Wildlife artist and author Nancy J. Bailey has written several books, primarily horse stories like, 

. A memoir co-authored with her sister Amanda Bailey, 

 won the 2015 Honorary Medallion from the Book Reader’s Appreciation Group. This makes Amanda one of few recognized authors with Down syndrome. 

An avid animal trainer, Nancy has 4 training books to her credit. She is active in community theatre and has garnered a CTAM award for one of her plays. Two of her plays have been produced by Michigan theatre groups. Nancy currently lives in Michigan with several dogs, cats, a geriatric dove, and two horses. Her horses, PepperJack and Clifford, Junior, travel to schools and libraries performing tricks and promoting literacy.



Nancy J. Bailey

Artificial intelligence can be very useful in infection control and prevention; however, there are challenges to be overcome. 

The use of artificial intelligence (AI) (ie, machine learning) has been a growing mainstay in infection prevention and control (IPC). The advantages are many, including consistency, speed, and the ability to handle massive amounts of data. However, although the positive attributes of AI handling IPC continue to be measured, there are a significant number of challenges involved. Representative datasets should be of high quality, and biases within preexisting databases must be considered.

AI offers tremendous potential for infection control. The National Center for Biotechnology Information states that AI can potentially locate transmission events during outbreaks. Predicting patients at high risk of infection is another advantage of AI and would hone IPC intervention. Studies have explored laboratory infection diagnosis and epidemiology.

Further exploration may reveal as-yet-undiscovered benefits of AI, which also offers:

objective pattern recognition, which can enhance diagnostics,

Studies show that hand hygiene apps, such as SureWash, will require further evaluation within varied clinical scenarios.

 However, these handwashing apps can deliver a change in habits, especially for staff members who have grown to depend on electronic reminders. When feedback is removed, performance tends to return to baseline. “In an outpatient setting, [although] machine learning with feedback was associated with improvements in staff hand hygiene before first patient contact, concerns regarding accuracy, long-term sustainability, and user fatigue from repeated notifications were noted,” according to Fitzpatrick et al, in a recent study.

Annual Update in Intensive Care and Emergency Medicine 2022

, research and development in the intensive care unit (ICU) is at an all-time high.

 To date, the implementation of AI into clinical practice has been inconsistent, but the results are promising. An increase in studies seems inevitable with the expected growth in the use of AI.

An urgent need within the ICU is to optimize infection management because patients require treatment that is adequate and timely. Inadequate management of infection can result in increased mortality rates.

 With antimicrobial resistance on the rise, the need for near-immediate and accurate diagnosing is vital. The primary focus of AI within the ICU has been advancing the infection management field, addressing the potential for sepsis, and more recently, addressing the potential for COVID-19.

Many AI models attempt to forecast an event, such as sepsis, central line–associated bloodstream infections, and ventilator-associated pneumonia (VAP). These prediction models have been developed using routine health care data stored within databases. The prediction models, in theory, can give the physician opportunity to address risk factors. If the event cannot be prevented, a prediction model can offer a chance to monitor the patient and treat them appropriately in a timely manner. The success of these predictive algorithms has shown an overall potential to decrease infection-related morbidity and mortality.

Although antimicrobials are still prescribed for patients who do not need it, machine learning may help identify patients who are less inclined to develop bacterial infection. These patients are suited to discontinue antimicrobials altogether. The best performing AI models in this effort identified patients at low risk of infection with a negative predictive value of more than 93%.

Although some infections are straightforward and well described, others may harbor symptoms that are subtle and more subject to interpretation.

 For example, VAP can present various combinations of symptoms, microbiological anomalies, radiographic features, and biochemical results. The AI assistant has been shown to improve objectivity and, when combined with human evaluation, improve diagnostic accuracy. New diagnostic techniques, such as electronic nose sensor signals when diagnosing VAP, have established good accuracy with AI. In addition, AI has been good at erasing the human-imposed hard line between infection and noninfection. AI can help classify the middle ground (ie, borderline infection). The time saved in this process can be extremely significant for treatment and healing.

Determining whether a patient has an infection or systemic inflammation without infection can be tricky because both diseases have different data types to review, abnormal values are common, and current discriminative tests are lacking. Antimicrobials are commonly prescribed, but the causative pathogen is learned long after therapy has begun.

