What is deep learning technology?
Deep learning or deep structured learning, is a form of machine learning which uses algorithmic architecture to analyze data. Deep learning technology helps to analyze data faster and with more accuracy. Deep learning models become more efficient and precise as they process more data, learning from previous results enhances its ability to make corrections. Deep learning can unlock hidden opportunities in clinical data which can facilitate healthcare providers to offer enhanced patient care and hence improve patient outcome levels.
The use of deep learning in EMR Software systems
Electronic Medical Record (EMR) software systems store patient data such as demographics, lab results, and medical history and make it accessible to healthcare providers to aid diagnosis. Through the use of deep learning algorithms Electronic Health Records software systems enhance the rate of correct diagnosis and the time it takes to conclude. The machine learning algorithms use the data which is stored in the software system to identify patterns in health trends and risk factors. With practice, machine learning algorithms get better with the diagnosis the same way as healthcare providers. Once the algorithm is trained it can work and continuously improve in diagnosis and workings. With Artificial Intelligence (AI) there’s no risk of human error and the risk of being tired and being ill is also eliminated.
How deep learning is applied in healthcare?
Deep learning technology uses the data stored in EMR software solutions to answer healthcare-related queries such as reducing the rate of misjudgment in diagnosis and the prediction of outcomes. Deep learning in healthcare is making a profound impact on the healthcare system in the United States by enhancing diagnosis and improving patient outcome levels. Deep learning helps clinicians to analyze data and identify multiple conditions such as;
- Detection of heart problems
- Using image analysis to detect tumors
- Detecting cancerous cells in the human body and diagnosing cancer
- Tracking glucose levels in diabetic patients
- Analyze blood samples
- To find cancer in blood samples
Implementation of deep learning
Physicians are seeing machine learning as a better way to diagnose various healthcare conditions. It’s expensive to implement machine learning in healthcare systems. Fortunately, newer processors that run faster require less energy and machine learning can advance at an affordable pace. Cristina Davis a biomedical engineer and professor predicts that within 5 years clinicians will have access to deep learning to support the diagnosis. Deep learning is the future of healthcare systems having a long-lasting impact on both patient diagnosis and treatment processes. Deep learning also helps in drug discovery by analyzing complete patient history to formulate a treatment plan. Deep learning technology can also be implemented with medical imaging such as MRI and CT scans to examine the disease in the best possible way and also develop an appropriate treatment path for the patient. The predictive analysis feature in deep learning provides an opportunity for insurance companies to detect any medical insurance fraud claims. Deep learning also supports the insurance industry to send discounts to their patients.
Privacy concerns with deep learning
The large amounts of patient data stored in EHR software systems are always at risk. To make sure the system is protected from cyber-attacks the software system should be fully encrypted to keep patients private and sensitive data protected round the clock. The software platform should offer HIPAA Compliance for Meaningful Use to keep the privacy and security of data stored and shared in the EMR software system.
