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Since the pandemic back in 2020, the healthcare system has recognized the urgent need for digital transformation. While traditional healthcare is crucial, technologies like artificial intelligence and machine learning can open the door to groundbreaking capabilities. From drug discovery and medical treatments to task automation and robotics, AI in healthcare can have an immeasurable impact on the entire industry. In this article, we’ll discuss the existing challenges in the sector, the cost of AI in healthcare, and real-world applications. We’ll also touch on the benefits and limitations of artificial intelligence in the healthcare sector. So, how does AI reduce costs in healthcare?
The healthcare system has long become one of the most inert and regulated industries, and for good reason. When human lives are at stake, making sure processes are highly regulated and each step is accounted for significantly minimizes potential risk factors. Before dissecting how technology can reduce healthcare costs, let’s identify the biggest cost drivers in the healthcare sector.
The costs of research and development of drugs, medications, and treatments have been on the upward trajectory for a few years, now reaching a whopping $2.3 billion USD per drug. Experts have put forward several reasons for this trend, including lack of competition, labor shortages, lengthy development timelines, high attrition rates, and more.
During COVID, numerous industries have suffered from supply chain disruptions that are still impacting healthcare costs. The uncertainty regarding the tariff policies in the US is also playing a role in the rising expenditure.
Another crucial factor that reflects the ever-growing healthcare costs is cybersecurity. The global healthcare cybersecurity is projected to grow 15% over the next five years, reaching a cumulative $125 billion USD. Due to the sensitivity of data that healthcare professionals collect, hospitals and clinics often become the target for breaches.
Not only are data breaches exceedingly expensive, but their costs in healthcare are even higher than in other sectors, $9.77 million USD in 2024. The healthcare industry is likely to continue to dominate the list of hack-vulnerable institutions, demanding more robust security protocols and standards.
According to the McKinsey report, roughly 15 cents spent on every dollar is wasted on claims processing, billing, payment, and other financial transactions. The key issue that perpetuates this money-draining system is a subpar revenue cycle management process. To this day, a large number of tasks are performed annually, making them not just inefficient but also error-prone. As a result, medical workers waste their valuable and costly time on manual processes, while inevitable mistakes increase the healthcare costs even further.
Perhaps surprisingly to some, health inequity is not only a social problem. The impact that social marginalization has on healthcare costs is higher than most consider. In fact, the gap in access to healthcare services is associated with an additional $320 billion USD each year. When patient outcomes are not equal between people, it puts more pressure on the already crumbling healthcare system, which became more evident during the COVID pandemic.
Now that we know the current issues in the medical field, let’s explore the benefits that AI can bring to the healthcare system. So, how does AI reduce costs in healthcare?
Healthcare providers can leverage AI tools to automate mundane and time-consuming tasks that hamper their efficiency and increase expenses. Activities like scheduling, billing, coding, and data entry and retrieval put an additional load on the labor force and contribute to overall healthcare costs. Generative AI in healthcare, along with other AI-powered technology, can aid in identifying operational bottlenecks and reducing waste, leading to cost savings in the long run.
In addition to taking away too much time from medical professionals, administrative tasks can often cause errors as they don’t require a lot of mental effort to perform. Working long and brutal hours can inhibit one’s attention to detail, especially when it comes to rather simple and routine tasks like data entry. This situation creates a perfect recipe for mistakes and inconsistencies in electronic health records and treatment plans, threatening patient outcomes. AI technology can drastically reduce inaccuracies, nearly eliminating minor errors.
Early detection is key to improving patient outcomes in the long run, especially in more serious and life-threatening illnesses. By enabling timely interventions before the disease progresses further, physicians can facilitate enhanced well-being. However, identifying slight signs of anomalies has proved to be a highly arduous task.
Using AI in the healthcare sector can lead to better patient outcomes through improved accuracy of medical diagnosis. AI technology allows healthcare organizations to analyze patient data, medical images, and lab results a lot faster and more precisely, detecting anomalies before the human eye can. By parsing through millions of images, artificial intelligence systems can identify new patterns that were hidden from us and discover new markers for various ailments, offering personalized patient care.
Besides driving recovery rates and patient experience, AI in healthcare can also yield considerable cost savings. For example, research reveals that patients who were diagnosed with cancer early pay two to four times less for treatment compared to those with more advanced stages.
As discussed above, drug discovery is an immensely expensive part of the healthcare industry budget, and clinical trials are a major factor. Drug development processes are incredibly long, highly regulated, and have a high failure rate. In fact, roughly 90% of drugs that enter the clinical research phase fail due to inefficacy, toxicity, poor properties, and ineffective planning.
AI systems are capable of predicting which potential components can increase the efficacy of a certain drug and choosing the best candidates for a clinical trial, ultimately increasing their success rate. The higher the chances for a successful trial, the less funding is required for drug development procedures.