 According to investigators, AI machine learning can play a role in rapidly identifying the pathogen and its antimicrobial susceptibility right from clinical samples within approximately 30 minutes. This would lead to more timely and appropriate treatment, possibly allowing an adjustment to the antimicrobial dose before the second one is administered.

TM Rawson et al supervised machine learning models to support the diagnosis of bacterial infection using only blood test results.

 This became a significant tool in the context of COVID-19, with the difficulty in diagnosing coinfection of bacterial origin. The algorithms are still in evaluation phase, but initial trials are encouraging.

AI is proving to be useful in finding species of bacteria without the need for culture or incubation period, using limited samples of urine, sputum, or blood. This technique has the potential to reduce time identifying antimicrobial resistance and offers a high accuracy rate for certain infections. AI can identify most urinary tract infections, antibiotic susceptibility, and the 30 most common bacterial and yeast isolate classes presented in the ICU worldwide.

With these efforts come the development of prediction models. These models are helpful for predicting the causative organism or antimicrobial resistance. Such models may become useful in low- and middle-income countries. One example is a study in Cambodia at a children’s hospital, performed by Oonsivilai et al,

 using only variables from clinical data, demographics, and living conditions.

Researchers are currently exploring machine learning apps in all aspects of the microbiology laboratory. Most of these AI models are geared toward identifying microorganisms and evaluating antimicrobial resistance, always in hopes of reducing the essential turnaround time.

Prescription review is another area under scrutiny. Parameters such as dosage, route of administration, and duration are included. These systems are time intensive and subject to changing guidelines.

Although research on machine learning continues to expand in the field of infection prevention, clinical implementation remains sporadic. Most models are still in the prototype stage. Because these models are designed for trials, validating them in external datasets will be a challenge. “IPC and health care generally need to become less fragmented to access these technologies. This is essential to train AI applications appropriate for a particular context and ensures that the algorithms perform consistently across patient cohorts, especially those [that] may not have been adequately represented in the training set,” wrote Fitzpatrick et al in a study using AI in IPC.

It’s universally agreed that nothing can replace the warmth of a caring health care professional. Although there appears to be infinite potential for AI as a tool within the infection control arena, a decision must be made about what health care professionals consider sufficient for using AI in clinical practice. Beyond the variables involved in treatment of individuals, other factors to consider include legal and ethical ramifications. Physicians must be educated and encouraged to evaluate AI model abilities and studies associated with AI. It is crucial that AI be regarded as a tool and not an end user. The limitations and biases within AI and the possibility of failure are real, and it is the responsibility of the medical professional to ensure the safety of the patient.

De Corte T, Van Hoecke S, De Waele J. Artificial intelligence in infection management in the ICU. 

. 2022;26(1):79. doi:10.1186/s13054-022-03916-2

Fitzpatrick F, Doherty A, Lacey G. Using artificial intelligence in infection prevention. 

. 2020;12(2):135-144. doi:10.1007/s40506-020-00216-7

Whitacre Martonicz TL. Thermal imaging to increase hand hygiene adherence. 

®. October 14, 2022. Accessed October 26, 2022. https://www.infectioncontroltoday.com/view/thermal-imaging-to-increase-hand-hygiene-adherence

Rawson TM, Hernandez B, Wilson RC, et al. Supervised machine learning to support the diagnosis of bacterial infection in the context of COVID-19. JAC Antimicrob Resist. 2021;3:dlab002.

Oonsivilai M, Mo Y, Luangasanatip N, et al. Using machine learning to guide targeted and locally-tailored empiric antibiotic prescribing in a children’s hospital in Cambodia. 

 2018;3:131. doi:10.12688/wellcomeopenres.14847.1

Articles

Artificial intelligence increases infection prevention and control measures in health care facilities and beyond. 

Artificial Intelligence Offers Advancements and Challenges in the IPC Sphere

Caymus, a Goldendoodle, and one half of a Pet Therapy Team with his owner and handler, Michelle Martonicz.

