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impact of quality improvement programs , and predict future trends ,” according to Fitzpatrick , et al . ( 2020 ) who add that “ AI offers huge potential in infection prevention and control .”
As Scardoni , et al . ( 2020 ) remind us , “ Surveillance of HAIs is the foundation for organizing , implementing , and maintaining effective infection prevention and control programs . Objectives of HAIs surveillance are : To quantify rates of infections and compare them within / between healthcare facilities , engage clinical teams to adopt best practices , introduce evidence-based and cost-effective interventions to reduce HAI and to identify priority areas where to allocate resources . Surveillance data is used to quantify and monitor HAIs burden , to detect outbreaks , to identify risk factors , to plan , implement and evaluate control interventions , to identify areas for improvement , and to meet reporting mandates . Various surveillance methods have been recommended and validated , including continuous surveillance , active / passive surveillance , prevalence surveys , alert-based surveillance , all of which , with different characteristics and at different rates are labor intensive , costly and time-consuming .”
Enter AI . It has proven to be valuable related to predicting the risk of nosocomial Clostridioides difficile infection ( CDI ), for example . As Fitzpatrick , et al . ( 2020 ) point out , “ Unlike traditional CDI risk stratification , machine learning is not limited to known risk factors but can consider a range of variables within the EHR to validate the application , and thereafter , models can be developed tailored to a particular healthcare facility or patient population . Machine learning applications can also more readily cope with the dynamic nature of healthcare than traditional surveillance models , whereby if a patient ’ s CDI risk changes during their inpatient stay , the IP & C and clinical team could be alerted accordingly . While prospective studies are required to validate these publications and study other HAI , this tailored approach has the potential to transform HAI surveillance and IP & C . Timely accurate identification of patients at high risk of CDI and those at high risk of progression to complicated CDI could facilitate customized IP & C and antimicrobial stewardship strategies and anti-CDI therapies . This approach should also be beneficial for clinical trials of novel anti-CDI therapies whereby patients most at risk of CDI can be readily identified for recruitment .”
In the clinical microbiology laboratory , AI data mining of routine microbiology laboratory results could be used to detect and predict clusters / outbreaks of multidrug-resistant organism colonization and / or infection events . This type of analysis
could also facilitate detection of potential sources of these events which is frequently a difficult and time-consuming aspect of epidemiological investigation .
More recently , the applications of AI in the novel coronavirus ( COVID-19 ) outbreak have highlighted its potential for generation of near-real-time information for public health and IP & C purposes . AI could facilitate decision-making when large amounts of data were emerging rapidly , by analyzing data from a variety of sources , such as government and national reports , social media , news outlets and others . At the local level , AI data analytics enable IP & C and public health experts to focus on strategies to minimize infection rather than spending inordinate amounts of time on data gathering and organization into reports .
AI ’ s Role in Infectious Disease
Rabaan , et al . ( 2023 ) observe that , “ AI is used in various ways in infectious disease and healthcare that cover diagnosis , epidemiology , treatments , and antimicrobial drug resistance studies . AI can process big data collected from the spread of the disease in a short time interval , which can help predict the new hot spots of the pandemic .”
In the diagnosis of infectious disease , AI can help clinicians comprehend the characteristics of the immune system as immunological memories that are the most critical characteristic of the immune system . AI-based image analysis is another technique used to analyze clinical images of different diseases . In the case of COVID-19 , AI was used to analyze the imaging dataset of patients and divide them according to severity levels that include mild , moderate , severe , and critical conditions . AI is widely used in healthcare for large data analysis and diagnosing communicable and non-communicable diseases . It also assists in disease outbreak analysis .
As Rabaan , et al . ( 2023 ) note , “ In the therapeutic aspect , AI plays an important role in pathogen detection , image-based diagnosis , and many other model-based diagnoses that help medical practitioners and researchers better elucidate the disease ’ s causative mechanism and host-pathogen interactions . Radiology , big data analysis , early disease detection , and personalized medicine are areas where machine learning algorithms have been found to be effective in the pharmaceutical and healthcare sectors .”
AI ’ s Role in Public Health
Beyond the IP & C sector into the larger world of public health , AI has demonstrated its benefits throughout the COVID-19 pandemic , when it was critical in genome sequencing , drug and vaccine development , identifying disease outbreaks , monitoring disease spread , and tracking viral variants , according to Parums ( 2023 ), who adds , “ AI-driven approaches complement human-curated ones , including traditional public health surveillance … AI has supported infectious disease surveillance methods to identify pathogens and their variants and allows for a rapid response with the most effective treatments or public health measures .” The challenge , Parums adds , is that surveillance data from clinical symptoms and signs alone can be unreliable . For example , the initial symptoms and signs of COVID-19 can be similar to rhinovirus , enterovirus , or influenza virus infections .
Parums ( 2023 ) says that the rapid spread of SARS-CoV-2 required “ a rapid response from various public health tools , including new and long-standing AI solutions for the surveillance of infectious diseases . Lessons learned have shown that AI may be used in early-warning systems for infection , detection of new disease outbreaks , epidemiological tracking , tracing , and forecasting , and the allocation of healthcare resources … AI algorithms and analytics can be used to track emerging pathogens with possible pandemic potential .”
Parums ( 2023 ) adds , “ The field of AI in clinical medicine is evolving . However , despite its many limitations and concerns , AI may bring the most apparent medical benefits in diagnosis and infectious disease surveillance . AI methods can help identify infection outbreaks from reports of symptoms , online posts on social media , contact with healthcare professionals , and evaluate electronic medical health records . Passive AI surveillance methods can be used to identify a lack of adherence to pharmaceutical and nonpharmaceutical interventions , including facial image-recognition algorithms to monitor compliance with mask-wearing . Facial image-recognition algorithms can also be used to monitor population movements and monitor social distancing .”
However , there are many limitations and concerns with using AI in infectious disease surveillance , and AI disease-tracking systems must be supplemented by molecular testing . As Parums ( 2023 ) explains , “ Constant recalibration of AI monitoring will be required as new pathogen variants emerge , and variables such as vaccination will modify demographic characteristics and disease presentation . Also , these AI systems will never be completely accurate and may result in false alarms or failure to identify important epidemiological signals .”
Some experts say that AI not only can improve public health but also combat misinformation , enhance surveillance , and streamline patient care .
As Marra , et al . ( 2023 ) observe , “ AI also has positive implications for public health . AI systems may potentially combat
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18 • www . healthcarehygienemagazine . com • june 2024