What healthcare organizations need to know about Emerging Artificial Intelligence Solutions, and how to approach them?
Dear Healthcare Leaders and Professionals,
As more artificial intelligence-powered tools and processes mature in healthcare, organizations are trying to figure out what aligns with their strategic goals. They may be hearing many conversations among their peers or vendors, and they may feel as if they need to make quick decisions, otherwise, they’ll be left behind. They may be hearing claims that seem too good to be true. They may not fully understand what a solution does. It’s crucial that healthcare organizations stay UpToDate when it comes to questions around AI. It’s easy to get distracted by the latest, greatest new tool, but if it doesn’t align with an organization’s overall mission, it may be more of a hinderance than a helping hand.
Healthcare organizations will want to focus on how AI can make operations more effective and efficient, how it can support workers, and how it will improve patient care.
First, what Are the Different Categories of Artificial Intelligence?
AI can be categorized in two ways, based on functionality and intelligence.
The first category classifies AI by system type:
1. REACTIVE
As the most basic form of AI, a reactive machine responds to external stimuli but does not form memories and cannot learn from past experiences.
2. LIMITED MEMORY
Nearly every AI tool used today falls into this category. A limited-memory machine builds on available data to learn and make future predictions.
3. THEORY OF MIND
Hypothetically, a theory of mind machine would identify, understand, and respond to human emotions.
4. SELF-AWARE AI
Also theoretical, a self-aware machine would think for itself and be conscious of its own emotions and desires.
The second category classifies AI by the level of intelligence:
1. ARTIFICIAL NARROW INTELLIGENCE
All current AI tools have artificial narrow intelligence. They are designed to perform specific functions and cannot think for themselves.
2. ARTIFICIAL GENERAL INTELLIGENCE
At this level, the machine would be able to think like a human, perform multifunctional tasks, and make independent decisions.
3. ARTIFICIAL SUPERINTELLIGENCE
Artificial superintelligence currently exists only in movies and books. At this level, the machine would be self-aware and have capabilities that surpass human abilities.
What Are the Main Use Cases of AI in Healthcare Today?
The healthcare industry uses limited memory AI in several areas:
CLINICAL DOCUMENTATION
ChatGPT is the most well-known generative and natural language processing tool. Chatbots powered with similar technology are used to help patients assess symptoms and book appointments and can assist with outpatient monitoring. Providers also use versions of this type of AI for clinical documentation. One example is Nuance’s Dragon Ambient experience and others. The program records and transcribes doctor-patient interactions and writes a comprehensive clinical summary in the electronic health record.
The more technology can handle the administrative work, the more time physicians must practice medicine. Having AI do a portion of the work also minimizes the clinician’s cognitive burden and fatigue. This type of AI improves access to care for patients. Healthcare Organizations will use some of that time to bring more patients through the door.
IMAGING
Imagine 10 million radiologists analyzing a scan, as opposed to just a handful of specialists. That’s essentially how AI works with healthcare imaging. Platforms such as Nuance’s Precision Imaging Network, for example, use AI algorithms to process images and provide suggestions to radiologists. AI may help reduce diagnostic mistakes by detecting anomalies a human might overlook. AI’s advantage is that it can learn from huge amount of data. An AI tool that has learned all the ways a disease can present itself in an imaging study can very quickly derive answers from new imaging studies, and it won’t get tired while doing so, as a human would.
PATIENT MONITORING
Medical providers have been able to expand remote patient monitoring because of wearable devices that use AI to track and analyze data such as blood pressure, glucose levels, and sleep patterns.
Virtual nursing platforms use smart technology to monitor large numbers of patients. The AI tool can be trained to detect potential problems, play automated messages to patients, and alert in-person care teams.
Other organizations are developing AI-driven systems to automate decision support for providers. The goal is to ensure critical patients receive the medications and treatment plans they truly need. This may need projects focused on real-time monitoring of vital signs. The AI tool compares the patient’s data against multiple sources, runs complex math, and makes infusion recommendations for doctors to review.