No question that pets provide many therapeutic benefits to patients and medical staff alike. Many animals can be therapy animals—dogs, cats, bunnies, guinea pigs, and even 

. We are only beginning to scratch the surface of the multiple ways therapy animals promote healing and the immeasurable comfort that their presence can provide. However, questions arise about the health risks of bringing animals into a medical facility, especially with the onset of COVID-19.

“Some risks are associated with therapy animal visits, and the possibility of infection is something that always needs to be kept in mind,” Michelle Martonicz, the human half of a registered Pet Therapy Team at Phoenix Children's Hospital in Phoenix, Arizona, and Caymus, her 6-year-old Goldendoodle, told 

). First and foremost, we must stay alert to our surroundings and the situations we can be exposed to regarding the type of patients we can be exposed to. The hospital takes great measures in proper signage on the doors to inform us whether the child is infectious or not.”

American Association for Critical-Care Nurses

, infection risks for COVID-19 in animals remain low. No animals should have any contact with patients infected with COVID-19. Other precautions can be taken to protect the risk of infections to patients, staff, and the pets themselves. These include:

Just as with all other medical-related practices, the emphasis on hand hygiene cannot be overstated. Wash hands before and after handling animals and any equipment associated with them.

Therapy animals should not interact with any patient currently in isolation.

. A single-use barrier, such as a towel, should be placed anywhere the pet rests, including if it is just the animal’s head or paws. Paws require special attention. Guidelines are covered in 

Animal-Assisted Interventions for Health and Human Service Professionals

. Pets are discouraged from licking patients or themselves.

Patients and staff should not sit on the floor to interact with the animal.

 Animals should be bathed and checked for parasites before visiting the facility.

all leashes, collars, and other equipment associated with the animals.

 for those not involved with interaction. This counts for pets, too.

. Animals should not visit if they have wounds or broken skin.

Animals should be trained not to eliminate indoors. But if they do, the handler must be prepared to clean up, and any waste disposal tools and materials must be disinfected before the visit and stored out of the area.

There are safety factors to be considered; mainly, the pet should be kept on a leash, with an awareness of all tubes, monitors, and other equipment in the room. Dogs and other pets should not be encouraged to “give a paw” or “shake hands,” as the feet can be a harbinger of germs.

Martonicz explains the protocols that she and Caymus are required to follow. “We practice the following methods to help reduce the risk of infection for Caymus, myself, and the children we visit:

Caymus has a health screening done by his veterinarian, confirming that he is free of transmissible diseases and parasites, and is up-to-date on his vaccinations. It is a requirement at PCH for this to be done annually.

PCH requires handlers to have certain vaccinations and TB tests up to date.

If he or I am not feeling well, we cancel our visit.

Caymus is not on a raw food diet as these products risk infecting pets with parasites and foodborne illnesses, which they could pass on to the children during our visits.

I must bathe Caymus no more than 24 hours before a visit.

We use many barriers while visiting the children at PCH, which include masks for everyone in the patient's room, and clean sheets put on the child's bed, so Caymus can visit on the bed and stand outside of a child's room if they need to be free of droplets.

Hand hygiene is essential. We sanitize before and after visiting a patient as well as anyone petting Caymus; they sanitize before and after touching him.”

 that the safety of the patients, parents, staff, Caymus, and herself is always foremost in her mind. “The standards and policies at Phoenix Children's Hospital are specifically designed to minimize risks, including the risk of infection, and to help make therapy animal visits as safe as possible. PCH and all of their Pet Therapy Teams feel the benefits of the human-animal bond are significant, and we all want to be sure every visit with a PCH child is safe and effective."

Hospitalized children may be scared and uncertain, and therapy animals can help ease their insecurities. But what is done to protect the humans—and the animals—from infectious diseases? 

Therapy Animals in Hospitals: Preventing Infection Risks

The onset of COVID-19 demanded a host of creative thinking from health care professionals, in ways to prevent infections while still learning about the virus. The pandemic required the transformation of already established facilities, and communication along with standardization become crucial factors in the process. Safety and consistency, especially for frontline healthcare workers, were an absolute priority. Thanks to successful practices that established reliable processes, a framework has been created for COVID-19 along with future pandemics, and, thus, health care professionals the lessons and continue to modify using this foundation.

