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A 2016 study conducted by Johns Hopkins found that medical errors are the third leading cause of death in the U.S....
This is a concerning statistic that physicians work tirelessly to change and hospitals continue to adjust their processes and procedures to reduce medical errors. Some hospitals implement new software that provides early warnings for potential risks when prescribing medication and offers clinical decision support. Fortunately, AI addresses some of the challenges physicians and hospitals face as they work to reduce medical errors.
The first step physicians perform before treating a patient is to review their medical chart. This includes the patient's medical history, lab results, medical images and other relevant information. Patients with complex conditions are likely to have more data than those with common illnesses. This means physicians need more time to review the data before treating these patients.
Physicians often rely on their EHR to flag common issues such as drug interactions and allergies. These are expected features in all EHRs in the market today. As patient data continues to grow, some EHRs cannot meet the demand for proactive patient data analysis without the help of third-party AI tools that perform advanced data analysis and mining.
AI can process large data sets in record time and faster than a human can regardless of format, such as text, handwritten notes and images. The AI tool organizes and analyzes data, compares it to other clinical data to detect abnormalities or similarities, and then provides feedback for the physician. This can help reduce medical errors by highlighting areas of concerns that might be easy to miss.
Using AI to evaluate patient data complements many functions that are currently offered in EHRs. However, tech giants have recently pushed cloud-based AI services to help physicians treat their patients. For example, Amazon recently announced a service called Amazon Comprehend Medical that supports physicians as they interact with their patient's data. The tool ingests health data for one or multiple patients, then uses its AI capabilities to sort, classify and analyze the data to generate a meaningful summary. This use of AI highlights the value the technology has as a way to reduce medical errors.
There are certainly plenty of other players in the AI space that target healthcare with their services and see the opportunity that exists to improve healthcare outcomes. IBM, Microsoft, Google and others continue to invest in AI capabilities, especially for healthcare.
The success of AI in supporting physicians and reducing medical errors is dependent on its ability to access clinical data in order to properly deliver its recommendations. As more services become available, healthcare organizations must ensure that their EHR vendors are committed to supporting their AI-based initiative by ensuring the data is readily available to the AI system. However, some caution that AI is not a perfect system and cannot be trusted yet for a full patient diagnosis.