Report: EHR Data and Machine Learning Techniques can Boost Value in Radiology

Report: EHR Data and Machine Learning Techniques can Boost Value in Radiology

Machine learning can extract the data available in cloud-based electronic health record (EHR) systems and help radiologists demonstrate improved quality.

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Today, many healthcare practices are looking to integrate their electronic medical record system (EMR) with their Picture and Imaging Archiving (PACS)/Radiology Information System (RIS) model to enhance workflow management and patient satisfaction. Radiology transcription companies play an important role in this scenario by providing services to integrate and import transcripts directly into electronic health records (EHRs) and/or RIS systems. A new Health Data Management report says that machine learning in radiology can extract the data available in cloud-based electronic health record (EHR) and other systems to help radiologists demonstrate improved quality and more value.

How Machine Learning bring More Value to Radiology The report describes several ways in which machine learning can bring more intelligence to radiology: •

According to an expert, machine learning can use EHR data to perform predictive analytics. Machines perform complex cognitive tasks much better than humans and have great potential to increase diagnostic accuracy, predict prognosis and, thereby improve patient outcomes.

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Machine learning algorithms in radiology can detect pulmonary nodules, diagnosing polyps and screening for breast cancer.

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Using a machine learning algorithm on a very well-defined problem of a brain tumor called glioblastomas, Mayo Clinic is able to predict which genetic variation the glioblastomas are. This allows physicians to provide the right treatment. Unlike machines, radiologists cannot identify glioblastomas by looking at images of the tumor.

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Machine learning could be used to examine EHR data, financial data, measurement of outcomes, and identify patterns based on individual providers or groups of providers.

Experts say that this will allow algorithms to detect each provider’s contributions to care.

Radiology Transcription Services – Accurate Data for EHRs Machine learning can achieve its full potential only with accurate data extracted from EHRs and other silo datasets. It could be used to examine patients and correlate them with their lab values, genetic profiles and diagnostic images to find patterns that help physicians

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establish a prognosis. High quality EHR-integrated radiology transcription solutions are critical for ensuring the value of cloud-based electronic health records databases.

Experienced medical transcriptionists provide error-free radiology reports of operative reports and procedures notes for a wide variety of imaging techniques. Knowledgeable about the terminologies and procedures associated with the specialty, they can ensure high quality EHR-integrated reports for •

X-ray radiography

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Ultrasound

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Bone scans

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Computed tomography (CT)

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Positron emission tomography (PET)

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Magnetic resonance imaging (MRI)

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Mammograms

Experts say that machine learning in radiology and other areas in healthcare still has to be validated. However, once the fundamental framework is established and economic and IT barriers are overcome, the data available in EHR, PACS and RIS systems can support machine learning in dramatically improving the ability of physicians to improve the quality of care.

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