When it comes to healthcare transformation, AI has a lot to bring to the table. The advantages of using AI in medicine include improving healthcare outcomes for patients, maximizing the efficiency and cutting costs for clinics, healthcare organizations, and private practitioners. The market for AI in healthcare is also enjoying steady growth: proceeding the COVID19 pandemic, it is expected to grow at 44,9% CAGR and reach USD 45.2 billion by 2026.
In this article, we will talk about the benefits of AI in healthcare, look at the areas of its implementation, and make suggestions about how you could make the first steps in adopting AI in medicine. We will also explore how hospitals are leveraging AI to fight COVID-19. Read on to learn more!
It’s hard to overlook the role of AI in healthcare. From diagnostics to all-in-one management suits for hospitals and clinics, AI helps deliver better patient care and treatment, automates routine tasks for medical personnel, and delivers personalized healthcare experiences. How effective is artificial intelligence in healthcare? Let’s now take a closer look at its indisputable advantages.
1. Eliminating human errors
In healthcare, the impact of even the smallest error may be highly disruptive and dangerous. AI algorithms help detect the slightest mismatches between the prescribed treatment and diagnosis and help physicians take action before it’s too late. The same mechanisms apply in safeguarding patients data, deciphering medical images, and diagnostics.
2. Streamlining repetitive and routine processes
AI can also assist healthcare organizations in managing their daily routines. Powered by such facets of AI as robotic process automation, natural language processing, and rule-based expert systems, clinical office management platforms facilitate access to patient data, EHR, demographic and billing information, and help provide better customer service. These systems are also used in laboratory testing and pharmaceutical R&D.
3. Making sense of large data sets
Big data, processed by AI-driven analytics, can act as a huge asset to modern healthcare. The insights that it brings help us understand patients’ needs, prevent medical equipment failure, facilitate preventive care, eliminate human error, and deliver better customer service. Data analytics also play an important role in securing patients’ sensitive data.
4. Optimizing personnel and reducing costs
AI is extensively used in the managerial levels of medical organizations, streamlining clinic workflows and using advanced reporting tools to make better staffing and financial decisions. Today, AI-driven end-to-end clinic management suits help optimize operations of healthcare organizations and ensure better outcomes for patients, clinics and medical personnel.
How medicine may be disrupted by AI is evident, if we take a look at the areas of its implementation. Although listing all of them falls beyond the scope of this article, we will try to outline the most significant ones.
Artificial intelligence in diagnostic healthcare helps predict potentially dangerous health conditions, provides support in making medical decisions, and reduces errors. Technologies like machine learning help identify cancer cells, and image recognition technologies interpret and categorize medical images obtained through ultrasound, MRI, X-ray scanning, radiology, or tomography.
New drug discovery
New drug development is a lengthy and painstaking process, not to mention the expenses. AI can accelerate drug discovery by streamlining several areas of this process, such as identifying drug targets, selecting the lead components, and predicting the lab experiment outcomes. Furthermore, AI can assist in collecting and analyzing clinical data and boost the efficiency of clinical tests.
Chronic care management
As our life spans increase, so do the number of chronic diseases that we have to deal with throughout our lives. AI solutions have an immense potential to facilitate chronic healthcare, reduce the number of hospital visits, monitor drug intake and encourage the adoption of healthy habits.
AI powered apps help patients with cancer, diabetes, Alzheimer, cardiovascular and other diseases manage their chronic conditions. Such systems normally feature smart notifications for caregivers, physicians, and relatives, keeping them updated about the patient’s state. They are also finding vast application in geriatric care.
Enhancing patient experience
Clinics, public clinics especially, serve large numbers of patients and often fail to provide a satisfactory patient experience. A lack of customer service, confusion over appointment schedule or paperwork, lost medical records, and unpleasant front desk experiences are part of our lives, but in reality, they don’t have to be.
By streamlining routine tasks, providing automatic responses to questions and queries, prioritizing patient flow, and managing patient data, AI helps financial institutions accelerate patient admissions and offer better customer service.
Preventive and personalized healthcare
Using AI, doctors and physicians can create personalized treatment plans for patients. AI algorithms can crunch data from healthcare wearables and use it to schedule consultations and prescribe treatments. AI can also detect repetitive patterns in previous treatments prescribed to patients and suggest personalized treatment plans.
Just like manufacturing, the healthcare industry was one of the first to start using robots for a number of purposes. Streamlining laboratory operations, handling dangerous substances, executing complex surgery, facilitating rehabilitation and physical therapy are some of the examples of how AI is used in healthcare. On top of that, robots are increasingly used in patient care, streamlining routine tasks and relieving medical personnel from hard physical labor.
With the Earth’s population increasing and many families living in rural areas, a lot of people remain underserved when it comes to the accessibility of medical services. AI-driven telemedicine apps offer a solution to this problem; but apart from picking a clinic in their area, AI also goes a long way in evaluating symptoms and helping choose the right medical professional.
Most of the telemedicine apps feature encrypted messaging and safe video chats compliant with healthcare data protection regulations.
