Wrapping Up the AI Revolution Series
AI can improve quality of care, increase access to care, and reduce costs in the home care/home health industries and beyond
THE VBP Blog
June 5, 2024 – Artificial Intelligence (AI) has already begun transforming the healthcare industry in many ways and offers solutions to some of the industry’s pressing challenges. From enhancing patient care with advanced diagnostic tools and assisting with the workforce shortage to managing complex mental health treatment and addressing health-related social needs, AI has many applications in healthcare. However, the integration of AI does not come with concerns. We need to ensure that AI complements rather than replaces the human touch in healthcare. In addition, there are other challenges like privacy and regulation, and the potential for data bias that need to be considered.
We’ve touched on all of this—and more!—throughout our AI blog series and in this blog, we’re going to do a quick recap and sum it all up.
Quick Recap of AI’s Potential in Healthcare
Throughout the seven blogs in our AI Revolution Series, we took an in-depth look at AI and how it can be implement broadly in healthcare, as well as home care, home health, and behavioral health.
1. Introduction to AI Applications in Healthcare
In the first of our blogs on the series, we looked at what AI is. It’s simple definition, is that AI is the science of making machines that can think like humans. To take it a step further, there are two types of AI: generative and prescriptive. While both have their uses in healthcare, prescriptive AI is the main focus. This type of AI has already been adapted into many technologies and begun to change how care is delivered and managed. AI can be used for predictive analysis, remote monitoring, personalized care plans, social companionship, behavioral health, and more!
2. Addressing Concerns of AI Implementation in Healthcare
In this blog, we took a detailed look at the concerns we have as advocates in implementing AI in healthcare. One of the primary concerns is the issue of privacy and data security because AI relies heavily on vast amounts of personal health information for analysis. To mitigate this, healthcare organizations can implement robust cybersecurity measures and invest in cyber security training. Another concern with AI in healthcare is the potential for bias in AI algorithms. If the data used to train AI systems is not diverse, there is a risk that these systems may develop biased algorithms that lead to disparities in care recommendations and outcomes for different populations. Steps are already being made to address this, but it is something that cannot be overlooked if the goal is health equity and better health outcomes for all. In addition to these concerns, we also need to ensure that AI ensuring that AI complements rather than replaces the human touch in healthcare, and that providers across the board have the funding necessary to build out AI infrastructure.
3. How AI Can Improve Quality Care in Home Health and Home Care
In the third blog in our series, we looked at how AI is contributing to the home health and home care sectors. AI can accelerate these industries in many ways, but predictive analysis might be the biggest benefit. By using data, AI can identify patterns and predict health deteriorations before they become critical, which means timely intervention can be made. AI can also increase the safety of those receiving care in the home by using apps like EarlySense which provides incident alerting, or VirtuSense for fall avoidance. Virtual health assistants can also be used to provide daily support like medication reminders and dietary restriction reminders, as well as walking them through wound care instructions or exercises.
4. The Benefits of Using AI in Home Health and Home Care
The fourth blog in our series looks at the overall benefits of using AI in the home health and home care sectors. The first is the creation of more personalized care plans, as AI-enabled technology can tailor care plans to the individual’s specific needs, adjusting recommendations based on their progress and responses to ensure they get the most appropriate care for their condition. But perhaps the biggest benefit is the ability to increase safety and independence for consumers. AI-powered systems can manage daily tasks that might be challenging for individuals with physical or cognitive impairments, while AI-driven medication dispensers can ensure mediation schedules are adhered to. Advanced monitoring systems can also detect unusual activities, such as falls or prolonged inactivity, and monitor vital signs, alerting caregivers or emergency services instantly for any necessary intervention. AI systems can also benefit providers by handling scheduling, documentation, billing, medication reminders, and monitoring tasks to reduce their workload and decrease burnout.