The cost of implementing AI in healthcare will be offset by the enormous savings AI technology can bring to healthcare organizations by detecting suspicious data patterns in insurance claims to minimize fraudulent behavior. AI in healthcare can also help hospitals develop more robust mechanisms to keep patient data safe from malicious actors.
The relative novelty of the technology poses certain risks and constraints on using AI in healthcare. Let’s take a look at some current limitations of AI and how it may impact patient outcomes and cost savings.
Although AI tools excel at pattern recognition, they lack in generalizing their knowledge base and consistency in decision making. Hidden behind a black box, AI algorithms don’t offer the level of transparency required for such complex medical processes. At this point, accountability issues also arise, creating an ongoing challenge of determining who’s at fault. Since AI is intended to streamline physicians’ workload, while the explanation from machine learning in healthcare is minimal, how does one identify the person responsible for a potential misstep?
During healthcare software development, AI models are trained on available patient data to enable them to recognize patterns and anomalies. However, the training data is initially generated by humans, who are inherently biased. Despite the recent surge in inclusivity and attempts to make medical data more diverse, the bulk of existing findings are based on skewed research that perpetuates prejudice and inequality. Markers like gender, age, weight, ethnicity, and other demographic factors can find their way into AI models, leading to misdiagnoses.
To create diverse data science in the healthcare industry is constrained due to organizational resistance and bureaucracy. At the same time, using poor-quality data for model training can be detrimental for patient care as it might lead to erroneous results.
Healthcare data management is another critical aspect of integrating AI into the healthcare industry. The biggest concerns are associated with data collection and sharing across systems. Especially in medical care, guaranteeing confidentiality and privacy of medical records is imperative to foster trust and transparency. It’s essential to establish norms and regulations that protect both healthcare providers and patients.
Patient care is not only about accurate diagnosis and treatment, but it’s also about the doctor-patient relationship. In the absence of an emotional bond between the healthcare provider and the patient, the quality of care might suffer. The current healthcare system is built on trust, with the clinician engaging with patients in a respectful and empathetic manner. This relationship can severely affect patient outcomes and play a major role in overall population health management.
AI systems, on the other hand, are rational and logical, but lack the human ability to connect and relate to others. They also can’t make behavioral observations that impact their well-being, which might lead to mistakes.
In this section, we’ll delve into concrete use cases of AI in healthcare that can improve patient outcomes, streamline operations, and make the industry more accessible.
AI in healthcare is instrumental for more accurate and timely diagnosis and treatment. For example, AI models are used in screening for cervical cancer to detect precancerous markers. Deep learning algorithms analyzed 60,000 images of cervical cancer and learned to identify the early signs with higher precision than human doctors.
In patient care and treatment, artificial intelligence has also made a difference. With mobile app development and various medical devices, patients can now take control over their ailments. Especially patients with chronic diseases like diabetes can now monitor their glucose levels using digital health solutions like Onduo. Not only does this unburden healthcare providers, but it also allows patients better autonomy over their conditions.
Artificial intelligence also plays a role in personalized patient care, bridging the existing disparity in healthcare services. By analyzing medical images, deep diving into patient data, and employing facial recognition systems, AI in healthcare can deliver tailored care to every patient. For instance, human-machine interfaces can detect even the smallest facial motions and help people with disabilities to drive a vehicle or type out a message.
Medical devices and robots also have the potential for transforming healthcare in the contexts of surgery, rehabilitation, research, elderly care, and more. Unlike humans, robots can perform tirelessly for hours without getting fatigued, distracted, or misled. They’re particularly groundbreaking at performing surgeries in places where human hands cannot reach. Medical robots are also imperative in elderly care as assisted-living companions, supporting patients with daily tasks.
As already mentioned, AI streamlines administrative tasks, allowing physicians and nurses more time to focus on patient care. AI in healthcare can transcribe medical records, generate bills and invoices, create schedules, organize and manage inventory, and perform other ongoing activities that take away from medical care. For example, such robots can assist clinicians with daily patient rounds, retrieve their medical data, and greet patients in the lobby to offer directions.
In drug research, artificial intelligence can rely on its advanced pattern recognition capabilities to shorten the timeline for medication development. AI in healthcare can analyze images, generate unique chemical compounds, and select the best candidates for trials. Using its sophisticated predictive analytics abilities, AI tools can forecast the proper molecular structure and identify potential side effects to create the safest drug in a shorter period of time.
It’s clear that AI in healthcare can yield numerous savings across administrative tasks, labor force, human error elimination, and other associated costs. However, what about the cost of technology in healthcare? Does the cost of artificial intelligence in healthcare offset the expenses of adoption?
How much it costs to implement and maintain AI in healthcare depends on several aspects. Among significant cost factors are:
If you’d like to kick-start your own mobile or web development project to unlock cost savings, NIX is here to guide you. We’re a team of seasoned software engineers and AI experts with years of experience in delivering digital health solutions. Get in touch with our team to evaluate the cost of AI adoption for your company and select the best approach to combat your issues and concerns.
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