In today’s dynamic healthcare landscape, the integration of artificial intelligence (AI) solutions is becoming increasingly crucial. Healthcare organizations need to balance superior patient care with operational complexities. The adoption of AI presents a transformative opportunity, empowering your organization to enhance various facets of your healthcare services.
You may be wondering, “Why should I embrace AI solutions for my healthcare organization?” The answer lies in the numerous benefits AI brings to the table. In this blog, we’ll shed light on a few reasons why healthcare organizations, just like yours, are wholeheartedly embracing AI.
Enhance Diagnostics and Precision Medicine
In the healthcare sector, AI technologies are actively advancing diagnostic accuracy, particularly concerning dangerous diseases. A study published in the National Library of Medicine highlights the extensive utilization of AI in enhancing medical diagnostics. These technologies, proficient in interacting with medical image data, contribute significantly to disease diagnosis and prediction.
For instance, AI’s capability to detect tumors in medical images stands out, providing a crucial advantage in early-stage diagnosis and subsequent treatment. The study emphasizes the pivotal role played by AI-based algorithms in identifying patients who might otherwise go undiagnosed, including rare diseases. This effectiveness opens up abundant opportunities for early intervention and improved patient outcomes.
Personalize Patient Care with Predictive Analytics
Harnessing predictive analytics through AI is revolutionizing patient care, offering tailored interventions, and enhancing healthcare outcomes. Grand View Research reveals that the global healthcare predictive analytics market reached $11.7 billion in 2022, underscoring the widespread adoption of this transformative approach.
Predictive analytics allows healthcare organizations to proactively anticipate patient needs, facilitating personalized treatment plans. This proactive approach enables timely interventions and preventive measures based on individual patient data, contributing to a more patient-centric healthcare model. The insights derived from analyzing vast datasets enable healthcare professionals to optimize resource allocation, reduce unnecessary procedures, and streamline patient care pathways.
Enable Clean Water for Healthcare Facilities
Ensuring clean water for healthcare facilities is a paramount objective embraced by healthcare organizations leveraging AI solutions. AI technologies facilitate the efficient management of water resources, optimizing usage and minimizing wastage within healthcare infrastructure. By actively monitoring water quality and consumption patterns, AI-driven systems enable healthcare facilities to identify potential issues promptly.
Access to clean water is crucial for healthcare organizations, as a lack of it can lead to disease outbreaks, challenging the healthcare industry’s ethos. Your organization should learn from past incidents, such as the Camp Lejeune water contamination, which resulted in severe chronic conditions. According to TorHoerman Law, the incident affected veterans, family members, and workers, causing them to suffer from diseases like cancer and Parkinson’s disease.
There is no question that artificial intelligence (AI) has tremendous implications for the world of healthcare. The list of applications is long. From using machine learning to analyze medical images to facilitating patient communication through chatbots and even using predictive tools to identify high-risk patients, AI has the potential to enhance healthcare systems.
And that’s not to mention all the time AI can save on the backend by automating things like data entry and appointment scheduling, thereby granting healthcare professionals more time to focus on actually diagnosing and treating their patients.
Still, many in the industry have approached this new technology with trepidation. Potential violations of medical privacy laws are a perennial concern for healthcare organizations, and AI—with its seemingly opaque algorithms and its potential susceptibility to breach—can seem like more trouble than it’s worth on this front.
The reality is more complicated. Yes, generative AI does present a risk to healthcare organizations when handled without the proper precautions, as any technology does. In fact, nearly 60 percent of healthcare industry respondents to a recent survey conducted by Portal26 cited at least one GenAI-related security or misuse incident in the preceding six months. But with the right security mechanisms in place, the benefits of AI significantly outweigh the possible downsides.
The problem is that—as the same survey revealed—almost 30 percent of healthcare respondents said their employers lack any guidelines or policies at all surrounding AI usage. Building those guidelines—implementing AI as carefully, and cautiously, as possible—is essential to realizing the true possibility of this technology.
