AI in 2024: Welcome to the 'new normal' in healthcare

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Summarise 'The value-based care market is projected to grow to $174 billion by 2032, according to a MarketResearch.biz report released recently. As the march to VBC accelerates, there are operational, financial, data complexity/accessibility, and technology-related challenges that need to be overcome to implement value-based programs and to scale them for mass adoption. Existing medical data is not fully exploited for analytics and risk score computation because of three main reasons: Unstructured data – this requires multi-model processing with a combination of AI/ML plus amalgamation with the structured and semi-structured data sets. Data gaps – many times, there are missing datasets that prevent creation of a good quantitative model. It sits in data silos, and privacy concerns restrict access to this data. The No. 1 issue mentioned above can be addressed using NLP and ML algorithms that digitize, plus train and process, data in conjunction with the data in the enterprise systems for an entity. We can then share permissioned data with the granularity (full record or only sub-set attributes) between participants in a permissioned manner. The second issue can be addressed using Generative AI by creating synthetic datasets. Synthetic data is generated by using algorithms to create data that mimics real-world data, but with variations that allow for more extensive testing and analysis. The third issue (keeping the data privacy and security issues in mind) can be solved by replicating only the pertinent data. In addition, this issue also can benefit from a Federated Learning (FL) approach to machine learning. FL enables gaining insights in a collaborative manner using a form of a consensus model, without moving patient data beyond the firewalls of the institutions in which they reside. Instead, the ML processes occur locally at each participating institution and only model characteristics like the parameters, gradients, etc., are transferred to participating entities. Instead of gathering data on a single server in a centralized fashion, the data remains locked on their individual Enterprise infrastructures and the algorithms and only the predictive models travel between the servers of the participating entities – never the data. When it comes to VBC, Gen AI can be utilized in a few critical areas: Intelligent contract builder process(es). The VBC contract process today is very time-consuming and manual to develop, review, and put into action. Gen AI processes can help streamline this approach. Using past contracts against which we can build and run large language models (LLMs), Gen AI can generate new contract based on past patterns. Individual components of these contracts, such as different variables and their values, pricing information, attributes of different clauses, expiry dates, etc., can be extracted out of complex and lengthy contracts within seconds and presented to the user with a simple-to-use workflow in which users can finalize the contract within days. Improvements in care management process(es). Care management strategies for patients center around the effective use of data, processes, and systems by a team usually comprised of physicians, nurses, CBOs (community-based organizations), care managers, and social workers. The basic concept is to have timely interventions for patients to reduce health risks and decrease the total cost of care. Personalized care plans for patients broadly fall under four steps: Population stratification, using risk-stratification methods Alignment of care management services to the needs of the patient (i.e., created while interacting with the patient in a personalized manner to ensure buy-in into the plan) Preparation of care plan and device monitoring for the patient for proactive care Association of appropriate personnel to establish care plan team for execution, follow-ups, etc. Gen AI isn’t needed for patient risk stratification, which can be achieved using simple data analytics, post-any data digitization (if needed for unstructured datasets), using a patient Longitudinal Health Record (LHR). The challenge starts with contacting and engaging the patient. The communication protocols (emails, phone calls, SMS/MMS messages, snail mail) require persistent efforts to yield results. Gen AI can help personalize outgoing communication (conversational AI) based on past patient interactions, including any language translation preferences and level of education of the individual to keep the communication simple to understand. Once the patient has been engaged, a care team can prepare the care plan and put the device monitoring/data collection protocols in place. Non-clinical and administrative steps like medication reminders, scheduling appointments on time, scheduling check-ins for a telehealth conversation, creation of alerts and notifications when things do not go as planned, Rx refills, and prompting for daily exercise under the care plan – all can be personalized and automated using Gen AI. For the Internet of Medical Things (IoMT), Gen AI could help companies create more personalized and patient-centered devices – incorporating software that allows for preventive maintenance and repairs. The last part would be to help the care team navigate the complexity of the healthcare system – different workflows, assignment of the appropriate personnel based on their availability and expertise, and providing insights to the care team about patients who are not yet that sick but could be if meaningful interventions don’t happen on time. Other Gen AI Successes A good number of use cases are being worked on using Gen AI. Some are in research/concept stages, while a few are being deployed into production pilots, including automation of administrative tasks, prevention of costly medical errors, medical education, and clinical decision support. For VBC specifically, building solid VBC contract processes and improving care management workflows are just two of the ways the technology already is impacting the acceleration of new value-based payment models. '

ChatGPT

ChatGPT

The value-based care (VBC) market is expected to reach $174 billion by 2032, with various challenges hindering its widespread adoption. These challenges include unstructured medical data, data gaps, and data privacy concerns. To address these issues, Natural Language Processing (NLP) and Machine Learning (ML) algorithms can be employed to digitize and process data, while Generative AI can generate synthetic datasets for testing and analysis. Federated Learning (FL) offers a privacy-preserving approach to collaborative machine learning.

