1 month post-implementation
Change in time to HAI cluster identification
Time frame: Baseline, 3 months post-implementation
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 May 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Courtney Hebert, MD principal investigator
Ohio State University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing GeoHAI in people with Clostridioides Difficile Infection and Healthcare Associated Infection, run by Ohio State University at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 5 May 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Change from Baseline Healthcare-Associated Infection (HAI) Rate (time frame: Baseline and 3 months post-implementation). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 5 May 2026.