We asked our experts and thought leaders what their predictions were surrounding AI in 2024. There was no lack of participation and so much that we had to create a second post. If this is any indication, it looks like AI in 2024 will continue to demand all the attention.
And join us for the next few weeks as we look at what we might see in 2024.
The key to AI adoption: acceptance among patients: As we move into 2024, a key challenge to artificial intelligence (AI) adoption will be acceptance among patients, particularly as cases of AI ‘getting it wrong’ are widely publicized in the media. In 2023, AI became a familiar concept, but next year, the industry needs to take the next step of supplying clarity, transparency, repeatability, and answers around AI’s strengths and weaknesses directly to patients. To do so successfully comes down to using AI when it makes sense – not always – and framing the added value AI can create. The gap between what AI can do in theory, and what it actually does in the real world will only be closed if we address barriers to access and acceptance among patients. In 2024, the industry needs to be specific about the value AI creates; efficient and seamless in its delivery for providers and patients; and resolute in the use of insights to help make decisions – only then will we see acceptance and the next step of adoption.
The Anti-Prediction of AI in 2024: The healthcare industry likes to compare artificial intelligence (AI) to a standard of perfection – we want it to get the answer ‘right’ every time, and sometimes, we’ll even hear this claimed as a ‘goal.’ The reality is, this won’t happen next year (or the next, or the next!). In 2024, we must compare what AI can do to the world as it is – real patient journeys, real diagnostic procedures and (lack of) uptake, and real treatment decision-making under tremendous uncertainty. AI will always disappoint if we expect it to be perfect, but it can be incredibly helpful if we use patients’ lived experiences as our baseline to create well-supported solutions that address real problems.
Successful AI in 2024 fueled by interest, maturity, and willingness: There is more momentum in artificial intelligence (AI) than the industry has seen in a while. Similarly, unlike previous upsurges, this momentum feels more mature – less about experimentation and ‘shiny objects’, more about real-world applications at scale. In 2024, the industry will see well-functioning AI up and running in clinical care pathways and large-scale settings, especially where AI is being used to surface ‘hidden’ or lost patients for diagnosis and access to effective treatment. While healthcare leaders have been talking about these applications for a long time, the next year feels like it has the right mix of interest, technical maturity, and willingness to invest for AI to really evolve.
Healthcare help isn’t about the chatbots
We’ve listened to over 200 million customer conversations this year. U.S. healthcare customers — whether Medicaid, Medicare, commercial or private — are not asking for more chatbots. Those features don’t equate to CX. Customers are asking for their questions to be answered, for their promises of return phone calls to be returned and for CX technology to work effectively so they don’t miss a medication dose. It isn’t that we don’t have a way to diagnose the problem correctly, we do — through conversations and the unstructured data they generate. Leveraging AI to analyze this unstructured data at scale will give leaders better insight into what’s actually happening in their customers’ experiences.
Our understanding of the problem is incomplete when we don’t factor in the literal voices of customers. Unstructured data was never accessible before, but it’s accessible now — in a way people can understand — with new technology and AI adoption.
In 2024, providers and health systems will be intensifying efforts to accelerate cash flow, placing a heightened focus on the CFO and their revenue cycle. Their key strategy will involve deploying an optimal blend of AI, automation, and process improvement to alleviate margin pressure. Clinicians will benefit from AI assistance in easing patient charting burdens, while payers will leverage AI to enhance precision in care payments.
Amidst the influx of new technologies, including the likes of LLMs and bots, CFOs and CIOs must engage critical viewpoints, separating genuine, practical solutions from mere promises. The goal is to implement solutions that yield tangible results, making life a bit less challenging for clinicians.
Proliferation of generative AI to better predict outcomes: Artificial Intelligence is maturing in the healthcare realm, evolving beyond diagnosis to become a predictor of healthcare outcomes. With generative AI, providers can anticipate patient outcomes more accurately, enabling timely, proactive decision-making and personalized interventions.
Top 4 impact-drivers for generative AI in 2024
2024 will be a watershed year for generative AI, particularly in healthcare. Look for solutions emerging in four key areas in 2024: lightening administrative burdens across the hospital; helping sharpen clinicians’ decision-making; boosting the efficiency of medical researchers; and helping the next generation of healthcare workers ramp up proficiency with smarter learning tools.
