AI Is The Future Of The Healthcare Industry

2020 was extremely challenging for healthcare. A year after, the challenges carry through. Caregivers continue to fight against the COVID-19 pandemic — the mission hard enough as it is, to say nothing of its aggravation by a workforce shortage, the national mental health crisis, and treatment of postponed chronic conditions.

Building on automation, artificial intelligence is about to revolutionize healthcare and help caregivers address the challenges they face.

So, what does the future of AI in healthcare look like? Will artificial intelligence replace doctors? What are the specific examples of artificial intelligence in healthcare organizations? And how do you implement AI solutions without adding up to the uncertainty of the pandemic? Let’s dig in.

Top 10 AI applications in healthcare

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 AI use cases that bring about such an impressive value.

Source: Accenture analysis

Robot-assisted surgery

Many fear that 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:

• Lower risk of infections
• Less pain, scarring, and blood loss
• Shorter hospitalization and faster recovery
• Quicker return to daily routines

Virtual nursing assistants

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:

• 24/7 access to medical support
• Round-the-clock patient condition monitoring
• The ability to provide quick answers about illnesses and medications

But if you decide to adopt a virtual nursing assistant, focus on thinking out data compliance and security aspects. Make sure to set up data protection controls and risk mitigation procedures to prevent common problems with AI in healthcare, such as PHI leakage and malware attacks.

Administrative workflow assistants

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 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:

• Healthcare professionals make quicker and more informed decisions about operational tasks
• The quality of care improves as doctors can concentrate solely on their patients
• Patients with critical conditions receive timely care due to the prioritization of requests and automated patient discharge
• Operational costs go down due to less human resources needed to keep a practice running

Still, AI by itself is not a cure to all workflow-related issues — it requires thorough expertise, process, and technology planning, workflow transformation, and weighing the outcomes. So, to maximize the impact of artificial intelligence on healthcare, go slowly and consider all the factors that influence AI adoption success, from the quality of data inputs to human factors.

Fraud detection

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:

• Faster insurance processing and patient dispatch
• Lower costs of care and lower premiums for patients
• Higher personal healthcare data security
• Higher patient satisfaction rates

But if you decide to tap into AI-powered fraud prevention, remember that however reliable, technology is not a replacement for the human eye. While AI can accurately detect obvious signs of fraud, one of the common disadvantages of AI in healthcare is its tendency to flag anything that seems out of place as a fraud mark, including typos. So, do have human analysts to sort out real fraud marks from human error until your AI engine is trained well enough to handle the task.

Prescription error recognition

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 the 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:

• Increase the quality of care by preventing drug overdosing and health risks
• Speed up electronic authorization, submission, and review of clinical documentation for drugs requiring approval
• Monitor patients’ medication adherence

However, along with organizational challenges halting the adoption of AI in general, wider usage of technology for prescription error recognition relies much on the quality and heterogeneity of input data. Today, hospitals and practices manage data in many different ways that are not always consistent. This may lead to hidden errors that may be hard to recognize. So, healthcare organizations will need to strengthen healthcare databases to enjoy the full benefit of AI.

Automated image diagnosis

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:

• A reduction in human error and more accurate diagnosis
• Well-recorded and reliable monitoring of a patient’s progress
• Well-recorded and reliable monitoring of a patient’s progress
• Automatic diagnosis report generation

If you are to develop an image recognition solution, pay attention to implementing DICOM-compliant data structures, media formats, and storage, and set up the needed security controls.

Cybersecurity

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 a 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:

• Eliminating threats to patients’ safety
• Preventing harm to a hospital’s reputation and avoiding lawsuits
• Cutting costs previously spent to recover from breaches

Still, skills shortages prevent healthcare organizations from massively adopting AI for cybersecurity. Almost 40% of healthcare companies say they lack qualified employees to manage security strategies. In such cases, bringing in third-party vendors may be necessary to realize the advantages of AI in healthcare and keep medical systems and data protected.

Connected medical devices

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:

• Personalization of care based on a patient’s activity and physiological needs
• Better clinical results due to informative insights fetched from diverse data sources
• The ability to prevent life-threatening crises through real-time patient monitoring and alerting
• Optimized hospital workflows based on care prioritization

Yet, we don’t see many hospitals adopt AI-powered connected, medical devices. Common concerns range from the need to ensure interoperability between medical devices and legacy hospital systems, data security, and patients’ reluctance to use such devices to ethical aspects of patient monitoring.

Identification of clinical trial participants

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:

• Up to 30% higher time-efficiency
• Up to 30% cost reduction
• Up to 20% fewer data errors

However, there’s no AI engine that can take any clinical notes and interpret them. For AI to drive value, clinical data should be prepared beforehand. The eligibility criteria that AI would use as the base for analysis should be translated into standardized, coded queries for the software to understand them.

Preliminary diagnosis and selection of optimal treatment strategies

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 applied 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:

• Ease the strain on resources
• Free up time for doctor-patient interaction
• Diagnose diseases faster with the accuracy of top medical experts
• Develop tailored, precise treatment strategies.

Although AI shows great diagnostic accuracy, both patients and doctors are still reluctant about its wider adoption. To build confidence in AI, it is crucial to establish transparency and let patients and doctors know how AI comes to its decisions. Also, any insights from clinical notes that are used by AI algorithms should be easily traced back to the exact place they come from.

A path to successful adoption of AI in healthcare

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.

1. Establish a use case.

To realize the benefits of artificial intelligence in healthcare, it is crucial to start right. Begin with incorporating AI into highly repetitive tasks that offer great opportunities to drive efficiency and build on from there to adopt more use cases.

Before getting approval from the executive board, interview hospital administration, doctors, and patients to detect organizational and clinical problems and identify how technology can improve patient experience and staff productivity.

Also, study available data to understand if it’s enough to train reliable AI algorithms and whether additional data processing will be needed.

2. Get buy-in from the C-suit and patients

AI is still new to healthcare. So it is usual for healthcare executives to worry about the ROI and reliability of AI-powered diagnosis and treatment. The worries often come from the lack of understanding of how AI works and how it drives value.

To get a buy-in from hospital executives, clearly state the process and economic value AI is going to bring (it is not too difficult with a well-established use case), supporting it with numbers. AI provides clear advantages here. Deloitte found that 83% of early AI adopters have already achieved moderate to substantial economic benefits.

And to persuade patients, make sure to explain how AI works, prove the efficiency of AI for solving a specific task, guarantee that a doctor will stay in control over AI’s decisions, and prioritize patient data security.

3.Overcome technology-related challenges.

When implementing AI for healthcare, it is important to address AI-related ethical issues and prioritize AI explainability. Whether you choose to go with a ready-made solution or develop one from scratch, make reliability, transparency, security, and compliance a priority. and networking. This will help you understand what makes sense to migrate and how your future architecture plan should look like.

4. Educate employees.

The introduction of AI will require new skills from doctors and nurses. To educate the medical staff on how to work alongside smart machines and applications, include comprehensive training sessions in your AI rollout plan.

Successful adoption of AI in healthcare largely depends on how experienced and skilled your technology partner is, as well as their readiness to support you during AI deployment and fine-tuning.

If you have any questions, drop us a line and we’ll be eager to help.

Previously published at https://itrexgroup.com/blog/examples-and-benefits-of-ai-in-healthcare/