Secure full visibility into your AI systems
At the center of any concern around AI and medical privacy violations is protected health information (PHI). PHI encompasses any information relating to:
The past, present, or future physical or mental health/condition of an individual.
The provision of health care to that individual.
The individual’s payment history.
Feeding PHI into the large language models (LLMs) that are at the foundation of GenAI can pay massive dividends to healthcare organizations looking to optimize their day-to-day operations. But successfully achieving this objective, with a minimum of risk, requires taking an extremely proactive attitude towards precisely how this data is being used.
The key word here is “visibility.” If you are going to be feeding massive quantities of sensitive PHI into your systems, you need to ensure that you are aware of what it is, who is using it, and for what purpose. The need for this is especially acute given the rise of “shadow AI”—i.e., AI-related activities occurring out of sight of those tasked with overseeing it.
Unsurprisingly, 67% of healthcare industry respondents to the Portal26 survey are concerned about shadow AI at their companies. It is a problem that is growing daily—and one that can only be curtailed through increased visibility.
Healthcare has a vibrant startup and innovation ecosystem, but that doesn’t mean everyone shares the perks that come with technological developments. Historically, payors have often been ahead of the game in adopting and benefiting from new tech, forcing providers to play catch up.
But artificial intelligence (AI) is changing the game. A persistent trend I’ve witnessed is the steady rise of providers prioritizing technology – especially AI – to inform strategic priorities and address chronic headwinds such as staff shortages, increasing cost pressures, and slow reimbursement times, to name a few.
As healthcare leaders catch on to the enormous potential of AI to combat thorny issues, AI will take center stage next year, reshape the larger healthcare ecosystem, and begin to even the playing field between payor and provider.
As the end of the year approaches, here’s how I see this playing out in 2024:
Autonomous medical coding will be widespread — if not the norm.
The latest health IT report from Bain & Company and KLAS Research highlights the increasing importance of software and technology. Per the report, 70% of providers think AI will have a more significant impact on their organizations this year vs. last year, and an impressive 56% of those surveyed view software and technology as one of their top three strategic priorities, with revenue cycle management (RCM) coming in at a resounding first place. With many health systems focused on reducing administrative burdens for clinicians and a continued shortage of medical coders, autonomous coding adoption will surge.
Large language models (LLMs) like ChatGPT won’t work as advertised.
There’s plenty of commotion about the capabilities of language models, but they will likely disappoint when functioning as the core of autonomous coding engines. However, they will be enormously valuable in solving smaller pieces and edge cases, pushing coding automation rates to 100% for all the high-volume outpatient specialties.
If you were to believe all the headlines you read about AI in healthcare, you’d probably think that AI will be curing cancer and replacing doctors within the year. I mean, there have certainly been some exciting advancements. For instance, medical teams at MIT and Mass General Cancer Center recently developed and tested an AI tool that was able to look at an image and accurately predict the risk of a patient developing lung cancer within six years.
On the other hand, Elizabeth Holmes, the founder of Theranos, stands as a prominent example of what happens when people blindly believe the hype about healthcare and technology. Her fraudulent claims about a supposedly revolutionary blood testing technology raised concerns about the oversight and regulation of AI and healthcare innovations, and ultimately ended with her being sentenced to eleven-years in prison.
To make the most of AI without getting blinded by the hype, I recommend treating it like any other new technology: subject it to rigorous scrutiny, demand transparency, and emphasize responsible implementation. AI isn’t a magic wand that will instantly cure all ailments or replace the expertise of medical professionals. It’s a tool – a potentially powerful one – but it’s still just a tool.
Which medical fields benefit most?
Some fields of medicine will benefit from using AI more than others. For instance, the field of medical imaging and diagnostics has already seen the benefits of AI. Again, radiology departments can now utilize AI algorithms to analyze medical images such as X-rays, MRIs, and CT scans. These algorithms can identify abnormalities and assist radiologists in making more accurate and timely diagnoses.