Although the adoption of AI in healthcare is nothing new, there will continue to be a growing need for AI technology in 2024 and beyond. With an overall lack of manpower in healthcare, as seen in nursing and staff shortage trends, AI looks like the best solution for retaining existing manpower at competitive compensation rates while increasing efficiency in workflow and improving clinician job satisfaction. According to the National Library of Medicine, the key to successful AI implementation is to do it in a clinically relevant way that clinical caregivers can get behind. It’s not only about the technology, it’s about how technology and caregivers work together in a trusted way to believe in, train, and commit their AI solutions to provide long-term value.”
There is no one-size-fits-all AI solution for streamlining healthcare data, as each health institution has their own unique approach and associated needs. In 2024, health leaders should avoid generalized “AI platforms” with nebulous value and instead, take a more nuanced approach. Given the excessive hype around AI at the moment, health leaders should cut through the hype by selecting solutions that provide value-based/risk-bearing engagements as opposed to SaaS contracts; that will prevent health providers from bearing the risk of the technical execution. At this point, AI has reached a degree of maturity that should no longer require providers to take a bet on a technology with a multi-million dollar SaaS contract. Technology companies should put their money where their marketing is. A successful, tailored solution requires consultation with experts familiar with the intricacies of healthcare data to employ techniques such as brute force mapping to create a stable system, while adjusting the approach periodically to keep up with changes in technology and management.
A health system will be sued for improperly leaking PHI and other information to the large language model providers. CIOs and leaders will be forced to come up with a solution for generative AI, rather than looking the other way.
The use of Large Language Models (LLM) in healthcare will continue to increase in 2024, especially in cardiology, which is among the specialties that utilize AI the most. LLM, in particular, will be used to help alleviate physician burnout and stress by scanning reports and records for information to inform treatment decisions. For example, the technology can review a dataset with information on millions of patients within seconds and extract or summarize information based on requests. These insights can be leveraged to inform treatment decisions, which will ultimately improve patient outcomes. With the rise in remote cardiac monitoring, it is critical to find ways to make reviewing data and results from devices more efficient. In the coming year, we will see additional cardiology practices integrating LLMs into their workflows to save time, paving the way for more efficient processes. There’s no doubt these models will revolutionize the way we process data and information across every specialty in the healthcare industry.
Artificial Intelligence (AI) was perhaps the buzzword of 2023, and 2024 will likely be no different. But as the use of AI becomes more widespread, we will see more in the development of ethics and policies for artificial Intelligence-augmented data utilization and predictive tools, especially in the healthcare space. As those policies and ethical rules are introduced, we will likely see broader use of health chatbots and virtual assistants to inform patients about novel diagnostics or procedures.
Responsible use of AI in digital treatments is imperative and should be patient-centric, with a focus on data privacy and the objective to develop personalized treatment options with minimal bias. AI in regulated digital treatments will increasingly be seen as much more reliable and trustworthy because of FDA’s regulation of SaMD, whereas other applications of AI in the healthcare industry do not have the same regulatory oversight. As a result, AI in regulated products will be best positioned to earn patient and provider trust.
As the current administration looks to regulate safe and ethical AI practices in healthcare, we need to ensure process measures yield better health outcomes. I believe the impact AI can have on clinical care is further out than many predict, however by promoting innovation and technological advancement in concert with responsible regulation, we can unleash its potential to drive better outcomes and revolutionize healthcare while also maintaining the utmost standards of safety and ethical conduct.
AI is crucial in 2024 and beyond for evidence-based medicine, enabling the expansion of care and improving access by swiftly analyzing vast datasets, identifying patterns, and providing actionable insights to enhance medical decision-making and patient outcomes.
Artificial intelligence technologies are increasingly enabling data digitization, prediction analytics, and interoperability of digital healthcare data. These capabilities have the potential to transform healthcare. It is critical, however, that use of AI in healthcare is handled responsibly. The best way to adapt and evolve to address new emerging cyberthreats is to adhere to industry standard best practices, while also layering in new technologies and strategies of responsible AI to fortify defenses and create proactive elements into enterprise security. Focusing on the four foundational aspects of Responsible AI – fairness, transparency, empathy, and accountability – will not only allow enterprises to be more secure, but also will benefit patients, providers, and all associated healthcare organizations.
