Artificial Intelligence in Healthcare: Key Trend in Digital Transformation
Artificial intelligence in healthcare is no longer a thing of science fiction movies. Instead, it is very much a part of the health industry. Artificial intelligence or AI is a key trend in digital transformation in healthcare, changing the way practitioners and patients manage care. From frontline tasks like AI-aided surgery to administration and even research, AI plays an increasingly important role.
Understanding artificial intelligence
Artificial intelligence puts machines in control of tasks that were previously managed by humans. That doesn’t necessarily mean that machines are doing whatever they want, though. In most cases, AI tasks and devices are still programmed and controlled by humans who direct how machines do their jobs.
In healthcare, specifically, artificial intelligence works in a few ways. In machine learning, for example, it’s used for analyzing large amounts of data and generating results with predictive analysis. Artificial intelligence in healthcare looks at information and finds patterns, supported by data to help healthcare providers and researchers make better decisions.
Artificial intelligence in healthcare is also used for diagnostics, drug discovery, pain management, clinical trials and overall improving patient outcomes. We will explore more of these AI applications.
Investment in AI healthcare
Artificial intelligence in healthcare is doing very well on the market. One research company’s report found the industry will be worth $120.2 billion by 2028. They attribute the potential success of artificial intelligence in healthcare to technological advancements and the need for efficient, innovative solutions to enhance clinical and operational outcomes.
As the report says, there is a lot of pressure to cut down healthcare spending, while the cost of delivering care is growing very quickly. AI technologies in healthcare help reduce expenses by improving the delivery of care and clinical outcomes. The report also adds that investments in AI technology are driving the market due to rising numbers of patients with chronic conditions. At the same time, there is a shortage of public health workers, which means AI has a role to fill.
Artificial intelligence in healthcare – pros and cons
When machines take over from humans, there are always savings in time, cost, and labor. Much of what we as humans do is repetitive and needlessly manual. If a machine can automate these tasks, including in healthcare settings, nobody draws a salary, there are fewer errors, and it all gets done faster.
This also improves quality, and consistency. By taking human error and fatigue out of the equation, artificial intelligence in healthcare reduces deaths and other complications. This improves the overall health and wellbeing of patients and the population using the medical system.
Of course, one of the biggest benefits of artificial intelligence in healthcare is the access to insights from data and machine learning. With AI, medical systems can predict trends, prevent outbreaks and other issues before they happen, and do better for patients. The healthcare system is collecting this data anyway, but there’s no comparison to how AI can use it to improve patient care and outcomes.
In our examples of how this technology is used, you will see how artificial intelligence in healthcare makes care easier to access, more affordable, and more efficient. There are also some challenges that go along with using artificial intelligence in healthcare. Top issues include informed consent to use artificial intelligence in healthcare, safety, and data privacy.
How can medical practitioners ensure that their patients know how AI is being used, and determine how much they need to know? Do clinicians need to let patients know that AI is being used in terms of informed consent? These are the ethical questions that come along with using artificial intelligence in healthcare.
Particularly when AI is used for guiding diagnostics and treatment, safety will always be a concern. Practitioners need to know and trust that their AI system is working correctly to give appropriate and safe recommendations. This means that any data used to train or operate the AI system needs to be accurate, and that healthcare systems using AI are transparent about any safety issues.
Finally, as with any service or product that involves personal information, patients will have concerns about data privacy. Health data is valuable, especially for bad actors, and healthcare systems need to continue to take every precaution in securing access to this information. There are also further concerns about patient data becoming part of the large datasets needed to run machine learning.
It is clear that quality oversight is very important when integrating AI into healthcare. There still needs to be humans in charge, monitoring systems and data and ensuring patients understand their rights and roles.
Examples of artificial intelligence in healthcare
Sometimes the best way to understand how something works is to look at real-life examples. We have gathered some use cases of artificial intelligence in healthcare so you can see how these systems and platforms are already impacting patient care. This is only the start of what AI in healthcare can potentially accomplish.
Automating Administrative Tasks
As the saying goes, time is money, and in healthcare, time is also directly related to quality of patient care. The faster things work — without sacrificing quality — the more patients can be seen, and the more effective and efficient the system becomes. Artificial intelligence in healthcare, used to automate administrative tasks, is one very effective way to streamline the patient experience.
Olive is one AI platform doing exactly this. Built specifically for healthcare purposes, it meets industry regulations while optimizing healthcare workflows. Olive can handle areas like managing prior authorizations, issuing insurance denials or rejections, managing vendor contracts, processing invoices, handling inventory, and sending reports. One case study shows that in 22 days, it saved $340,000 for a large health system, along with the equivalent labor of seven full-time staff members.
