Benefits of AI in healthcare: from accurate diagnosis to remote patient monitoring

The COVID-19 crisis has pushed the healthcare industry, traditionally rather skeptical of IT innovations, to actively adopt modern tech. Accenture predicts that through 2021, the market of healthcare AI will reach $6.6 billion with an explosive CAGR of 40%. The same study highlights the positive economic impact of AI in healthcare. The adoption of artificial intelligence will drive $150 billion in annual savings for the US healthcare economy in just five years. Let’s have a closer look at the examples of AI in healthcare that bring about such an impressive value.

Many fear that a robot-assisted surgery involves AI replacing doctors and autonomously making decisions about surgical motions. In reality, it’s a human who stays in control, while AI-powered robotic instruments help surgeons make more precise and delicate motions. For instance, Maastricht University Medical Center uses an AI-powered robot to suture small blood vessels, some no thicker than 0.03 millimeters. Another hospital to rely on AI-powered robots is Mayo Clinic in Jacksonville, Florida. They use AI to perform abdominal surgeries and now develop robotic technology to operate on the brain. For that, they make AI “watch” surgeons perform, recognize their movements and patterns, and convert these patterns into commands for the robot. When it comes to how robots and artificial intelligence can benefit hospitals, the value is clear. The key advantages of applying AI in operating theatres are:

89% of patients in the US google their symptoms before turning to doctors, and the results of self-diagnosing turn out rather scary. Virtual nursing assistants prevent such delusive endeavors. Utilizing AI healthcare analytics, they monitor patients’ health parameters, medication intake, and habits to prevent worsening of chronic conditions and schedule medical appointments when needed. A good example of a multi-functional virtual nurse is Molly. Visualized as an avatar, it provides remote support for common medical conditions and tracks a patient’s weight, blood pressure, and other parameters generated by monitoring devices. The app features a chatbot, too, for patients to discuss their health requests privately and conveniently book an appointment with their physician. Virtual assistants like Molly have already won remarkable popularity — 64% of patients feel more comfortable receiving instructions from them. Virtual nurses also outrun humans in the following aspects:

Doctors spend about 16 minutes per patient just to fill out EHR forms. But with AI-powered workflow optimization, caregivers can stop worrying about administrative tasks and dedicate their time to patients. Machine learning, robotic process automation, and natural language processing — the subsets of AI — help to navigate medical records with voice commands, transcribe clinical data recorded during patient visits, and return personalized responses to EHR searches. AI-powered workflow assistants simplify appointment booking and help prioritize and discharge patients quicker as well. Many healthcare organizations, among them Cleveland Clinic, already see their workflows improve. The clinic collaborates with IBM to process medical papers with AI and reduce operational costs, while driving patient care forward. We at ITRex, too, have helped caregivers optimize daily operations by developing a robotic process automation solution that synchronizes appointment scheduling between an appointment booking service and an EMR. The solution frees doctors from manually entering appointment data into two systems and adds consistency to patient scheduling. The benefits of AI in healthcare workflow optimization are numerous:

About 3% of all healthcare claims in the US are fraudulent. Not that big a number, but it translates into a hundred billion dollars lost annually. Artificial intelligence automates claims assessment. Machine learning models detect invalid claims before they are paid for and help to speed up processing, approval, and payment of valid ones. But it’s not only insurance fraud AI is capable of detecting. When it comes to billing for procedures a patient never received, upcoding — billing for a simple procedure as for something more complex — or preventing patient data from being stolen, AI helps there, too. Leading healthcare services organizations, among them Harvard Pilgrim Health, are embracing AI to root out healthcare fraud. Harvard Pilgrim Health has chosen AI-based fraud detection software over legacy rule-based systems to identify claims and provider behaviors that don't look normal. Such early adopters already enjoy the benefits of artificial intelligence in healthcare fraud prevention:

In the US alone, 5,000 to 7,000 people die annually because of prescription errors. These errors often stem from flawed EHR interfaces — doctors choose wrong drugs from a drop-down menu or get confused in dosing units. AI fights this issue. ML models analyze historic EHR data and compare new prescriptions against it. Those prescriptions that deviate from typical patterns get flagged, so doctors can review and adjust them. Brigham and Women’s Hospital uses an AI-powered system to pinpoint prescription errors. Over a year, the system identified 10,668 potential errors, and 79% of them were clinically valuable, so the hospital managed to save $1.3 million in healthcare-related costs. Along with cost savings, AI-powered prescription error recognition apps help:

Computer vision capabilities of AI benefit healthcare a lot. Hospitals and clinics use AI to recognize abnormalities in different kinds of medical images — from CT to MRI to radiology scans. Image recognition assists doctors in improving cancer prognosis, diagnosing tumors, kidney and liver infections, bone fractures, and other illnesses and conditions. One of the examples of AI-powered visual perception in healthcare is the tool applied at the UVA University Hospital. Utilizing ML algorithms, the tool analyzes children’s biopsy images to distinguish between environmental enteropathy and celiac disease, doing it as reliably as doctors do. Image recognition is also being used to fight the pandemic. Huiying Medical developed an AI-powered imaging solution that analyzes patients’ chest scans and detects the COVID-19 virus with 96% accuracy. The benefits of AI-powered image recognition comprise:

The healthcare sector isn’t new to cyberattacks. Back in 2017, the infamous WannaCry ransomware paralyzed parts of the UK’s National Health Service for days. In 2019, a malicious agent leaked personal data of thousands of Singapore’s HIV-positive patients. In the midst of the pandemic, cybersecurity has taken on extra importance. To avoid system downtimes and data breaches, healthcare organizations are tapping in AI-powered cybersecurity. AI-based security solutions analyze data flows within a technology system to get the grasp of what behavior is normal and abnormal for each user. Building on this knowledge, AI detects and neutralizes cyberattacks, so attackers are caught before they do any damage to the system. For instance, AI has become a powerful tool for Boston Children’s Hospital. Using AI, they stay one step ahead of the potential attackers and identify anomalous behavior, for example, hundreds of doctors trying to access a patient’s record at the same time, as it’s happening. Along with preventing cyberattacks, AI protects healthcare data. For example, we used AI to mask huge volumes of personally identifiable information across our client’s databases, cloud apps, and unstructured resources, so that they could achieve compliance with healthcare standards. A high degree of medical systems’ protection brings about numerous benefits:

AI serves as “the brain” for a whole range of connected medical devices — from simple glucose monitors to advanced insulin delivery systems and wearables for monitoring blood pressure and other vital signs. The AI engine processes the data fetched from connected devices to alert doctors if anything goes wrong and provide real-time reports about patients’ health. A well-known example of a connected medical system is IntelliVue Guardian Solution by Philips Healthcare. It uses AI to analyze data from sensor-equipped wearables, identify abnormal changes in the patient’s vital characteristics, and notify doctors to prevent any events threatening the patient’s life. Seventy percent of doctors say that the solution has made it easier for them to identify patients who need immediate help. We also have implemented AI to help our client track the frequency and quality of hand hygiene in hospitals via connected wristbands and a remote patient monitoring platform. With an advanced dashboard, hospital administrators could monitor hand hygiene compliance on a daily, weekly, monthly, or even hourly basis. As a result, the hospitals saw an increase in hand hygiene compliance by more than 70% within a single week. Using AI to power connected medical devices opens up opportunities for:

Recruitment is the most time-consuming and expensive part of clinical research. Searching for the right group of people may last for years. With AI, however, researchers could find potential clinical trial participants in hours, if not in minutes. Harnessing vast amounts of data, including those from EHRs and smart wearables, AI algorithms may drastically speed up medical research, save healthcare and pharmaceutical companies billions of dollars, and pave the way to more efficient experimental treatments. Natural language processing algorithms can search through patients’ health records and pathology reports to single out patients eligible for clinical trials. AI helps design better processes for clinical trials as well. As each trial follows a formal protocol, any deviation from it requires amendments, which may delay the research for months. AI analyzes previous studies and scientific literature to let researchers integrate agility and reliability in protocol design. Although AI is still a rare component of clinical trials, some institutions already witness its value. Using AI-powered software, the researchers at Cedars-Sinai Smidt Heart Institute in Los Angeles found 16 eligible participants for their trial in just one hour, while manually searching for candidates provided only two in six months. The advantages of AI for healthcare research are:

AI algorithms can diagnose patients the way doctors do. For that, AI feeds on the data about previous diagnoses and learns to make its own diagnostic decisions. Then, the algorithms take in symptoms data and, if any, the data from wearables or medical images and analyze it against previous research mistakes, available treatment options, side effects, and diseases with similar symptoms to give a preliminary diagnosis. Because AI can simultaneously process so much data, it has the potential to outperform humans in diagnosing diseases, from cancers to eye conditions. Moorfields Eye Hospital in London, for instance, uses AI-powered software to diagnose ocular conditions. AI diagnoses and offers treatment for over 50 diseases with 94% accuracy, which matches the performance of top medical experts. We at ITRex have developed an AI-powered platform that runs accurate power calculations for lenses implanted in patients as part of a treatment for cataracts, myopia, and other eye conditions. Another example of AI in healthcare diagnosis is an AI platform designed to collect, manage, and present data for patients diagnosed with cancer. The platform features a predictive analytics and decision support system that generates survival curves for newly-diagnosed patients based on the analysis of multiple patient-specific factors such as patient age, gender, comorbidity, cancer site, cancer stage, and tumor grade. Applications of AI for diagnosis and treatment help:

The road toward AI can be bumpy. But having led many AI implementation projects ourselves, we’ve come up with a roadmap that makes AI adoption less stressful and far more effective.