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AI in EHRs: Using AI To Improve Electronic Health Records -


AI-powered EHR systems seamlessly integrate and offer solutions with a variety of functionalities. Machine learning and Natural Language Processing (NLP) can help in recording the medical experiences of the patients, organizing the large EHR data banks for finding important documents, gauging patient satisfaction, etc. The machine learning models merged with NLP can help healthcare providers in transcribing the speech from the voice recognition system into text. The algorithms can be trained well on large volumes of patient data on patient's treatment, equipment used for treatment, respective doctor, etc and carefully segmented based upon the individual patient, illness, treatment for illness, etc. This will enhance the document and information search from the large databases.

The .com World: 8 Ways to Keep Digital Patient Data Safe


Not every healthcare organization embraced electronic medical records (EMRs) at first. But the incentives and regulations put in place by the Meaningful Use and the Affordable Care Act have made it both financially beneficial and necessary to implement them. Now, organizations are not only embracing EMRs, but making it easier for their patients to access and manage them through remote portals. According to the Office of the National Coordinator for Health IT, approximately 63% of patients who used portals did so at their doctors' recommendation. Despite the growing popularity of patient portals, there are still more than 25% of patients who refuse to use them because of privacy and security concerns, according to a 2018 National Coordinator for Health Information Technology (ONC) study.

5-year forecast: exponential growth for AI healthcare market


The AI in healthcare market is projected to expand from its current $2.1 billion to $36.1 billion in 2025, representing a staggering compound annual growth rate (CAGR) of 50.2 percent. That's according to new research from ReportLinker, which notes that the rapid increase in value will be driven largely by North American investment, with the United States at the forefront of innovation and spending. Hospitals and physician providers will be the major investors in machine learning and artificial intelligence solutions and services, the report predicts. "A few major factors responsible for the high share of the hospitals and providers segment include a large number of applications of AI solutions across provider settings; ability of AI systems to improve care delivery, patient experience, and bring down costs; and growing adoption of electronic health records by healthcare organizations," noted the summary of the report. "Moreover, AI-based tools, such as voice recognition software and clinical decision support systems, help streamline workflow processes in hospitals, lower cost, improve care delivery, and enhance patient experience."

20 Years After 'To Err is Human,' NLP Offers a New Way Forward for Patient Safety Health IT Answers


With late 2019 marking the 20th anniversary of the landmark report on medical errors "To Err is Human," now is time for a renewed focus on novel ways to improve patient safety. The report launched the modern patient safety movement by shedding some much-needed light on the prevalence of medical errors and preventable deaths in the U.S., spawning many improvements to patient safety over the subsequent two decades. But before the healthcare industry gets too self-congratulatory, we could use a quick reality check. Patient safety remains a persistent global issue that exacts a huge human cost, as well as a financial one, as a recent report from the World Health Organization (WHO) starkly illustrates. While it is estimated that there is a one in 3 million risk of dying while travelling by airplane, the risk of patient death while receiving healthcare due to a preventable medical accident is estimated to be one in 300, according to the WHO.