5. The Benefits of AI in Behavioral Health
In our fifth blog, we explored how AI can revolutionize how we address behavioral health issues. By leveraging AI, healthcare providers can offer more personalized, efficient, and accessible care to those struggling with behavioral health conditions such as anxiety, depression, and substance use disorders (SUDS). Predictive analytics can analyze patterns in a patient’s behavior and medical history to identify early signs of behavioral health issues, including recognizing speech and text patterns that are indicative of emotional distress or mental health crises. For those with SUDS, there are apps that detects predict an individual’s risk for relapse, and others that can serve as replacement for a group meeting setting like AA or NA. AI can also be used to treat behavioral health conditions, namely through AI-driven chatbots and mental health assistants that provider 24/7 support. However, it is important to note that many of these AI-enabled technologies do not explicitly claim to diagnose or treat medical conditions and are not regulated by the FDA so we need to ensure that the right infrastructure is in place to ensure proper implementation and monitoring.
6. Addressing Health Related Social Needs with AI
The sixth blog in our series explored how AI can transform the way we find and tackle the underlying social factors that significantly impact health outcomes, also known as social determinants of health (SDOH) and health related social needs (HRSN). Studies have shown that HRSN and SDOH are often under documented in health records. However, by leveraging AI’s data analysis and language processing capabilities, critical information can be extracted from clinical texts to identify individuals with adverse social determinants of health. But AI doesn’t just stop there. It can also be used to help providers and consumers address these needs head on. AI can connect individuals with housing support, food assistance programs, or transportation services that are tailored to their specific circumstances, and also facilitate the integration of the information into care plans. On top of that, AI can also facilitate the integration of healthcare and social services by streamlining referral processes and tracking outcomes. However, for consumers to truly benefit and health equity to advance, the integration of AI into healthcare needs to be thoughtful and ensure that AI systems are transparent, fair, and inclusive.
7. How AI and Value-Based Payments Can Increase Quality of Care
The last of our blogs in the AI series ties everything back to the core of our blog—which is value-based payments. As advocates, we love how AI enhances personalized care and operational efficiencies and believe that this aligns with VBP goals of higher quality care and cost-effective services. AI technology can reduce hospital readmissions, help providers manage chronic conditions effectively, and enhance consumer engagement. And, with AI’s data-driven insights, providers have more informed decision-making, leading to better health outcomes and, ultimately, higher reimbursements under VBP arrangements. It also allows plans and providers to reduce unnecessary services, which lower their costs and boost their bottom line. In theory, AI is a win for plans, providers, and consumers! Value-based payments can also help address two of our big concerns with AI: the loss of human interaction and the cost of providers to build out infrastructure. When health outcomes are tied to reimbursement, health plans have an incentive to invest in AI systems for their providers. And patient satisfaction is also tied to reimbursements, so providers cannot eliminate human interaction in lieu of AI-technology.
While these summaries provide a quick recap, each blog goes into much more detail. Feel free to check out all our blogs for more information!
Advocates Perspective
As we wrap up our exploration of AI in healthcare, it’s clear that AI presents the opportunity to enhance quality of and access to care, improve health outcomes, and optimize operations across the industry. From predicting patient deterioration and providing timely intervention to managing complex mental health treatments and creating personalized care plans, AI’s integration is reshaping the landscape of healthcare. However, this technological advancement does not come without its challenges. Concerns around privacy, the potential for algorithmic bias, and the essential human elements of caregiving must be carefully managed. While we are encouraged by the potential for AI, these concerns cannot be overlooked. As we continue to navigate the expansion and implementation of AI into healthcare, it is crucial that all stakeholders — from policymakers to providers to consumers — engage in ongoing dialogue to ensure that harnessing AI’s potential does not come at the cost of reducing human interaction and maintaining a diverse and accessible market for all consumers.
Onward!
Share This Blog!
Get even more insights on Linkedin & Twitter
About the Author
Fady Sahhar brings over 30 years of senior management experience working with major multinational companies including Sara Lee, Mobil Oil, Tenneco Packaging, Pactiv, Progressive Insurance, Transitions Optical, PPG Industries and Essilor (France).
His corporate responsibilities included new product development, strategic planning, marketing management, and global sales. He has developed a number of global communications networks, launched products in over 45 countries, and managed a number of branded patented products.
About the Co-Author
Mandy Sahhar provides experience in digital marketing, event management, and business development. Her background has allowed her to get in on the ground floor of marketing efforts including website design, content marketing, and trade show planning. Through her modern approach, she focuses on bringing businesses into the new digital age of marketing through unique approaches and focused content creation. With a passion for communications, she can bring a fresh perspective to an ever-changing industry. Mandy has an MBA with a marketing concentration from Canisius College.