Another field that will benefit from AI is drug development in pharmaceuticals. Scientists can use AI to analyze massive datasets of molecular structures and predict potential drug candidates. This is much more efficient than having organic chemists sift through datasets by hand. AI can also expedite clinical trial recruitment by matching eligible patients with suitable trials based on their medical records. So, AI can accelerate drug discovery, reduce research and development costs, and bring life-saving treatments to market more quickly.
Even more human-oriented tasks, such as patient engagement and remote monitoring, stand to benefit from AI. AI-powered healthcare CRM systems can enable personalized patient communication and remote health monitoring. These systems can send automated follow-up messages, answer patient queries, and detect potential issues based on patient-reported symptoms. AI enables enhanced patient engagement, improved adherence to treatment plans, and early detection of health issues. This frees up time for healthcare staff, allowing them to focus on more complex tasks.
As the healthcare industry continues to evolve, the integration of artificial intelligence (AI) has emerged as a powerful tool with the potential to revolutionize the way medical professionals work. Contrary to popular concerns, AI will not replace doctors and nurses; rather, it will complement their skills and make them more productive and effective. One significant area where AI will disrupt and bring about transformative change is the “back office” of hospital operations, where manual and outdated processes, along with fragmented systems, have been wasting countless hours of clinicians’ time.
The potential for AI to streamline and optimize various aspects of healthcare administration is vast. One key advantage lies in dramatically reducing the time physicians spend on researching and keeping up with their Continuing Medical Education (CME) and Maintenance of Certification (MoC) requirements.
Traditionally, physicians devote significant amounts of time to stay updated with the latest medical research and advancements, which can be an arduous and time-consuming task. AI-powered platforms can swiftly process vast volumes of medical literature, journals, and clinical trials, providing doctors with curated and relevant information tailored to their specific areas of expertise. This will not only save valuable time but also enhance the accuracy and quality of patient care.
Healthcare leaders can also leverage AI to offer more personalized employee experiences at scale. By analyzing vast amounts of data on individual clinicians’ preferences, work patterns, and career aspirations, AI can create tailored development plans and support systems. This personalized approach can boost job satisfaction and engagement, ultimately leading to higher retention rates among clinical staff.
Gone will be the days of a one-size-fits-all approach to workforce management, as AI empowers organizations to cater to the unique needs of each clinician, thereby fostering a more conducive and fulfilling work environment.
My father was diagnosed with diabetes when he was very young and lived his entire life maneuvering various healthcare systems. He had multiple eye surgeries, which is common for diabetes patients. He had pancreatic issues, open heart surgery, and two kidney transplants. It was a lot and, as his daughter, one of the most difficult parts of it all was watching my mother be his primary caregiver, managing doctor appointments, medication prescriptions, treatment plans and insurance claims.
At the time, there was little to no technology on the practice management side of healthcare. Until the internet connected us all, EHR systems were contained to inpatient and outpatient facilities, primarily used as data interchange platforms for claims processing with scanning capabilities to create images of documents. I remember my mother carrying a binder from doctor’s office to doctor’s office – the most effective way for her to keep an easily accessible list of my father’s ailments, healthcare conditions, medications, and all the other details needed by the countless doctors across my father’s continuum of care.
How AI is creating more meaningful patient experiences
Fast-forward two decades and we’re now in an entirely new reality with an ever-expanding healthcare technology ecosystem. Medical office software platforms do so much more than claims processing and digital document storage. Interoperable EHR systems let physicians share treatment plans and easily access medical histories and lab results anywhere and anytime. Patients can schedule appointments online and login to patient portals to get test results in real time. Telehealth has ushered in brand new healthcare delivery models and helped drive greater adoption of behavioral health services. Remote patient monitoring (RPM) technologies have been a game-changer for so many, helping patients better manage chronic illnesses.
And now, with the integration of artificial intelligence (AI), we’re seeing the next wave of healthtech – an advancement that is bringing our entire industry closer to the promise of fully connected or interoperable healthcare systems. AI is having a massive impact on medical practice workflow processes, empowering healthcare providers and medical office staff. AI applications are making it possible to automate tedious and redundant tasks so that medical professionals can focus their time on valuable work that greatly improves the patient experience and outcomes – and that’s just on the business side of things.