One of the lessons learned was transforming a nursing unit into a pandemic response unit and that it works best when a group of teams is established. In the study recently published in the 

“Converting an inpatient nursing unit to a pandemic-response unit: Lessons learned,”

 the investigators reviewed how creating a pandemic response unit was done effectively and quickly. The study also covers what the issues the medical leadership encountered, and how they overcame the problems.

“Standardization and communication were crucial in maintaining safety and consistency, especially for physicians and nurses on the front line. This unit did not have any patient to staff transmission of COVID–19 due to the highly reliable processes that were put in place,” the investigators noted in the report. “These best practices create a framework for future pandemics. As we continue to learn from this and future emerging pathogens, this framework can be utilized as a foundational starting point and modified to fit the next challenge.”

The University of California, San Francisco (UCSF) Medical Center, one of the first institutions to quickly formulate a pattern of treatment once the COVID-19 surge began, needed to create a unit solely dedicated to COVID-19 care with the conversion of an inpatient nursing unit led by the Infection Prevention (IP) department. With an interdisciplinary approach, the UCSF Medical Center effectively planned, executed, and sustained caring for COVID-19 patients within a converted inpatient unit. This study describes the framework that the team created to “provide visualization, organization, and communication around some of the key elements that are needed to successfully convert a unit in this manner,” the investigators wrote.

The teams that made up the framework to create the pandemic response unit included: infection prevention, nursing, facilities and engineering, safety, and environmental services. Communication kept everything moving as smoothly as possible.

Teams Involved in Interdisciplinary Approach

The central part of the pandemic response policy, this team develops the approach to COVID-19 patients, from admittance to discharge. Combined with the Department of Medicine Division of Infectious Diseases, this team provides guidance for isolation requirements and personal protective equipment (PPE). This team organizes by providing check lists and signage. The infection prevention team works with staff on direct care, training, and safety issues. Various staff members and health care professionals can perform roles in support of infection prevention. These include roles such as PPE Monitors and advisers on screening methods and follow-up on exposure.

Nurses took the brunt of the COVID-19 pandemic workload to reduce exposure and transmission by other staff members. Nurses established a workflow area with equipment at the ready, and the pattern was the same in every pandemic-specific room. These included areas for lab specimens, medication, inhalers, and other supplies. The rooms were clearly marked with reminders of the protocol.

For this hospital, the facilities and engineering team needed to convert the 28-patient room unit to negative pressure, so air scrubbers and HEPA filtration were added to achieve appropriate airflow.

The teams included in the safety department were the Environmental Health and Safety (EH&S) and security teams. The  teams selected, disinfected, and stored PPE, and faced issues when PPE was difficult to obtain, and alternative solutions had to be found.

Security dealt with travel flow with transporting patients, keeping out visitors, and allowing only staff with badge access on the unit.

The role of Environmental Services (EVS) was vital to cleaning and disposal of waste for the protection of all patients and staff, preventing cross-contamination once a patient is moved from the room, and a new one is moved in. The guidance for this work was given from local, state, and national health experts, and included reducing exposure time in the patient room. When supply shortages occurred, the EVS team collaborated with the IP team to eliminate essential items waste.

Hospital leadership included the sum of all groups as well as the Patient and Family Advisory Council. The pandemic response unit reported all updates to Incident Command, which helped to determine workflows and common practices as the pandemic evolved. Communication was crucial in planning for emergency facilities if the pandemic unit overflowed. Once a protocol was established, it helped bring clarity to the pandemic phase. The patients’ loved ones were kept apprised through the Patient and Family Advisory Council. Patients were given information about this plan of action and provided with as much comfort as possible during their isolation.

Because these guidelines were established, the facility was able to avoid patient-to-staff transmission of the virus.

With COVID-19 beginning to surge, University of California, San Francisco (UCSF) Medical Center needed to create a response unit from what had been an inpatient nursing unit. A recent study explains how this conversion was effective and can stand as an example for other facilities. 

Nancy J. Bailey

Articles

Artificial Intelligence Offers Advancements and Challenges in the IPC Sphere

Therapy Animals in Hospitals: Preventing Infection Risks

Transforming a Pandemic Response Unit From an Inpatient Nursing Unit