Person creating artificial intelligence in a digital brain 3D rendering
HealthMay 13, 2022

Strengthening antimicrobial stewardship with artificial intelligence

Artificial Intelligence (AI) is rapidly transforming the healthcare industry and pharmacy practice. However, many clinicians lack a foundational understanding of AI technology and may have difficulty applying it in their own practice setting.

So, what exactly is “Artificial Intelligence'' or “AI”? It is the science of building machines to perform tasks that typically require human intelligence. AI is broken down into three branches: Machine Learning, Natural Language Processing (NLP), and Computer Vision.


Machine Learning is a way to train a model to make accurate predictions. An example of machine learning is how a GPS processes traffic patterns and predicts how long it will take at a certain time of day/day of the week to reach your destination. Based on historical and real-time data, the algorithm can predict that a commute could take 10 minutes longer on Friday evening due to rush hour traffic. This is a simple example, yet it highlights the countless possibilities used in machine learning. The basic steps to developing a machine learning algorithm are to:

  1. Formulate a question
  2. Collect data
  3. Select, train, and evaluate the model
  4. Make predictions 

Natural Language Processing (NLP) is a way to decipher the complicated human language and produce a simple, understandable output. A great example of this is smart speakers activated in our homes. For instance, there are many ways to ask a smart speaker about the weather. You could utilize a voice command to activate the smart speaker and ask out loud “What is the high for today? Will it rain?” and so forth. NLP analyzes the language, generates a human response, and provides an outcome such as “The high today is 65 degrees Fahrenheit and bring your umbrella because there is a 100% chance of rain.” 

Computer vision uses pattern recognition and deep learning to recognize what’s in a picture or video. Have you ever wondered how social media pages, such as Facebook, can tell you who exactly to tag in your picture? This is because computer vision technology can process and then “recognize” facial features of individuals in the photo.

Applying artificial intelligence to healthcare delivery

Now that we have covered the basics of AI, how can these advanced technologies be used in healthcare delivery? Several goals include:

  • Increase efficiency
  • Digital task shifting and managing staff shortages
  • Population health applications to enable targeted and differentiated services
  • Earlier detection of diseases 
  • Improve quality of clinical decision making
  • Continuous patient monitoring

AI applications used in pharmacy practice can be broken down into prevention, diagnosis, and treatment. Examples include: 

Key factors to consider when evaluating an AI solution for healthcare:

  • Feature engineering – data elements that go into the model
  • Clinical expertise – did the data science team collaborate with clinicians in building the model?
  • Validation – How well does the model predict the outcome of interest using validation data?
  • Application and clinical benefits - how is it being used? Having a decent model that makes accurate predictions yet has no applicable clinical benefits has no place in the real world.
  • Interpretation - Is the application easy to interpret and understand?
  • User education and training – clinicians should be educated on what the model output means and how it may impact patient care decisions.
  • Workflow integration - how and when will the clinician receive the information to impact clinical decision making?

The impact of AI in C. diff detection 

Wolters Kluwer leveraged this technology and developed a Clostridioides difficile infection (CDI) risk score. C. diff infections negatively impact patient outcomes and organizations’ financial and quality performance measures. According to CDC data released in 2019, over 223,9000 CDI cases and 12,8000 deaths were reported related to CDI. Quality measures for CDI exist in both the Hospital-Acquired Condition (HAC) Reduction Program3 and Value-Based Purchasing (VBP) Program4 measures.

This means poor performance in these programs results in reduced payments by Medicare. Patients with hospital-acquired CDI have longer length of stay, higher readmission rates, higher total cost, and higher mortality than those who avoid it. Studies have shown Antimicrobial Stewardship (AMS) programs that reduce antimicrobial use in targeted antibiotics (i.e., clindamycin, fluoroquinolones, etc.) result in reduced CDI incidence.5-7 

Paradigm shift in the management of C. diff: Improve patient outcomes with big data


The current practice for treating CDI follows a reactive patient management strategy, where we test our patients after they develop symptoms. With Sentri7’s CDI Risk score, there is a paradigm shift in the management of C. diff to a proactive predictive surveillance approach. The opportunity for improving healthcare delivery is to identify high-risk patients before they are infected. If we identify them earlier, monitor them continuously, and intervene on select few patients (i.e., discontinue an antibiotic or proton pump inhibitor), we can avoid unnecessary testing and promote a more responsive, robust AMS program. 

Wolters Kluwer C. diff AI team: Experts from multiple disciplines working together to reduce patient harm 

A team of data scientists, epidemiologists, infection preventionists, ID physicians, gastroenterologists and an AMS pharmacist reviewed literature and de-identified records to assess risk factors for CDI. The CDI Risk Model was developed and validated against the test data and uses real-time data to calculate a patient’s risk of contracting CDI. This includes hundreds of data elements in the model that incorporates both patient-specific (i.e. demographics, medication orders and administrations, laboratory results, etc.) and hospital-specific (i.e. hospital type, location, C. diff incidence, etc.) factors. An internal study was conducted, and it showed that the model predicted CDI risk with an average lead time of 5.3 days before onset, standard deviation of 4.2 days.

Rule content using CDI risk score

Sentri7’s CDI Risk Score continuously monitors and prioritizes patients at risk in a centralized view by calculating a CDI risk score in real-time. Pharmacists are alerted when a patient’s score reaches above a certain threshold. This allows the pharmacist to intervene on modifiable risk factors, such as antidiarrheals, laxatives, high-risk antibiotics, and proton pump inhibitors. Proactively identifying high-risk patients before they become infected protects patients, patients' families, and hospital staff from this serious and costly hospital-acquired infection (HAI).

The use of AI in healthcare will continue to evolve and grow. New use cases will develop and clinical teams will identify ways to utilize artificial intelligence to tackle challenges, optimize workflow, and improve patient care. We will continue to monitor and identify ways we can support care teams in delivering the best care everywhere, by identifying and building solutions that leverage AI to enhance healthcare delivery and improve outcomes. 


Trusted real-time alerts and evidence-based guidance to ensure at-risk patients receive the right care at the right time, every time.

Sentri7's sophisticated algorithms identify at-risk patients in real-time by breaking down data silos that exist across hospitals and driving consistent clinical action. All to improve patient outcomes and hospital performance.
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