The past decade has seen rapid improvements in Artificial Intelligence (AI) technologies. Driven mainly by advancements made in the field of Machine Learning. A branch of AI that stems from the idea that systems are capable of learning from patterns and making decisions with little to no human intervention. These systems have allowed researchers to make great strides in fields that heavily involve data science and statistical prediction.
Healthcare is a field that affects all of us. Running a healthcare system can often feel akin to performing a circus juggling act. There are a great many moving pieces involved, from clinics to testing kits. That all need to be in the right place at the right the time in order to keep it all balanced. The importance of the healthcare industry means that any issues that arise in the supply chain need to be tackled with the best and most innovative technologies available to ensure that things are kept running smoothly, something a great many healthcare systems have already taken note of by using AI powered emr systems to ensure everything is where it needs to be for healthcare to de delivered to the masses.
Supply chain is not, however, the only area of healthcare that can benefit from AI. Providers have also begun implementing AI systems capable of using electronic health data to predict diagnoses and promote health equity. Rather than replacing physicians, these systems serve as decision support through their ability to process large quantities of information, that would otherwise take physicians a long time to sift through.
Healthcare is an important and hot-button topic that affects virtually every living human being. This critical need calls for the best and most innovative technology available, consistently adapting to the changing state of healthcare. Using electronic health data and artificial intelligence technologies, hospitals are using technology to predict diagnoses and promote health equity among several other uses.
AI in Healthcare
When it comes to healthcare the potential uses for AI are just as limitless as they are in any other field. Let’s take a look at a few ways, how healthcare technologies has implemented AI, so far.Machine Learning
Machine Learning is an AI process that allows for more accurate model building. Letting computers make predictions with an accuracy greater than ever before. Isn’t it already obvious how useful this technology could be in healthcare? While EHRs have already improved on the pen and paper processes of old. Machine learning holds the potential to move us another step forward. The analysis tools already utilizing this tech can analyze medical data, providing doctors with better and more accurate information. Than they’ve ever had before.The benefits are already being felt, with Google having developed a program that can identify cancerous tumors on mammograms, and Stanford developing a machine learning algorithm that can identify skin cancer. These processes, that would otherwise require large image datasets for doctors to painstakingly examine, lend themselves fantastically to being automated via machine learning.
Administrative Tasks
On to the more mundane side of medicine, let’s discuss how AI can be put to good use in administrative tasks. There’s more to doctors’ visits than exams and tests, all of which is held up by the diligent work of the supporting admin staff. Forms and calls take up most of the day for these staff, which is time that might be better spent elsewhere.Why not facilitate that by using AI automation? There are plenty of tasks that are simple and repetitive that don’t need constant human supervision. AI can:
Respond 24/7: While it may still be some time before AI can hold natural conversations over the phone. The technology we have today is more than sufficient to handle text-based chat, either on the internet or via SMS.
Appointment Scheduling: AI can study patient behavior patterns to better identify when a patient might be a no show, and adjust the schedule accordingly. For patients who have been waiting for an appointment, it can automatically reach out to them when time is available. Once the initial visit is concluded, AI can then automatically schedule follow-ups.
Best Practices: Increased usage of AI for administrative purposes will allow the software to learn and improve and develop a set of best practices for how it should behave. For example, AI can analyze data collected on how patients respond to reminders at certain times to find out when the best time is for sending reminders.
Diagnosis and Treatment
While AI is not yet advanced enough to be trusted to handle the entire diagnosis and treatment process. This can be a tool that helps physicians make better decisions. With many patients coming in with more data than the human brain can process effectively. AI can be used to streamline the process of identifying the illness. After the diagnosis is complete. AI can then analyze what treatments have worked for similar patients with similar conditions in the past. So, it can suggest treatment plans with the highest likelihood of being effective.Supply Chain
The past few years have seen a global disruption in supply chains, due to the COVID-19 pandemic. In response to this, companies have been developing AI powered tools to help manage their supply chain issues. To do their best at avoiding the worst of the problems. While none of these solutions are perfect. It will likely be years before supply chains across all industries are once again. Operating at pre-pandemic efficiency levels, they have helped to alleviate some of the strain being placed on the economy. The very same tools can also help make healthcare more efficient.By modeling available resources and the dependencies on them across an entire health care system. AI powered tools can efficiently and effectively allocate those resources in a manner that maximizes the value. In simpler words, these tools can help our health care systems make the most of the resources they have at their disposal. A feature which has benefited many over the course of the pandemic. Moreover, these systems can also identify gaps in service, predict disruptions. Allowing health care systems to accurately model the effects of certain changes without actually needing to implement said changes.
Conclusion
While many of these technologies are still in their infancy. It is already clear that they can positively impact healthcare for years to come. With greater investment, research and development. This highly probable that an AI-powered revolution in healthcare is not too far away.About the author:
Summer Larson's background in healthcare stretches for over 7 years. He is well-renowned health IT writer who contributes regularly to popular blogs and websites. She covers topics ranging from health reforms to the application of IT in healthcare. He is always looking for opportunities to post informative and user-engaging content on high-quality websites.
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