RESEARCH
It usually takes several years for novel drug therapies to be developed for actual patient use, but generative AI tools such as the NVIDIA BioNeMo Service can speed up the drug discovery process. With this technology, candidate drugs could be quickly generated and tested in a simulation environment, this is made possible by AI and accelerated computing. The cycle to produce drugs will be shorten and make it less expensive to develop drugs for rare diseases.
ROBOTICS
Surgeons use AI-powered robotics for minimally invasive procedures. High volume of laparoscopic surgeries using the da Vinci Surgical System, are performed. And everything happens with great precision.
Where to Start with AI in Healthcare?
Healthcare organizations can take the following approach when looking to incorporate AI in their workflows:
1. Start small. Address a real need in the organization rather than craving for a bright, shiny object. If it’s used for a specific case, there’s a higher likelihood of adoption. And if it’s something that meets a budgeted need and has a calculable ROI, that’s even better because it increases the chance of it getting funded.
2. Think platforms rather than point solutions. There are AI solutions that can do one thing very well. But if organizations want to create a foundation for more AI adoption in the future, they’ll want to adopt a platform approach.
Currently, a few healthcare organizations use AI-powered solutions for administrative tasks and some clinical decision support. Clinicians who see their administrative burdens reduced can reclaim time to focus on their patients and work at the top of their licenses.
Challenges Ahead for AI in Healthcare
For an AI solution to deliver results, it requires large amounts of high-quality, trusted data. As a prerequisite for any AI adoption, organizations will need to have a well-crafted data strategy to ensure that a steady supply of data is available for current and future AI tools.
If an organization’s data strategy is deficient in any way, it’s going to be difficult to generate useful results. An organization’s data strategy also must address considerations around data security and privacy. How much personal or confidential information is going to be required by or even made available by an AI solution? Think about data governance, who has access to data, and what makes the most sense given what it’s trying to accomplish at an organizational level with its data strategy. Security is always going to be top of mind.
What if a patient wants personal information to be removed and does not permit its use anymore? How does an organization handle those types of requests, and how is that going to impact already deployed solutions? Let’s say an organization has trained an AI model using that data. Will it need to retrain the model with that data removed? What about any of the decisions or results that came from older models that used that data? This only increases the scrutiny and concern over handling data, privacy, and security issues.
AI solutions will also require more movement to the cloud. If a healthcare organization wants to try out an AI solution, it can do it via the cloud without having a large commitment to hardware and infrastructure as a testing phase. If the solution is a fit, the organization can either increase its footprint in the cloud or decide to move some workloads in-house. Several cloud vendors are hosting newer AI solutions, especially those connected to generative AI, in their own environments. The expectation is to move more data to the cloud to use those solutions.
Fast, reliable networks are also necessary to use any AI in a healthcare organization. High-bandwidth, low-latency networks are ideal because AI algorithms, especially deep learning ones, need speed and volume. Once a model is trained, any latency from the model to the signal for a clinician who needs to decide on medical treatment could be a life-changing matter.
Ultimately, these three areas will be important prerequisites to consider any kind of AI:
1- A strong data strategy
2- A modern data platform to handle large workloads, which involves cloud computing.
3- A clear user experience and adoption process
4- Data and AI governance are team sports. These aspects do not fall under any one umbrella. They involve IT, compliance, Privacy, clinical, advocacy groups, and more.
What’s Next for AI in Healthcare?
AI solutions should improve the lives of patients and providers. On the patient side, these solutions should make the healthcare journey seamless. On the provider side, they should reduce repetitive administrative work. One area where AI can serve patients is in self-service options. How can patients schedule appointments? How can they receive more personalized care through the integration and analysis of multiple data sources for preventative measures?