The only thing worse than missing out on transformative change is diving in headfirst without a plan. Healthcare leaders today have the opportunity to proactively shape the responsible development of AI through engaged partnership, ensuring positive returns on investment through optimized care and improved efficiencies. The smart leader will focus first on proving value with targeted use cases and pre-defined outcomes tied to priorities like cost, quality and efficiency, and then use those wins to build trust necessary to unlock AI’s full enterprise potential.
Building trust into healthcare GenAI: content quality is the critical driver in 2024
The global consultancy McKinsey has said that Generative AI “has the potential to reimagine the healthcare industry in new and exciting ways.” Leveraging GenAI with clinical decision support could provide a way for providers to make faster and better decisions. We predict as healthcare explores GenAI and patient care, the source and vetting of the underlying content will become a key factor in the speed in which it is deployed. It is said that innovation happens at the speed of trust – in healthcare that trust is based on the quality of the underlying content and its evidence base.
AI-Driven Predictive Analytics
Confronted with budget constraints and persistent staffing challenges limiting their ability to increase census, providers will increasingly embrace technology to enhance operational efficiencies. In the coming year, I expect a significant increase in the integration of cutting-edge technologies, including AI and predictive analytics to arm care teams with the insights they need to predict trends in the health of patient populations, possible disease outbreaks, and individual patient risk factors. Looking ahead, senior care providers will continue to embrace the opportunity to proactively allocate resources, such as personnel, equipment, and medications, where they are most needed, to help in increasing top line revenue, to improve efficiency, and patient outcomes.
In the ever-changing realm of global health challenges, emerging technologies like AI hold tremendous potential to revolutionize healthcare. By leveraging these advancements, we can achieve greater accuracy, accessibility, and economic sustainability in healthcare. AI can propel us toward a healthier world, where quality care is no longer a privilege but a fundamental right for all. To capitalize on this opportunity, we must employ AI safely and responsibly to ensure a future where everyone receives the healthcare they deserve.
Responsibly Implementing AI
The healthcare sector must discover optimal ways to harness AI, complementing healthcare professionals to improve patient outcomes without diminishing the human element. The objective is not to supplant human roles but to harness AI’s additional value to aid staff, patients, and their families. Properly executed, AI and automation can cut down non-clinical workloads, freeing up professionals to focus on direct patient care. The industry’s aim for 2024 should be this collaborative approach and ensuring data safety as we advance this remarkable technology.
- As customers experience the integration of personalized user technology in other industries, they’ve come to expect the same when entering healthcare facilities. In response, health organizations will further prepare for the increasing demands from patients in 2024 who seek the inclusion of digital technologies and AI throughout their care journey.
- Advanced interactive patient engagement systems (IPS) utilize patients’ personalized mobile devices to align with the in-room experience, providing a virtual connection to loved ones and providers, and access to entertainment, meal ordering, and non-clinical room control. In 2024, this digital integration is expected to extend further beyond the hospital stay, continuing the patient engagement even after discharge.
- At times, technology can get in its own way. There’s so much we can do technology-wise, but it has to be balanced against what the patient really needs. Productive digital interactions supplied by an IPS, such as healthcare alerts, reminders, and communications, can provide real-time updates and check-ins for patients to remain engaged with their care as they engage in positive distraction activities such as watching television or playing games.
- As a whole, patients don’t want to be in the hospital, and from a cost and wellness standpoint, organizations don’t want them there either. Health systems seek to efficiently heal patients and reduce avoidable admissions. This is where advancing telemedicine can play a big role. With evolving use cases before, during, and after a hospital admission, virtual care will continue to evolve as fantastic tool to easily engage patients, optimize clinician time, and reduce both length of stay and readmissions.
Readying health data for AI prime time
Health organizations are making it a strategic priority to extract more value from the volumes of data they’re moving to the cloud. One big health data target in 2024? Training AI models on all that data. To make it happen however, they’ll first need to untangle a rat’s nest of messy data. In the race to build data-rich AI models in ’24, many will turn to AI-driven terminology tech that speeds normalization of data, making it ready for prime-time.
As more service providers incorporate AI into their software, it is critical for healthcare organizations to understand exactly how AI is being used within their own four walls, what data is being accessed by AI, and why the AI is necessary. Accordingly, healthcare organizations must prioritize regular audits of their processes and their third-party tools and services to ensure that patients’ personal health data is not at risk.