The healthcare field has taken to artificial intelligence in a big way. Patient, pharmaceutical, and other medical data are aggregated and stored electronically. Electronic health records (EHRs) in particular have seen an enormous growth in popularity thanks to their ease of use and integration with clinical support tools. These support tools provide medication dosage guidance, drug interaction alerts, pop-up warnings of unsafe practices, and more.
Clinical support tools also include database maintenance and tools to target population trends so that patients at risk of complications can be reached quickly.
EHR—also called EMR, electronic medical records—data includes financial, operation, and genetic information that doctors use to determine the best patient treatment options.
With fast improving AI technology, clinicians from different institutions can access the same EHR data. As a result, patient care becomes more efficient and resources can be better utilized.
In the same vein, pharmacists and pharmaceutical companies use EHR data to identify patient needs to develop products and medicine that people need.
Of course, those in management positions use the healthcare and pharma data to optimally manage their institutions.
With improved AI and machine learning programs, the opportunities for the healthcare industry only grow.
Some machine learning models already widely used include Disease Identification/Diagnosis, Drug Discovery/Manufacturing, and Personalized Treatment/Behavioral Modification.
The Disease Identification/Diagnosis model doesn’t need any explanation nor does Drug Discovery/Manufacturing. However, Personalized Treatment/Behavioral Modification does.
Personalized treatment based on individual health data paired with predictive analytics leads to more effective care and more patient satisfaction. For this reason, researchers focus on this aspect of patient care, as well. At this time, personalized treatment works with supervised learning algorithms, where physicians select from a limited set of diagnoses based on patient symptoms and genetic information.
Further developments in the healthcare field pertain to Drug Discover/Manufacturing. The use of machine learning in preliminary drug discovery has the potential for various uses, from initial drug compound screening to clinical trials.
Currently, developers are looking for external data to improve their AI models. Social media data and information on doctor visits in addition to genetic data on specific populations provide information that would immensely improve healthcare and pharma data. Even better, including this data results in smaller and less expensive trials.
Machine learning algorithms also help the fields of radiology and radiotherapy, identifying cancerous tissues efficiently and quickly.
Newer ML programs include Smart Electronic Health Records and Epidemic Outbreak Prediction.
Smart Electronic Health Records classify documents (including patient email) with support vector machines and optical character recognition (to translate doctor handwriting into readable form).
Epidemic Outbreak Prediction models monitor and predict disease outbreaks around the world. They use satellite data, real-time social media updates, historical data, and more to predict outbreaks.
Remedy Health Media enables non-physician staff to help patients suffering from chronic illness from their phones. Patients and clinics find this personalized service invaluable.
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BioSymetrics provides a data-processing and data-analytics platform that can process the huge amounts of data generated by medical devices (EEGs, MRIs, etc.). With this, clinicians and others in the healthcare field (pharma companies, scientists, etc.) can diagnose patients and discover new drugs.
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