For providers, the automation of repetitive tasks will be a major area of focus. How can ambient clinical documentation improve? How can better computer vision support a virtual nursing program? AI solutions that can take cumbersome tasks off the plates of busy clinicians so they can focus on patients will be embraced heartily.
There’s a lot of excitement around AI and its potential. There’s also recognition that if it’s used in the wrong way or gives the wrong information out, it could present a safety issue for patients. AI is not meant to do everything. It will be an augmentative tool for providers and patients that supports healthcare decision-makers. New or improved artificial intelligence-powered solutions are continuing to make healthcare headlines, especially as industry leaders keep being cautious on workforce challenges. Health Systems hope to ease nurses’ documentation burdens with natural language processing support. Other Medical Center researchers have found promising results for AI-powered chatbots in medical diagnoses.
Many experts agree that AI works best not as a replacement but as a tool to enhance care delivery, whether that’s using it to understand and derive actionable insights from the enormous amounts of data that providers collect or focusing on patient logistics to provide a seamless patient care journey back to life outside the hospital.
Supporting Overburdened Clinicians Should be a Priority.
There’s a ton of opportunities just on the workflow efficiency side to really help clinicians. Physicians and nurses are often spending too much time on administrative tasks, cutting short their focus on patients. Automated solutions can help return that precious time. There’s just no way to be able to continue to provide the volume and level of care that is needed to do without having some forms of automation. Because of the complexity, it’s going to require computer vision and natural language processing at the basis of that to really get to the gains that are needed. For example, clinicians need to track the moment a patient enters an operating room, but having employees manually input that data is a high burden. Instead, a computer vision solution should be able to track that movement. If we could just automate some of those very simple tasks, we could reduce the friction for our clinicians and allow them to get back to the bedside care that we want them to do.
What Providers Are Relying on Now, with an Eye to the Future?
Now, organizations are moving beyond throughput to predict patient outcomes. Hospitals have found cost savings and that the average length of stay has dropped. What can we do with the data that we’re sitting on to either improve care or lower the cost of healthcare? Developing voice technologies to support a clinician experience without a keyboard or mouse is a major goal, with security top of mind. From a networking perspective, how can providers let data flow to the places where AI tools can do the best work? The current network bandwidth might not be enough.
We need to look at how to put all those things together: how to build a better network, how to build better data flows. How can we provide a technological framework that makes professionals jobs more efficient?
Some Hospitals are using Amazon Alexa in hospital rooms to support clinicians at the bedside. Patients using Alexa can have their needs directed to the correct department rather than using up a nurse’s time troubleshooting. Robots could be used to deliver supplies to rooms as a major advantage amid staffing shortages. AI is becoming more embedded into healthcare, applications that previously did not have an AI component have evolved. With so much data in a patient’s record, it’s difficult for clinicians to look through it all to deliver the most personalized care. We are looking forward to better automated clinical documentation. Solutions such as Nuance’s Dragon Ambient experience and others have been critical in supporting nursing documentation at the bedside.
The idea of visiting an AI-powered robot for your medical care may still be the stuff of Hollywood movies, but artificial intelligence is far from fictional in the healthcare industry. Providers and researchers widely use AI-powered tools designed to improve clinical efficiencies, prevent errors, and advance treatments. AI is a support and augmentation tool and it’s up to us to harness its power for the good.
Conclusion
AI will revolutionize everything, certainly, patient outcomes, and it will redefine our expectations of clinical efficiencies. But the big challenge with AI is ensuring there is a healthcare-specific code of ethics and a regulatory environment. We need to ensure the use of AI is always safe and clinically proven. AI is not magic. Like everything else, you must test it and run clinical trials to ensure the solutions work in the real world. Although AI is a highly useful tool, at this stage final medical decisions remain in the hands of human professionals.
Do we think this technology will get to a point where it makes the decisions and automatically administers care? Not sure! It is a question for the future. But we think AI can help with everything in between, and that’s where there’s a massive opportunity in healthcare.
Best regards!