Scientists worldwide are working on harnessing cutting-edge AI techniques to make a mark on patient safety. Be it in a pharmacy, hospital, or managed care facility, the focus is on eliminating medication errors. According to a recent survey, 7,000 to 9,000 people die each year due to a medication error in the United States alone. Over $40 billion is spent looking after patients with medication-associated errors, not to mention the associated psychological and physical distress.
Some of the main reasons for these errors include failure to communicate drug orders, illegible handwriting, wrong drug selection chosen from a drop-down menu, confusion over similarly named drugs, confusion over similar packaging between products, or errors involving dosing units. Medication errors may be due to human errors, but it often results from a flawed system with inadequate backup to detect mistakes.
Innovative digital tools
Israel -based MedAware's technology uses big data analytics and machine learning algorithms to analyze large-scale Electronic Medical Records (EMRs) data to learn how physicians treat patients in real-life scenarios automatically. When a new prescription deviates from the spectrum of typical treatment patterns, it's flagged as a potential error and prompts the physician to double-check.
Recently, MedStar Health and the Pennsylvania Patient Safety Authority partnered to use machine learning tools to generate more actionable insights into improving patient safety.
A medication safety system powered by artificial intelligence to help nurses stop and prevent medication errors is MedEye. Equipped with a scanner for pills and capsules, it uses cameras to identify other medications and has checked over a million administrations in hospitals in the Netherlands, Belgium, Iceland, and the United Kingdom. MedEye verifies the medication using visual recognition and machine learning as it compares against the hospital information system and demonstrates the medication's accuracy.
PerceptiMed is a biotech startup that leverages AI technology to verify medications during dispensing and administration. By identifying the drug and dose of individual pills in real-time while ensuring delivery to the correct patient, this technology eliminates medication errors while improving patient safety and satisfaction. It's also enabling the concept of telepharmacy, which will allow healthcare facilities and pharmacies to balance and distribute workloads while maintaining compliance, adherence, and efficiency.
It is also necessary to have a system for predictions at the patient level rather than on individual prescription orders alone. Lumio Medical is a hybrid AI decision-support software that combines machine learning and a rule-based expert system. Researchers trained a binary classifier to identify patients who were likely to have at least one drug-related error in their prescription order. Some 133,179 prescription orders, along with each patient's individual information was used for model development. In an independent validation dataset, the Lumio Medication hybrid algorithm intercepted 74% of prescription orders requiring pharmacist intervention. Of the remaining 26%, none were life-threatening, researchers said.
Irody is a mobile technology that enables the digital identification of medications to eliminate medication errors. Using a smartphone, patients scan their medicines; advanced computer vision and artificial intelligence algorithms identify the medications immediately and automatically – and compares them with the user's prescription. Pill recognition technology and image-guided medication management are empowering patients to take better control over their medications.
Preventing Drug Overdoses
AI plays a role in patient safety, also by preventing drug overdose. AI uses medication history to predict the potential for opioid overdoses before they happen, augmenting clinicians' efforts to detect this life-threatening risk. Using patients' medical and prescription history from HER or previous claims, algorithms calculate overdose risk scores and predict a patient's risk of unintentionally overdosing from a prescribed opioid.
AI's use to determine the risk of opioid overdose and recommend interventions exemplifies where machine learning and natural language processing are well-suited to create value in medication management by augmenting clinical decision-making. The numerous risk factors contributing to prescribed drug overdoses plus the multiple types and sources of a patient's health history needed to detect actual risk makes it very challenging for a clinician to assess the risk of overdose in a timely fashion, let alone based on an assessment during a patient's visit.
Reduction of prescription errors remains a challenge in patient safety. AI-based solutions serves as a valuable tool for physicians, who are often overworked due to the increasing number of patients. ML algorithm checks against prescription errors, reduce potential lawsuits from their mistakes, and thereby helps hospitals reduce the inevitable human error of physicians and costs associated with those errors. However, the technology's potential hinges on the clinicians to respond to such alerts and their potential to prevent actual patient harm.
While the clinical decision support (CDS) system at many hospitals has alerting tools to identify and reduce medication errors, these tools have several limitations. Being rule-based, they can specify only the medication errors that have been previously programmed into their alerting logic. Further, most have high alerting rates with many false positives, resulting in alert fatigue.
AI offers a solution to this problem as it efficiently scans through the hospital's EHR data and identifies medication errors such as wrong medication prescriptions.
A CDS system driven by machine learning to improve medication error identification would also help alert fatigue, one of the root causes of physician burnout. The use of AI benefits the physician and the hospital and the patients and pharmacists. It improves the speed and efficiency of manually intensive tasks of verifying medication, counting pills, and maintaining adequate staff and inventory, thereby ensuring patient safety and adherence.
Machine learning-driven CDS tools can be a viable approach to improving medication error detection and preventing patient harm. When implemented correctly, AI-enabled decision support systems can enhance patient safety by improving error detection, patient stratification, and drug management.