Machine Learning: How it Impacts Electronic Health Records
Like it or not, but the truth is that technology is everywhere. It is now an integral part of our lives, ultimately transforming how we go about doing, well, pretty much everything. And the impact of this change has been especially profound as well as intriguing in the healthcare industry. The increased adoption of modern technology in the healthcare world has resulted in some high-level and intense changes that, in turn, have helped healthcare providers deliver substantially better care to patients. And one of the most crucial ways it has done so is Electronic Health Record. You see, the inconsistency and lack of standards and quality inpatient record translated into a decline in treatment provided to patients.
However, with Electronic Health Records, the industry gained a terrific means to maintain virtually seamless records, including extensive details about their ailments, treatment history, diagnostics history, and more. Now, this is great, but the truth is that given the quantity of data that healthcare companies have to deal with, a need for tools that allowed them to leverage it better was felt. And the solution was then promptly found in machine learning — in the union of electronic health records and machine learning, to be precise. ML can glean the most amount of data possible from every possible source and then leverages it to achieve insights to drive a world of new benefits. Some of these benefits have been listed below.
1. Tailored treatment plans: As more and more hospitals and healthcare organizations across the globe, we now have access to more data than ever before. Machine learning is then able to use this abundance of data to help healthcare providers to understand patients’ issues better. Not just that — it can also help doctors identify and foretell any risks to the patient based on their current symptoms, family history, medical history, identified allergies, and more. It can also be used to help establish better behaviors amongst patients.
2. Enhanced diagnosis: Deep learning EHR systems, though newbies in the medical order, have already proven to be rockstars of sorts. Machine learning and deep learning, together, have helped doctors make a substantially more accurate diagnosis, even ones that would be impossible to detect with traditional diagnostic tools. IBM Watson Genomics, Microsoft InnerEye, and more are some examples of such technologies.
3. Better research for clinical trials: Research has already shown that machine learning-based tools can deliver much value in the context of clinical research as well. For starters, ML-based predictive analytics can be leveraged to improve the efficiency of clinical trials, bring down the scope of errors in data, and more. Additionally, these tools can also be used to determine ideal candidates for clinical trials by analyzing all the relevant data about them.
It has been for everyone to see the kind of positive impact novel technologies such as machine learning as well as tools like deep learning EHR systems can have in the world of healthcare. And if we continue to integrate them into the ecosystem carefully, the benefits we achieve will also grow multifold.
