How Artificial Intelligence is Transforming Healthcare
AI technology and ambient intelligence are rapidly transforming healthcare as we know it. From predictive analytics and personalized medicine to AI-assisted diagnosis and treatment, artificial intelligence is making huge strides in improving patient outcomes and saving lives. In this article, we'll explore some of the ways AI is making a difference in healthcare and look at some of the challenges that still need to be addressed.
Benefits of AI in Healthcare
One of the key benefits of AI in healthcare is its ability to make data-driven decisions. By analyzing large amounts of data, AI can identify trends and patterns that human beings would take longer to identify, or miss altogether. This robust data analysis allows clinician and other healthcare professionals to spend time more efficiently and make more informed decisions about treatment options and patient care.
An example is how AI is being used to develop better diagnostic tools. By analyzing images and other data from patients, AI-powered diagnostic tools can often identify diseases and conditions more accurately than human providers can. This is particularly important for cancers and rare diseases, the latter of which can often be difficult to diagnose or detect.
More robust systems are able to analyze the diagnostic information and couple it with AI analysis of patient medical history and past data from tests and scans to identify the best treatment options. Rather than looking at patient information in a vacuum, AI is able to aggregate all of the available information from that individual patient, as well as population data, and use it to help make more encompassing care decisions. It can also help clinicians plan treatments, monitor patient progress, and warn them about any potential problems. In some cases, AI can even be used to carry out treatment itself — either through AI-powered robots that can assist during surgery, or systems that automatically dispense medications.
Drugs and Treatment
AI is also being used to help develop new drugs and treatments. By analyzing data from clinical trials, in some instances AI is able to identify patterns that might suggest new uses for existing drugs, or new ways of treating diseases. In other instances, AI is used to screen vast numbers of compounds and test for effectiveness against certain diseases.
In 2021, researchers at the Mayo Clinic used AI simulators to virtually screen 30 million drug candidates that were thought to be effective against COVID-19. The AI was quickly able to narrow down the list of viable candidates by modeling the effects of those compounds, and eventually produced a list of just 25 candidates that were believed to be effective against the virus.
Recommended Reading: Ambient Intelligence in Healthcare — The Future is Near
Healthcare, Data, and AI
As mentioned, the viability of artificial intelligence in healthcare relies almost entirely on what data is available. Fortunately, healthcare is one of the most data-rich industries in the world. As such, AI is uniquely positioned to help unlock the value in all of that data and use it to improve patient care, increase transparency, optimize workflows, help develop new drugs, and much more. In essence, AI can help to make healthcare more personalized, more effective, and more efficient, and can also help make care more accessible and less expensive.
AI won’t solve everything
In general, it is critically important to remember that AI is designed to be an assistant — and help clinicians and care professionals consider variables and arrive at conclusions faster. In no world will AI totally take the place of the care provider, and in no world is AI a panacea. Artificial intelligence will not solve all of healthcare's problems overnight and it will take a massive amount of effort on behalf of engineers and technologists to design and fine tune AI systems in a way that is employable, efficient, and as-promised.
Additionally, there is valid hesitancy among the widespread implementation of AI, and discussions regarding the dangers of algorithmic bias and how emerging systems could exacerbate care disparities among women, people of color, and other historically marginalized groups. But if these systems are designed by diverse, thoughtful teams with detailed intention, there is confidence in the healthcare community that AI will do a great deal to improve patient outcomes.
Vocal Biomarkers and Mental Health Diagnosis
What is often less discussed in the conversation of AI in healthcare is the ability of artificial intelligence to actually improve the lives of care providers around the world. Clinicians are burdened with an ever-growing administrative load, which can include tasks such as data entry, medical documentation, and patient correspondence. This administrative load is growing year after year, and is the primary contributor to burnout, depression and suicide among clinicians in the United States — a phenomenon that goes largely underreported.
Thanks to developments in AI and speech recognition, as well as growing data supporting the efficacy of depression biomarkers, there is increasing faith among experts that AI may some day have the ability to actually diagnose and detect depression based on speech patterns among both patients and providers.
Studies show that certain vocal biomarkers such as hesitations, pauses, overuse of filler words and unique linguistic patterns may all be associated with depression. AI has the ability to analyze large volumes of data and find these subtle signals and in turn help both patients and providers get the help they need before symptoms of depression worsen.
While flagging potential depressed symptoms among patients may be valuable, flagging the same symptoms for a provider proves difficult, especially if the provider denies these sentiments (which is likely considering healthcare is the most burned out industry and providers are highly unlikely to disclose their symptoms). Perhaps an easier, less intrusive way to reduce clinician burnout and thus clinician depression is through emerging AI-powered medical scribes like DeepScribe.
AI-Powered Medical Scribes and Documentation Automation
DeepScribe uses a combination of machine learning and natural language processing to provide clinicians with a robust AI system that is able to extract medical information from a natural conversation between a patient and a clinician. From there, the AI creates a complete medical note that syncs directly with the provider's electronic health record system, so all the provider has to do is review the documentation and sign off at the end of the day.
While DeepScribe cannot yet offload all of the administrative burdens placed on clinicians, it is capable of automating EHR-related medical documentation, a task that providers spend 6 hours a day completing.
If you're interested in learning more about how DeepScribe can automate medical documentation at your practice, consider reaching out to us. We've helped provider's save up to 3 hours per day on their documentation, allowing them to refocus on the patient, themselves, and the things in their lives that matter most to them. We're on a mission to bring the joy of care back to medicine.
.jpeg)
