Key Takeaways
1. AI is implemented in the healthcare industry to streamline administrative tasks, make more accurate patient records, and improve patient outcomes significantly.
2. AI in healthcare comes with its own risk factors, including data security, ethical challenges, biases, and lack of strong outcome-based foundations.
3. IS Partners has a dedicated team of experts specializing in AI compliance regulations. Enjoy hassle-free audits for AI management regulations, such as NIST AI RMF.
How Is AI Used in Healthcare?
AI is not really a new addition to the healthcare field; we are just noticing it more because adoption is accelerating and expanding. One year ago, we marveled that large language models, including ChatGPT, had successfully passed the US Medical Licensing Exam (USMLE) without specialized help from medical professionals. Now, we see artificial intelligence being used in healthcare organizations in innovative ways.
At the HIMSS Global Healthcare Exhibition 2024, many companies presented innovative approaches to using AI, such as revenue cycle management software and robots for billing. In some ways, it felt like we were stepping into the next generation of healthcare, something resembling a futuristic film, but these applications were not fiction.
"The amount of AI applications on display at HIMSS this year was eye-opening, even compared to the year before. From Chicago in 2023 to Orlando in 2024, the volume of healthcare companies based in AI increased significantly."
Increasing the Accuracy of the Healthcare Industry
Doctors and providers in health systems already use AI and machine learning in clinical practice to analyze data and improve patient care. For example, Viz.ai uses AI to quickly identify the warning signs and symptoms of strokes, helping doctors act fast to save lives.
Companies like PathAI also use machine learning to predict patient outcomes – based on their genetic information and medical history – with astonishing accuracy.
Making Administrative Tasks More Efficient
Today, healthcare providers at all levels already use AI, whether they know it or not. This technology has been added to all the major Electronic Health Records (EHR or EMR) software platforms. It helps the healthcare sector detect patterns and anomalies in patient records and even create personalized treatment plans.
“At the conference, companies introduced tools that basically eliminate the need for entry-level healthcare positions because AI can handle a significant amount of the workload and do it more accurately,” continues DeArment. “We saw how AI was used to develop a diagnosis based on a patient’s conversation with an AI bot.”
Powering Pharmaceutical Development
In the field of drug development, AI speeds up the process of testing new drugs by analyzing data faster and more accurately than any human in the laboratory could. New applications are speeding up the discovery of new drugs by analyzing huge amounts of data, leading to finding new medicine candidates much faster.
It’s also making clinical trials quicker and cheaper by automating tasks and managing trial data more efficiently. Additionally, AI helps biopharma companies run their factories better by predicting problems and improving supply chain decisions while also becoming crucial in defending against complex cyber threats.
VC Spotlight: Generative AI and Healthcare
Speeding Up Patient Intake
“Hello patient, how can I assist you today?” Does this question look familiar? It’s because AI chatbots and virtual assistants, like those from Ada Health, answer simple health questions and help schedule doctor visits. They’re quick and easy but can’t replace real doctors for serious issues.
One example is the popular OpenBots Documents based on GPT. It uses AI to quickly highlight important information from various documents, avoiding the need for manual data input and complex programs. It’s designed to work with medical forms and records easily and offers features like instant document handling, the ability to make custom templates, and a simple interface for checking and approving data.
This one app is already widely used to upload healthcare forms, extract key patient information using AI, create templates for consistent data capture, and provide users with quick access to important document details.
24/7 Patient and Vitals Monitoring
Wearable AI devices, an industry valued at $180 billion by 2025, have the capacity to monitor health in real-time. They track things like heart rate and blood pressure, alerting doctors if something’s wrong. Devices like CGM systems track blood glucose daily, giving doctors accurate data to manage diabetes better. The adoption of wearable medical devices accelerated exponentially during the COVID-19 pandemic, and we don’t expect that trend to fall.
Remote Patient Monitoring (RPM), according to Jorie Advanced Automation, uses tech to send patient information to doctors in different places. AI helps by finding patterns, guessing future health issues, and advising patients and doctors.
AI-powered patient monitoring systems process data quickly, providing immediate insights and accurate analysis of health patterns, while real-time monitoring allows for fast medical action. AI also cuts healthcare costs by reducing hospital stays, optimizing resource use, and offering remote specialist care, and improving patient engagement through personalized communication, treatment adherence support, and clear health status explanations.
Enhancing Surgical Precision
According to the American College of Surgeons, AI can anticipate the next steps in surgery and offer additional oversight, which can assist surgeons in adjusting their strategy if necessary. Plus, AI now plays a significant role in surgical training, offering learning tools for med students and acting as an expert assistant during operations.
AI-supported tools can guide and enhance surgical procedures, such as laparoscopic and robotic surgeries, by providing real-time information and alerts during operations. This type of robotic surgery enables healthcare professionals to do complex operations with high precision and less risk, which means faster patient recovery.
Reducing Fraudulent Medical Claims
The U.S. Justice Department finds that 3% of healthcare claims are fraudulent, costing nearly $100 billion and causing higher premiums. But that’s where AI tools come in. They are number-crunching powerhouses; they can scan loads of data and detect inconsistencies that would be hard, if not impossible, to detect otherwise.
