Use of Artificial Intelligence and Machine Learning to Improve Care by Critical Care Pharmacists
Using machine learning- and artificial intelligence-developed tools in the ICU has the potential to optimize critical care pharmacist resources and improve patient safety by reducing adverse drug events.
Critical care pharmacists have the expertise to handle patients that have complex medical needs
In intensive care units (ICUs), critical care pharmacists (CCPs) are integral members of healthcare teams. CCPs analyze and manage the highly complex medication regimens of ICU patients and work to identify and provide guidance for medication-related problems. Research shows that having a pharmacist on rounds in the ICU has significant benefits, including reduction of medication errors and adverse drug events (ADEs), improved patient outcomes, reduced costs, and most importantly, reduced risk of death by 20 percent.
But access to these pharmacists in the United States is lacking. Not all ICUs have CCP care and even when they do, the CCP is often caring for many patients—up to 50 or more at a time. This leads to high cognitive load and difficulty effectively managing time to provide for the patients most in need of CCP care.
As a CCP herself, Dr. Andrea Sikora sees firsthand how ICU patients benefit from having a CCP on the care team. With two recently awarded AHRQ-funded grants, she is studying the challenges that can be created by these gaps in CCP care and availability and how technology can better support their work to improve patient-centered outcomes.
Implementing a tool can quantify the complexity of a patient’s medication regimen and predict potential ADEs
In the first study, Dr. Sikora and her team at the University of Georgia are using artificial intelligence and machine learning to create algorithms for predicting which patients are at risk for ADEs based on patient features, opportunities for intervention, and those associated with poor outcomes. The tool, called the Medication Regimen Complexity Intensive Care Unit, or MRC-ICU score, quantifies the complexity of a patient’s medication regimen to predict ADEs that could be prevented by timely CCP intervention. The tool will be integrated into visualization dashboards, called ICView, that guide CCPs in preventing ADEs in patients, thus improving patient safety.
Using machine learning to predict when pharmacists are needed for critical care
In the second research study, the team plans to develop and validate machine learning predictive models to optimize the workflow for CCPs by identifying which patients need CCP intervention. The models will be integrated into the MRC-ICU tool and will summarize CCP workload through predicting total CCP interventions. The models will also guide CCP care by predicting ADEs that may be prevented by CCP intervention. Identifying which interventions—and under which conditions—can improve outcomes by helping administrators better determine workload, such as optimal CCP-to-ICU ratios through providing workload insights.
“That tool quantifies the complexity of what the patient is taking in the ICU with the goal that it's going to help predict how much effort you need from a pharmacist. If you knew that, then you could say, ‘Okay, this patient needs an hour of effort, and we have 20 patients in the ICU.’ Well, that's 20 hours, so that is probably more than one 8-hour shift. And so, you'd be able to have those kinds of conversations around workload.”
- Dr. Andrea Sikora
Collectively, Dr. Sikora’s innovative AHRQ research shows the exciting ways that machine learning and artificial intelligence can be used in healthcare. In addition to benefiting patients by facilitating CCPs interventions when medication-related problems are found, the research can benefit the healthcare system at large by providing the justification for the critical role that CCPs play.