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The importance of efficiency, speed and productivity in health care delivery is indisputable. However, while the utilization of Electronic Medical Records (EMR) aims to support Doctors in meeting these demands, it’s yet to optimize their productivity in a significant way. This is often a priority for the very healthcare system, as extended healthcare delivery times translate into higher costs for patients moreover as physician burnout and job dissatisfaction. uses hands-free speech recognition and computer science to assist reduce time consuming manual clinical documentation, saving physicians to put their focus where it should be – on their patients.

Electronic Medical Records (EMR) were intended to enhance the standard of clinical care and workflow. However, most physicians dislike using Electronic Medical Records (EMR) – studies indicate that physicians spend two hours documenting and managing Electronic Medical Records (EMR) for each hour spent with the patient. Furthermore, many healthcare organizations have hired staff dedicated to Electronic Medical Records (EMR) data management, increasing costs and impacting patient privacy. addresses this by analyzing the automatically recognized clinical dialogue speech in near real-time.

A narrative and structured data output are simultaneously generated for the physicians within the Electronic Medical Records (EMR) program and data analysis within the back-end. Not only does enable Doctors to significantly reduce their Electronic Medical Records (EMR) use and practice medicine the way it was meant to, but it’ll also unlock the large potential of knowledge analytics in healthcare. AI-based Speech-To-Text technologies can help ease these pressures by minimizing much of those administrative tasks.

Electronic Medical Records (EMR) solutions embedded with an AI layer can document patient problems, diagnoses and procedures in compliant formats through voice-based commands. These smart Electronic Medical Records (EMR) solutions make it easier to seek out specific patient information and even help physicians convert their narratives into actionable information for real-time deciding. Patient data has to be easily accessible to providers for faster diagnosis and decision-making. Moreover, it should be clear and straightforward to read for physicians to interpret the information accurately. However, sorting through large amounts of Electronic Health Record (EHR) data and picking the bits that apply to a patient’s condition may be a huge challenge.

AI-enabled Electronic Health Record (EHR) systems allow physicians to rapidly access, extract and electronically export patient data with minimal error. For example, HCPs extract data from clinical documents using AI-enabled cloud-based Electronic Health Record (EHR) at one medical. can review provider notes and extract structured data, using AI to acknowledge key terms and reveal data insights.

The humongous data created by Electronic Health Record (EHR) systems lends itself well to advanced AI and machine learning tools to uncover patient insights, predict high-risk conditions, and enable more personalized care. For example, solutions help predict hospital readmissions, patient mortality and risk levels, sepsis, hospital-acquired diseases, patient deterioration, among other potential dangers. AI solutions that learn from new data and enable more personalized care are being developed. For example, various doctor’s teams are teaming up with healthcare networks to develop prediction models using big data to produce HCPs with alerts about serious conditions like sepsis and failure. also uses AI-derived image interpretation algorithms to extract health insights and trigger real-time actions supported by it.

Post Author: msood

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