Other companies working in this area include Concord Technologies, Innodata Inc., and Intellidact AI. Flatiron Health’s human “abstractors” can review provider notes and extract structured data, using AI to recognize key terms and reveal data insights.Īdditionally, Amazon Web Services recently launched a cloud-based service known as Amazon Comprehend Medical, that can retrieve and index data from clinical notes. For instance, at OneMedical, HCPs extract data from clinical documents using Athenahealth’s AI-enabled cloud-based EHR. In fact, 41% of hospitals reported that public health agencies’ inability to effectively receive patient data was one of the major barriers physicians faced during the COVID-19 pandemic.ĪI-enabled EHR systems allow clinicians to rapidly access, extract and electronically export patient data with minimal error. However, sorting through large amounts of EHR data and picking the bits that apply to a patient’s condition is a huge challenge. Moreover, it should be clear and easy to read for clinicians to interpret the data accurately. Patient data needs to be easily accessible to providers for faster diagnosis and decision-making. Automated extraction of patient information These smart EHR solutions make it easier to find specific patient information and even help clinicians convert their narratives into actionable information for real-time decision making.Įxamples of firms that work in this field:Ģ. EHR solutions embedded with an AI layer can document patient problems, diagnoses and procedures in compliant formats through voice-based commands. Automated capturing of clinical notes through natural language processing (NLP) reduces clinician admin work, freeing up more time to focus on patients.ĪI-based speech-to-text technologies can help ease these pressures by minimizing much of these administrative tasks. Not only does this eat into valuable patient time, but also contributes to excessive work–life imbalance, dissatisfaction, high rates of attrition, and burnout. Reducing administrative burden of clinical documentationĪ study by the American Medical Association (AMA) and the University of Wisconsin, shows that nearly 50% of clinician time is spent on admin work, including documentation, order entry, billing and coding, and system security. In this article, we explore how exactly AI is making EHR systems more efficient.ġ. This is a concern for the US healthcare system, as extended care delivery times translate into higher costs for patients as well as physician burnout and job dissatisfaction.Ī slew of healthcare and technology firms have stepped up to address this challenge and more through AI. A US study by the American College of Physicians found that doctors spend an average of 16 minutes per patient on EHR functions across specializations. While the use of electronic health records (EHR) aims to help clinicians meet these demands, it has yet to optimize their productivity in a significant way. The importance of efficiency, speed and productivity in healthcare delivery is indisputable.
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