AI can identify fraud patterns in medical claims before payment, speeding up valid claims and reducing costs. Platforms like H2O.ai specialize in healthcare applications and are currently working with major companies to implement machine learning interpretability for industry compliance.
When Was AI First Used in Healthcare?
While the concept of AI originated in the 1950s, it appears the first applications of AI, specifically in healthcare, began emerging in the late 1960s and early 1970s with early expert systems like Dendral, MYCIN, and INTERNIST-1. These systems were used for applications in organic chemistry, reaching diagnoses, drug discovery processes, and prescription decision-making.
However, these early systems faced limitations that prevented their widespread clinical use until further advancements in the following decades.
What Are the Potential Challenges and Risks of AI in Healthcare?
AI in medicine raises tough questions about who’s to blame if something goes wrong, with some saying the doctor is always responsible.
Ethical Concerns
AI in healthcare raises important ethical questions, like how to avoid harming patients and what to do about biases in data that could affect patient care. There are also worries about big datasets being hacked and private information getting out. It’s not clear yet who owns the data or who is responsible if there’s a breach, especially because privacy laws differ worldwide.
Data Privacy Issues
Using patient data to train AI in healthcare also comes with privacy worries. AI needs a lot of data, which raises the risk of leaks. This data has sensitive info like medical records and personal details, which laws like GDPR and HIPAA protect.
Biases in Algorithms
Clinically irrelevant performance metrics present another challenge since the success measures for an AI model don’t always translate well to clinical environments. To bridge this gap, developers and clinicians must work together to explore how AI algorithms can improve patient care and assess AI models for accuracy using decision curve analysis.
Lack of Foundation
Lastly, there’s a shortage of established methodologies, forward-looking research, or peer-reviewed studies on AI in healthcare. Most studies have been retrospective, relying on historical patient records. To truly understand AI’s diagnostic value in real-world scenarios, doctors need to conduct prospective research by monitoring current patients over time, combining physical exams with telehealth appointments and remote monitoring technologies.
Can HIPAA Effectively Regulate the Use of AI in Healthcare?
Experts in the field are starting to raise red flags as they fear that HIPAA laws are woefully outdated and no longer capable of protecting patient information the way the act was intended. It seems clear that HIPAA Privacy & Security Rules haven’t kept up with new technology and how medical data is actually used today. Plus, various states have different privacy laws that don’t work cohesively together well and which aren’t regulated on a federal level, unlike the EU’s GDPR, so updating HIPAA isn’t enough; we also need new rules for AI ethics and better technology to keep health information safe without losing its usefulness.
To keep patient information safe, organizations must improve how they protect data from cyber threats and stay alert for any weaknesses. As AI becomes more common in healthcare, HIPAA rules must change with the technology. Some suggested updates include clarifying privacy notices, encrypting data when sent elsewhere, and being careful about who can access patient information.
To ensure HIPAA keeps up with AI, cybersecurity practitioners must check for vulnerabilities more often, use better encryption, and manage who can see patient information. We also anticipate that the government will publish new policies on AI, like the FDA’s plan for keeping an eye on AI in medical devices.
Can HITRUST Close the Gap Between HIPAA and AI Assurance?
In the past few months, HITRUST has released several resources related to AI compliance. The organization drafted the first AI strategy document to provide practical and scalable ways to manage risks and security for generative AI in healthcare.
Their AI assurance program offers a trusted method for organizations to understand AI risks and show they follow AI risk management principles, maintaining the same level of clarity and quality as other HITRUST Assurance reports.
HITRUST has also introduced a new program to help healthcare groups safely use AI, focusing on managing risks. This program is part of the updated HITRUST version 11.2 and aims to improve discussions about risk between organizations and AI service providers. HITRUST is also collaborating with Microsoft Azure OpenAI Service to keep the HITRUST framework up-to-date with new laws and standards.
The HITRUST AI Assurance Program is working to build a detailed framework for managing risks in AI systems, focusing on threat assessment, compliance with data protection laws, and adapting to new threats. It emphasizes the importance of clear and understandable AI systems and promotes sharing knowledge across organizations to handle AI-related challenges effectively.
The Future of AI in Healthcare Compliance Is Bright with IS Partners
Integrating AI into healthcare is a game-changer, offering incredible benefits like enhanced diagnostic accuracy, administrative efficiency, and personalized patient care. However, it also brings forth significant regulation, ethics, and data security challenges.
As we navigate AI’s integrations, healthcare providers, regulators, and AI developers must work collaboratively to ensure these technologies are used safely and effectively. Play your part by ensuring that your company is compliant with the most integral compliance regulations concerning AI.
Implement strict security frameworks that specifically aim to create a harmonious integration of AI into your system, including NIST AI RMF, and HITRUST AI RMF.
Consult with IS Partners today and collaborate with experts from IS Partners. Having worked in the compliance industry, IS Partners has kept its team up to date with the most recent developments in compliance.
Ready to ensure your AI healthcare initiatives are compliant and secure? Contact IS Partners now to schedule a consultation and take proactive steps toward regulatory compliance and ethical AI implementation. Don’t let compliance concerns hold back your innovation in healthcare.
