Abstract
Much of the AI work in healthcare is focused around disease prediction in clinical settings, which is an important application that has yet to deliver in earnest. However, there are other fundamental aspects like helping patients and care teams interact and communicate in efficient and meaningful ways, which could deliver quadruple-aim improvements. After heart disease and cancer, preventable medical errors are the third leading cause of death in the United States. The largest subset of medical errors is medication error. Providing the right treatment plan for patients includes knowledge about their current medications and drug allergies, an often challenging task. The widespread growth of prescribing and consuming medications has increased the need for applications that support medication reconciliation. We show a deep-learning application that can help reduce avoidable errors with their attendant risk, i.e., correctly identifying prescription medication, which is currently a tedious and error-prone task. We demonstrate prescription-pill identification from mobile images in the NIH NLM Pill Image Recognition Challenge dataset. Our application recognizes the correct pill within the top-5 results at 94% accuracy, which compares favorably to the original competition winner at 83.3% for top-5 under comparable, though not identical configurations. The Institute of Medicine claims that better use of information technology can be an important step in reducing medication errors. Therefore, we believe that a more immediate impact of AI in healthcare will occur with a seamless integration of AI into clinical workflows, readily addressing the quadruple aim of healthcare.
Highlights
The third most common cause of death is not disease, but medical error, with 250–400 k or more mortalities per year.[1,2,3,4,5] The epidemic of medical error gained attention in reports from the Institute of Medicine,[3,6] which found that the most common type of preventable medical error is medication error, which results in over 1.5-m injuries and over $3b in complication costs alone
The results shown in our survey of the existing Convolutional Neural Network (CNN) technology applied to pill identification demonstrate that recent advances in AI make it feasible to mostly automate this task involving pharmacies, patients, first responders, and care providers
AI-powered tools like ours will allow care teams to focus more on the patient increasing their productivity and deceasing the risk of error. It is through these mechanisms that our system can have an effect in the quadruple aim
Summary
The third most common cause of death is not disease, but medical error, with 250–400 k or more mortalities per year.[1,2,3,4,5] The epidemic of medical error gained attention in reports from the Institute of Medicine,[3,6] which found that the most common type of preventable medical error is medication error, which results in over 1.5-m injuries and over $3b in complication costs alone. The Institute of Medicine’s 2006 report further provides guidelines on reducing the high frequency and unacceptable cost of medication error, including greater use of information technology, which could be implemented at each stage from prescribing and dispensing through to monitoring the patient's response. There are clear cost benefits; computerized medication systems have the potential to reduce errors by 84% and save hospitals over $500 k/year in direct costs.[7]
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