By Rinat Akhmetov, product lead and ML solutions architect, Provectus.
The use of artificial intelligence (AI) is growing across all sectors, and healthcare is no exception. In fact, AI is particularly well-suited to healthcare applications due to the vast amount of data — from electronic health records (EHR) and clinical trials, to disease registries and claims — that is generated in the industry on a daily basis.
Ophthalmology is one area where the application of AI technology is more than justified. Faster and more accurate, at-scale eye screening can help diagnose and prevent such eye conditions as amblyopia, strabismus, diabetic retinopathy, glaucoma, age-related macular degeneration, and many others. AI holds the potential to improve patient diagnosis, reduce cost per screening, and expand the availability of eye screening to all.
This article explores how AI can be used in ophthalmology. We will consider the benefits and challenges of AI, outline prospective use cases, and offer a framework for adopting AI.
Ophthalmology is ready for AI innovation
Artificial intelligence is beginning to be used in ophthalmology for a reason.
A 2020 study researching the use of AI to screen for diabetic retinopathy, a leading cause of blindness, found that AI was able to achieve an accuracy of around 95%, which is comparable to that of expert human graders. Another study used AI to detect glaucoma, also a leading cause of blindness. The AI system was able to achieve an accuracy of over 90% in detecting the disease.
These studies show that the amount of real-world data is enough to develop highly accurate algorithms that can detect disease as well or even better than humans — in all types of eye screens, and at a speed and scale that exceed human potential many times over.
The potential benefits of using AI in ophthalmology are significant. The improved accuracy and scale of disease detection lead to earlier diagnosis and treatment, which improves patient outcomes. Automated disease screening frees up time for ophthalmologists to focus on other tasks.
However, there are also some challenges associated with using AI.
AI requires high-quality data for training. And while the volume of data is usually not a problem, finding the right talent to prepare it can be problematic. Only professional ophthalmologists are qualified to label training data in a manner that ensures high accuracy on real-world data in production.
There are risks of false positives or false negatives. Some diseases may be incorrectly diagnosed, while others may be missed altogether. Hence, the importance of prepped data, an infrastructure for AI monitoring and re-training, and human-in-the-loop (HITL) for processing user feedback.
Thankfully, AI technologies are developing so quickly that it becomes easier for practitioners to build eye screening applications from scratch, using open-source tools and cloud services from AWS, Google, or Microsoft.
Practical applications of AI in ophthalmology
There are a number of ways in which AI can be used for disease screening in ophthalmology.
One example is fundus photography, which is a type of medical imaging that captures an image of the back of the eye. For instance, AI can help capture and interpret the retinal vasculature, to determine risk or presence of diabetes. Similarly, AI can preemptively reveal pathologies that cause blindness and vision loss by enabling at-scale screening for fundus and retina abnormalities at birth.
Another example is the use of Optical Coherence Tomography (OCT). This is a non-invasive imaging technique that uses light waves to take pictures of the retina. These pictures are processed and analyzed by AI to detect any signs of anomalies associated with disease.
AI can also be used to augment photoscreening applications. GoCheck Kids, a company assisting primary care networks, implements cost-effective pediatric vision screening, and utilizes AI to supplement image analysis and improve user actions, to help ophthalmologists capture the best image possible for further analysis.
The paradigm for AI adoption in ophthalmology
The power of AI lies in its ability to identify patterns and anomalies in data that may be difficult for humans to spot. Nowhere is this more apparent than in the field of ophthalmology, where AI is used for disease screening — detecting anomalous parts of eye screens that may indicate a specific eye condition.
For AI in ophthalmology to work effectively, however, certain conditions must be met.
Any disease screening system or application has to have an image labeling component. AI is a work in progress, a system that evolves over time on new data, and users should be able to label new screens and verify low accuracy screens that were previously taken.
End-to-end infrastructure for AI has to be in place so that models can be built, trained, deployed, monitored, re-trained, and fine-tuned. Any types of data or model drift, or bias, should be monitored and countered by cyclic model updates.