The pace of digital transformation in healthcare would have been much slower, if it were not for the COVID-19 crisis and the challenges that went hand-in-hand with it. In essence, the healthcare system in many countries was facing the pandemic unprepared for the exponential growth of the demand for medical services.
Here’s when AI technology comes to rescue. For more of the spectacular examples of the use of AI in healthcare, let’s see how hospitals are leveraging AI to beat COVID-19.
Using chatbots for patients triage and initial COVID-19 screening
With hospitals fighting a losing battle with the abnormal influx of patients, the need to streamline patient queries became pressing. For example, Partners HealthCare has built an automated response tool to help patients evaluate whether they should be screened for COVID-19. The COVID-19 screener guides users through the simple question and answer procedure, helps them evaluate risks, and suggests a further course of action.
Using robots to reduce contamination by minimizing human contact
Protecting doctors and medical personnel at the frontline of the pandemic is another COVID-19 challenge. Boston Dynamics and MIT are ready to provide intelligent robots to perform basic patient care tasks, such as delivering medications, measuring pulses, body temperature scanning, ect. Robotic assistants are now being implemented at Massachusetts General Hospital and at Brigham and Women’s Hospital.
Using AI systems to detect infected individuals
Spotting an infected individual in a crowded place can help take instant measures and curb the contamination. Manual check ups take too much time and aren’t effective enough in crowded places. In China, the technological company Baidu has developed an AI scanner that uses infrared sensors to detect people who may have a fever in the crowd. The system is now in use at Beijing’s Qinghe Railway Station.
Using AI-driven tools for COVID-19 diagnostics
The computer and machine learning technologies assist radiologists in detecting the signs of COVID-10 on CT images. In Wuhan, China, hospitals are using AI systems to sort through thousands of CT lung scans and detect the visual signs of the pneumonia, induced by COVID-19.
Using demographic and location data to reduce the spread of the coronavirus
In South Korea, people can download and install smartphone apps that send them notifications about how far they are from a place that a person with COVID-19 has recently visited. One of such apps, Corona 100, will automatically alert you if you come as close as 100 meters to a place previously visited by an infected individual.
Adopting artificial intelligence in medicine and healthcare takes lots of work, time, and investment. There are also many obstacles that hamper its successful implementation. If you’re planning to launch an AI project, below are some tips that could be helpful.
1. Partner with your end users and get their support
The main problem with AI solutions is that they often don’t have enough data for solving urgent clinical problems, and fail to integrate into the organization’s workflow. The solution would be to build them in close partnership with medical personnel who will be using these solutions.
The early product design stages are the ones that require active participation of healthcare professionals. This way the solutions will be best tailored to their needs, and they will become the advocates for the product within their organization.
2. Focus on data
AI feeds on data, and its lack or poor quality is often the main obstacle to building AI solutions. Focus on retrieving and categorizing data for your AI project. This often takes months and may pose quite a challenge: the data you need may reside in disparate databases, its formats may not be fit for processing with AI tools, or it may be contained within legacy systems and difficult to retrieve.
3. Have robust data protection policy in place
Most countries have regulations with regards to collecting, processing and sharing patient data. In the USA and the EU, healthcare data protection is regulated by HIPAA and GDPR, respectively. To ensure regulatory compliance of the AI-based solutions, implement strong security policies, use data encryption and anonymization technologies, and be explicit about how you will be using patient data. Your data protection policy must ensure that no sensitive patient data is used without their informed consent.
4. Take care of integration and interoperability
Before you start implementing AI solutions, digitize and automate your core processes. Then, make sure that the solution you implement is compatible with the existing systems, such as EHR. Before you start implementing AI solutions, instill a data-driven mindset within your organization. The medical personnel should be able to trust the data and to rely on it when making decisions.
5. Introduce new job roles
One of the most disruptive things about AI, is that its implementation has a highly disruptive impact on job roles within healthcare organizations. AI renders some jobs obsolete while creating a demand for new roles, such as data scientists or data engineers.
Admittedly, one of the obstacles to adopting AI in medicine is the skills gap. Understanding data science is a rare gift for medical professionals, yet, one way to bridge this gap is to grow AI talent internally. Invest into your personnel training and development to help them adjust to using AI solutions.
The world population is ageing and people across the world need faster, cheaper and more efficient healthcare services. The future of AI in healthcare lies in opening doorways to better and more accessible medicine. Not only is AI expected to improve patient outcomes, it is also supposed to resolve the shortage of doctors and nurses by accelerating and simplifying patient care. Medical professionals, on the other hand, will be relieved of routine tasks and manual labor and could keep better focus on their patients.
The road towards adopting AI in medicine is rocky, though. To fully deliver on the promise of radically transforming healthcare, organizations need to overcome legacy systems and mindsets, data availability and quality, lack of funding, and the shortage of skilled professionals. A reliable tech partner could make this journey easier for healthcare companies, and accelerate AI implementation and development.
At Eastern Peak we have strong expertise in building AI solutions for a variety of healthcare domains. Contact us now to uncover the disruptive potential of AI in healthcare.