Antimicrobial resistance (AMR) is a global health threat, necessitating robust strategies to manage its impact. Infections emerge relentlessly, while new antibiotics are scarce, making it crucial to preserve current antimicrobial treatments. The UK’s National Action Plan (NAP) 2024-2029¹  highlights the critical role of antimicrobial stewardship (AMS) to confront AMR. Despite its potential, NHS data remain underused for clinical care, prompting calls from Lord Darzi² and NHS England³ to leverage technology more effectively. One such technology is Clinical Decision Support Systems (CDSS), a primary marker of digital maturity⁴. This piece will look at how CDSS can significantly enhance AMS initiatives. 

The role of CDSS in Antimicrobial Stewardship 

A CDSS is software designed to assist healthcare professionals in making informed clinical decisions. In an increasingly digital environment, CDSS harnesses accumulating healthcare data and integrated patient information with evidence-based guidelines. This provides real-time recommendations for antimicrobial prescribing, ensuring that treatments are more likely to be appropriate and effective. 

Optimising Antimicrobial Prescribing 

CDSS tailor recommendations based on clinical diagnosis, local and national guidelines, and patient specific factors such as allergies, weight and previous infections. There is a limit to the amount of information a person can review  when making a decision. CDSS analyse large volumes of data instantaneously, leading to more informed decisions. This personalised approach to prescribing will help reduce the misuse of antibiotics, enhances patient safety and reduces prescribing errors - key goals of effective stewardship.
Example - CDSS flags an overweight patient on too low a dose of antibiotic.  

Identifying and Prioritising Patients for Antimicrobial Review 

The use of antimicrobials in hospitals is widespread. However, AMS teams may have limited resources, making it crucial to utilise their time efficiently. CDSS uses real-time patient data from hospital information systems to identify patients on suboptimal treatment. By automating time consuming manual processes, CDSS enhance the efficiency of AMS team's workflows, enabling them to focus on reviewing patients where they can make a significant impact. 
Example - a scoring system can flag patients on an inappropriate antibiotic for the target organism for priority review.

Integration of Diagnostics 

Diagnostic results are crucial for antimicrobial treatment; however clinicians can lack timely access to relevant clinical results. CDSS integrates patient data from numerous systems to support decision making. They can assist in interpreting diagnostic tests and identifying infections that require antimicrobial treatment, are resistant or do not require antimicrobials, reducing unnecessary exposure and resistance risk. 
Example - an alert to a resistant bacteria requiring treatment change.

Support De-escalation and Intravenous to Oral Switch

Broad-spectrum antibiotics can harm patients’ microbiota, leading opportunistic infections. The United Nations General Assembly has set a target for 70% of appropriate prescriptions to be narrow spectrum¹. CDSS collates localised antibiograms to help prescribers select optimal narrow spectrum antibiotics.  Intravenous to oral switching improves patient outcomes, reduces admission time and lessens nursing burden. CDSS analyses patient factors such as white blood cell count, temperature and infection site to determine appropriate candidates for switching. 
Example - CDSS alerts clinicians to a patient with normal temperature, procalcitonin and enteral access for oral therapy. 

Supporting Infection Prevention and Control 

The NAP¹ emphasises whole-systems approach to tackling AMR. CDSS can contribute to this by providing alerts for Infection Prevention and Control (IPC) measures, infection outbreaks and contact tracing. CDSS can track and analyse infection patterns, helping hospitals implement targeted interventions to prevent resistant organisms spreading. 
Example - CDSS detects an unusual number of MRSA cases on a ward, which could be a potential outbreak. It produces an alert to prompt immediate investigation and containment. 

Extraction and Utilisation of Data 

Resistance trends are dynamic and differ by geography, therefore real-time surveillance is a crucial element of optimal antimicrobial stewardship. CDSS allows you to identify trends at an individual prescriber level, clinical specialty or area. This granular insight provides stewardship teams with the autonomy to identify unwarranted variation and opportunities to drive improvement. Interventions tailored to the specific environment allows for more effective strategies for reducing antimicrobial resistance and improving patient outcomes. On a national level, access to this surveillance can drive policy development. 
Example - Hospitals can use resistance data to regularly make amendment to their prescribing guidelines. 

Aligning with National and Digital Strategies

The National Action Plan 2024-29: The NAP¹ sets out strategic outcomes to reduce the need for, and unintentional exposure to, antimicrobials through IPC and strengthened surveillance and optimise the use of the antimicrobials by using CDSS. 

NHS England Digital Vision for Antimicrobial Stewardship: The NHSE digital vision³ outlines the functionalities required for clinical digital systems to support AMS. CDSS meet these requirements by integrating with required hospital data systems to provide timely and relevant clinical information, real time patient alerts to ensure optimal antimicrobial prescribing, as well as aiding clinical workflow by providing decision support to identify and prioritise antimicrobial reviews⁵. CDSS supports surveillance and reporting of key metrics. 

A Case Study – Lumed^TM APSS^TM 
Centre Hospitalier Universitaire de Sherbrooke, Canada

Lumed^TM APSS^TM is a CDSS for antimicrobial stewardship. It collects real-time patient data from the electronic health records and laboratory information system to identify non-optimal prescriptions, producing a prioritised view of alerts based on local and national guidance. Each patient has a timeline visualisation, with integrated clinical parameters, microbiology results and antimicrobial therapy, to provide a holistic view of patient data in one place to aid decision making. Up-to-date agile cumulative antibiograms provides microbiology experts with local and relevant resistance profiles, and the reporting dashboards allow local and national reporting. 

The Centre Hospitalier Universitaire de Sherbrooke in Canada, a 700-bed hospital, implemented APSS^TM and conducted a retrospective cohort study over 5 years⁶. APSS^TM collected and reviewed 40,605 hospitalisations. 5,665 recommendations were produced, which when reviewed had a 91% acceptance rate by prescribers. Alert fatigue is a known challenge with CDSS and can lead to clinicians ignoring important warnings. This high acceptance rate highlights the importance of local customisation and adaptability of CDSS to ensure clinical relevance.

The most significant findings were a decrease in length of stay by 2.3 days, a reduction in antimicrobial consumption by 24% and a 28% reduction in antimicrobial spending. These positive effects were sustained for the 3 years after implementation, showing a change in practice. At a time when the NHS is under huge pressure to reduce hospital stays and spending, this case highlights the significant potential benefits of utilising CDSS for AMS.