By: Jake Reedeler, Contributor
AI is everywhere these days. From organizing your work week, to figuring out what you want for lunch, AI systems and advanced algorithms are simplifying daily tasks and making old processes more efficient. But, how far will AI really go?
In the next few years, it’s expected that AI technology will be the biggest disruptor in the healthcare industry. New technology is making it easier for doctors to diagnose patients, reach out to those in hard to serve areas, and even predict a patient’s next medical issue.
Still, for all the improvements AI is set to make in healthcare, there are some issues that remain unresolved. One doctor recently caused outrage when he told a patient, via video link, they were going to die. Imagine what the response will be like if AI systems become the ones informing patients of a critical illness.
Whether it’s for the better or worse, AI tech is here and is becoming a huge part of the healthcare system. We take a look at the big changes about to happen, and we’ll let you decide on whether these developments are positive or negative.
1. Expand access to under-served areas
In places where there is limited access to doctors, there is an even bigger problem with access to other healthcare professionals. In many developing countries, and rural areas, access to radiologists, ultrasound technicians, and other specialist professionals is almost non-existent.
While some under-served groups need to travel hours for an ultrasound or x-ray, others have no access at all. They may experience severe medical issues from problems that could be easily diagnosed elsewhere.
AI technology will soon be able to fill in some of the gaps in diagnostics. For example, AI imaging software is able to scan x-rays for signs of tuberculosis and other diseases. And while its accuracy is still not perfect, it is comparable to an actual human professional.
As the technology advances, many hope that quick access to diagnosis and treatment will be available.
2. Intelligent medical devices and machines
A typical ICU patient is attached to multiple monitoring machines. While the technology is improving quickly, there is still a lot of reliance on constant monitoring, and human to human communication in the event a monitor shows a problem.
New integrated tech can consistently monitor the outputs from all of the individual devices an ICU patient is connected to. By constantly analyzing all of the data, from multiple sources, the machines can accurately identify deterioration.
Once the machines identify a problem, the integrated system will alert all team members to the situation, while providing the relevant data. This cuts down on time, and saves crucial seconds, when ICU patients need emergency care. Instead of nurses getting the alert, reading the monitors, then alerting the physician, nurses and physicians now receive automatic updates on their smart devices and can take immediate action.
3. Ease and integration of healthcare records and administration
Electronic Medical Records (EMR) are already benefiting healthcare professionals. By providing searchable access to health records, which can then be easily shared between medical centers, EMRs are allowing doctors and medical professionals to more effectively diagnose and track patients.
But, this improved access to patient data comes with a cost: data-entry. System users spend more time entering and sorting through clinical documentation than accessing the data.
By integrating new voice recording and recognition software, as well as natural language processing tools, EMRs are becoming more efficient. Much like how Alexa can listen to your music request, then find the exact song you want, new AI tech will allow doctors to dictate their search request, easily navigate to the information they need, then dictate back their documentation and patient notes.
Other medical professionals will be able to do the same, which will improve the speed of patient testing and clinical reporting.
This integrated technology greatly speeds up the process for all medical professionals and improves access to a patient’s data. And, in emergency situations where time is a major factor, this could be extremely useful.
4. Turning medical records into risk predictors
Once medical records are completely integrated and accessible to multiple medical professionals’ input, where do we go next? All of that data is a gold mine for analytics programs. And, while there are still a huge number of issues to overcome, big data analytics teams are creating systems to accurately predict patient risk.
To simplify, a data analytics AI, when applied to EMRs, can sift through thousands of patient records, and find the ones that are similar to a specific patient the doctor is currently examining. And while this helps with diagnosis (which comes up next), it can also be used to predict future health issues.
Doctors will be able to accurately tell patients that they need immediate testing because they are very close to having a stroke, or a myriad of other issues.
Again, there are still big hurdles in analyzing the data, but one day, doctors may be able to predict life threatening issues months before they can be detected by traditional testing methods.
5. Fast and accurate diagnosis
While WEB MD was probably a horrible precursor to this next point, the AI technology behind data-driven diagnoses is becoming more accurate everyday.
In 2016, IBM’s Watson computer was able to accurately diagnose a patient whose illness had been baffling doctors for months. This technology could increase efficiency in hospitals significantly.
While doctors and other medical professionals will always be necessary to understand the entire patient story and administer treatment, bedside AI diagnosis can quickly compare millions of patient records and look for similarities in seconds. Instead of spending hours on research, medical professionals can quickly access a list of most-likely diagnoses, and then evaluate which one most accurately fits the specific situation.
This technology can reduce ER wait times and improve workflow efficiencies for many healthcare professionals.
6. Increased health monitoring
For at risk patients, and those suffering from multiple conditions, keeping track of health factors like diet and medication can be incredibly problematic.
New advances in AI can help patients track their diet and exercise needs, and also keep them on their medication schedule. The system will keep a record of all this information, and create easy to access reports for the patient’s primary care physician and even the patients themselves.
With future developments, the system may even be able to make adjustments on behalf of the primary care physician. For example, the system could send nutritional advice updates based on the patient’s current blood sugar levels. Or, the system could inform a pharmacist and physician that the current dosage of medicine is negatively interacting with another prescribed medication.
Most of these developments are still a long way off, but increased health monitoring is already helping patients to accurately manage their own healthcare needs.
7. Increased precision
It’s estimated that 70% of all medical decisions are based on a pathology result. The more accurate these pathology results are, the more quickly doctors can get to the correct diagnosis.
New imagine analytics software can zoom in to the pixel level on extremely large digital, pathology images. The imaging software is programmed to look for signs of cancer, or other illnesses, and can detect incredibly small anomalies within minutes.
This digital analytics tool has already shown progress in flagging breast cancer from images up to 10 gigapixels in size. The huge scale of the image would make it difficult for even the most experienced human eye to catch. And even then, analyzing an image that size would take hours of their time.
Saving time and lives, this increased precision is set to make a huge impact on the way healthcare is provided to patients.
The key to successful AI integration is solving different technical problems yet to be worked out in the systems above. They still require a lot of testing, and there will always be room for human input and error. But, with the right integration, AI can reduce burnout for medical professionals, and increase patient safety and care for those who need it most.