It is better for the solution to live in the cloud. It helps realize such benefits as automatic scalability, high flexibility, and reduced IT costs. It also ensures collaboration efficiency and business continuity. For instance, an eye screen taken with an app by an optometrist in Chicago can be labeled by a highly trained ophthalmologist in LA, with both of them contributing to the improvement of the application’s AI.
Having the right UI matters. Doctors taking eye screens should have access to a section that explains why AI made certain decisions, to better understand the signs of detected abnormalities. The labelers should be able to sort existing screens, and markup and feed new screens to the system. A customer-centric UI ensures that doctors do not have to spend time examining screens with no signs of pathology, so they can focus on patients who need assistance.
The potential of AI in healthcare is immense. From streamlining administrative tasks to providing insights for clinical decision-making, AI can help to improve patient outcomes, increase productivity and efficiency of care delivery, and make it easier for wider categories of the population to access healthcare services.
In ophthalmology, AI-powered disease screening is the future. By automating pattern identification, AI can help to increase accuracy while saving time. It can identify individuals who are at risk of developing, or who already have a certain disease, as well or better than human doctors.
It is estimated that by 2050, over 1.8 billion people will suffer from some form of vision impairment. This number could be reduced drastically if preventable vision loss was detected and treated early on. The way forward is to scale disease screening with AI, to enable doctors to focus on patient care while leaving routine work to the machine.
Artificial intelligence (AI) is a driving force in the future of technology, often enhancing the speed, precision, accuracy and effectiveness of human efforts. As a result, AI has had a tremendous impact on nearly every working industry, including healthcare and its specialties. In recent years, there has been an increased adoption rate of AI in the healthcare industry.
Although this growth may be related to the need for telehealth and other remote tools as a result of the COVID-19 pandemic, recent estimates predict a more than tenfold growth in the market for AI in medical imaging over the next decade.
Although there is a projected growth for AI in healthcare, if this technology cannot be efficiently implemented into existing daily workflows, then these AI tools will not be practical in a real-life setting. Understanding how healthcare practitioners use AI in a clinical setting and how AI can help solve real-life challenges are crucial to increasing further adoption rates. This will ultimately drive innovation around this technology and will lead to improved quality of patient care.
What clinicians want from AI-powered technology
A recent survey by the American College of Radiology (ACR) indicated that 30% of respondents use AI to enhance image interpretations across all modalities. The modalities most commonly identified were computed tomography (CT) scans and mammography scans. When asked what they would specifically like AI technologies to do to enhance their clinical practices, respondents indicated that lesion detection (73%) and anatomic measurements (71%) were most important.
These responses indicate that clinicians are most interested in reducing the need for manual tracing and measurements of medical imaging, which takes a significant amount of time and effort. The survey’s findings also indicate that respondents would prefer additional support, or a “second opinion”, when detecting and identifying lesions – a task known to be difficult. The clinicians’ responses also indicated that the development of a method to evaluate an AI algorithm within the workplace setting before purchase is of utmost importance.
With these findings in mind, software developers need to prioritize building technology to help provide a practical solution to clinicians’ most important needs. The use of artificial intelligence may be the best way to ensure clinicians’ needs are met in an effective and practical manner. With the use of AI, the software can continuously learn from collective insights of multiple experts, effectively offering practitioners thousands of “second opinions.”
This type of technology can also help to provide interpretations and analyses of medical imaging, significantly reducing the amount of time it takes for clinicians to measure and trace these images. With less time and effort spent on interpretation and analysis, clinicians are able to spend more time with patient facing tasks – ultimately leading to higher quality care for a lower cost while reducing employee burnout.
Although these solutions exist, there are still several barriers that prevent the successful implementation of AI in clinical practices from occurring. For example, clinicians want to ensure that the AI is safe, effective and solves specific needs before the technology is purchased. However, of those providers who currently use AI in their practice, most were satisfied with their overall experience and found that AI provided value to them and their patients. Therefore, it seems that education about the potential benefits of AI in all practice types will continue to be important (Allen et al, 2021).