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Machine learning and predictive analytics powered by AI are helping identify and address the root causes of medication non-adherence through a new generation of medication management systems.
Fremont, CA: Modern medications are always evolving and progressing due to the science behind them. All sorts of illnesses and conditions are being treated, cured, and prevented by new treatments, cures, and prevention methods in the news. In spite of this wondrous science, the medications that must be administered are complex. The complex instructions given by doctors often lead to patients missing their medication doses, losing track of when they last took their medicine or taking their medicine incorrectly. Healthcare science is exact, but when it relies on human memory and routine for success, issues and complications can occur.
The Need for Medication Management Technology
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CMIOs believe medication management initiatives positively impact patient safety, according to a survey from the Association of Medical Directors of Information Systems. According to a recent study, there is a 45 percent risk of error across all of these medication management processes, which can range from minor to serious issues. These errors could lead to more serious errors that could endanger the patient.
How Does Medication Management Technology Work?
Healthcare staff is spending more time with patients thanks to medication management solutions and software, providing the right dose of medicine to the right patient at the right time and improving medication adherence for patients. As a result of these systems, patients are assessed, medication reconciliation is performed, prescriptions are given, medications are dispersed, and monitoring is performed. By integrating an electronic health record (EHR) and a medication device at the patient's bedside, medication parameters from the EHR can be pre-populated into the device based on patient characteristics, reducing the chances of programming errors.
Machine learning and predictive analytics powered by AI are helping identify and address the root causes of medication non-adherence through a new generation of medication management systems. Through machine-learning models, outliers in prescriptions can be identified from a pool of similar patients and potentially prevent medication errors and logistical errors over time. Additionally, clinicians can use AI-based systems to detect potential side effects of drug combinations or controlled substance overdoses during the prescription process, during medication administration, and during patient transfers. Across healthcare organizations, next-generation analytics platforms are delivering actionable insights integrated into clinical workflows and empowering nurses, clinicians, and executives at the point of care.
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