Robots, Chatbots and Virtual Health Assistants
Often when a patient just needs a small bit of help or a simple piece of information, they don’t actually need to connect with another human. However, without an automated system to deal with simple requests, the patient is stuck waiting for customer service. And, on the healthcare side of things, health staff have to routinely answer phone calls, emails, and messages that could be easily managed with AI.
Chatbots and virtual health assistants step into this role easily. Babylon Health, for example, provides patients with simple healthcare on their devices, 24/7. With an AI-powered symptom checking chatbot, Babylon helps patients understand what might be happening with their health, and what to do next. Systems like this often integrate with regular clinical care, allowing patients to book appointments or access health information without having to make a call or send a message.
The self-service options give patients more control over their health, and better access. Even if it’s late at night or they are far from their usual care provider, they can get peace of mind, and a plan of care.
What about robots? They are often used in clinical settings, assisting in surgery and diagnostics. Equipped with cameras and micro-instruments, robots get to places that are a challenge for practitioners to reach, with accuracy and precision.
Precision Medicine and Medical Imaging Diagnostics
Robotics, as mentioned above, are a great example of precision medicine made possible with AI. With machine learning and advanced sensor technology, medical devices can better treat impacted areas of a body while leaving healthy tissue safe. This improves recovery time and overall health.
Used for medical imaging diagnostics, AI works similarly, analyzing what it sees to make a diagnosis or otherwise report back to specialists. Medical imaging diagnostics, powered by AI, work in concert with radiologists who then interpret the data. Robotics and imaging are a fine example of how AI can improve medical care without replacing human practitioners.
Data-Driven Clinical Decision Support
AI-based clinical decision support tools analyze vast amounts of data to guide practitioners on their next steps for treatments. Physicians and other care providers may have some experience with past CDS tools that are not integrated as well as an AI-based tool can be. The ideal, AI-enabled CDS tool works with other clinical point of care devices and data to efficiently and intuitively support staff.
These modern, AI-based CDS tools reduce diagnostic errors, in turn reducing mortality rates and the length of hospital stays. For example, Sepsis Watch, a Duke University tool, was developed to trial a way to combat a condition with a 28 percent mortality rate. The program successfully flags patients who are at medium to high risk of sepsis, based on data points, which is then communicated to nurses and physicians to treat.
Again, AI-based technology in health care is not there to completely replace physician care, but to augment it. A CDS tool like Sepsis Watch still requires physician oversight to confirm the diagnosis and begin treatment. The AI tool exists to analyze data and make suggestions, which healthcare teams can then guide as they see fit. Similar tools exist for the steps beyond diagnosis, with data-driven AI systems offering solutions for treatment as well.
Drug Discovery, Trials, Genomics, and Preventative Medicine
AI capabilities for drug discovery are very exciting, given how expensive and time consuming development typically is. Back in 2007, a robot named Adam hypothesized gene function in yeast, after looking through billions of data points. This heralded an era in which AI can test more compounds, with greater efficiency and accuracy, than human counterparts. The result is AI-enabled technology, poised to search for new compounds and analyze how existing drugs can be used in novel ways.
When drugs and treatments are ready for clinical trials, AI like Atomwise predicts bioactivity and identifies patient characteristics in advance. Finding the ideal candidate for a clinical trial is a better use of resources, increasing the chances of success and speeding up the time to market. These AI systems can screen genetic compounds by the millions each day, delivering results much faster than conventional methods.
With newer drugs, better suited to patients and coming to market faster and at less cost, preventative medicine is also enhanced with artificial intelligence in healthcare. Instead of having to wait until a condition develops or becomes bad enough to treat, patients identified with AI-enabled genomics studies can access proactive support.
Wearable Healthcare Technology
Wearable healthcare technology is another exciting digital development, so it only makes sense to combine this and AI. Researchers are applying artificial intelligence machine learning to the data that is retrieved from wearable medical devices. The results funnel into the clinical decision support, diagnosis, and treatment tool mentioned above.
The combination of AI and wearable healthcare technology is a logical one. AI uses data to learn and to guide its human counterparts, and wearable healthcare tech exists to collect data. By analyzing information like aerobic responses, AI-powered wearable tech can potentially diagnose chronic disease.
Wearable healthcare technology is not as far developed as many of the other use cases for AI. The potential is there, however, and with artificial intelligence in healthcare poised to grow substantially in the next decade, it could be the next big thing.
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