Gen AI, or Generative AI, can play a crucial role in the VBC landscape. It can streamline the time-consuming VBC contract-building process by utilizing past contracts and large language models (LLMs) to generate new contracts. Additionally, Gen AI can enhance care management processes by personalizing outgoing communication, automating non-clinical and administrative tasks, and creating more patient-centered Internet of Medical Things (IoMT) devices.

In patient care, Gen AI aids in personalized communication, care plan preparation, and device monitoring. It can automate tasks like medication reminders, appointment scheduling, and daily exercise prompts. The technology also helps engage patients through conversational AI based on past interactions, translation preferences, and educational levels.

Beyond patient care, Gen AI contributes to the IoMT by creating personalized devices with preventive maintenance features. Furthermore, it assists care teams in navigating the complexities of the healthcare system, optimizing workflows, and providing insights for timely interventions.

Various use cases of Gen AI are in progress, ranging from automation of administrative tasks to medical education and clinical decision support. In the context of VBC, Gen AI is already impacting the acceleration of new value-based payment models by improving contract processes and care management workflows.

You

SUmmarise 'This past year was a pivotal one for artificial intelligence in healthcare, of course, with healthcare provider organizations participating in significant new developments that promise to transform healthcare delivery in the years to come. The coming year promises to introduce new layers of technological and regulatory nuance as policymakers and healthcare organizations grow more sophisticated in their understanding and use of AI, and as they acclimate to the "new normal," says Ronen Lavi, CEO and cofounder of Navina, a developer of AI technology that works with primary care data. Healthcare IT News interviewed Lavi, who offered three trends healthcare leaders should be aware of going into 2024: one on "meaningful use" of AI, one on explainable AI and another on greater adoption of tech-enabled, advanced payment models. Q. You have said that this year there will be early steps toward what you call "meaningful use" for AI. Please explain. A. 2023 saw the emergence of AI into mainstream workflows across industries with the launch of OpenAI's ChatGPT. With it, analysts predicted paradigm shifts in labor, with particular emphasis on the middle class "knowledge worker." In medicine, we saw countless releases of AI-enabled functionalities, ranging from ambient dictation, task management and clinical decision support. When electronic health records came to the scene, regulators responded with incentive programs to help accelerate the transition from paper to digital records, with the objective to increase interoperability, quality and safety of patient care. With every exam room now equipped with a computer or tablet containing troves of patient information across a variety of platforms, clinicians are increasingly turning to AI-powered systems for summarization and insights. Today, AI-powered data summarization and exploration has become increasingly accessible to clinicians. We anticipate regulators will see an opportunity to incentivize the usage of these solutions that enable "a complete view of the patient" to increase safety and quality of care. Q. There certainly is a wariness in healthcare toward so-called "black box" systems. You suggest that in 2024 responsible, explainable AI will be king. Please elaborate. A. During 2023, clinical and healthcare IT leaders witnessed an unprecedented number of creative examples for how to incorporate AI into clinical and administrative workflows. However, a year in, these leaders have grown more sophisticated and have developed a deeper understanding of what AI can – and cannot – do for their organizations. While software developers continue to push the boundaries of what AI can do, when it comes to healthcare, nothing can replace the clinician-patient experience. Despite overhyped promises of systems that can overcome staffing challenges, the reality is that in a clinical setting, AI will augment rather than replace human interaction in the exam room. A year into the hype cycle, we are well aware of the dangers associated with large language model-based AI "hallucinations." These risks are especially profound in a healthcare environment, and clinicians are seeking ways to "de-risk" their interaction with information coming in from multiple sources and AI engines, both for patient safety and from regulatory liability perspectives. With President Biden shining a light on regulatory responsibility in AI, we expect the AI systems that will emerge victorious in the contentious battle for clinician adoption will be those that explain AI-driven insights and enable the final verdict to be that of the clinician. Q. On a different front, you predict the inflationary environment will lead to greater acceleration and adoption in 2024 of tech-enabled, advanced payment models. How so? A. Amid the technological and pharmaceutical advances we saw during 2023, the third salient theme of the year was rapid inflation. The result is costs rising across labor, procedures and drugs. 2023 saw significant movement in the value-based care investment space, with Aledade securing significant financing, and Walmart allegedly in talks to acquire ChenMed as two examples. We anticipate the current trends across providers and health plans toward value-based care will only increase in an environment where every stakeholder is feeling the pressure of rising costs. Health plans will react by further investing in programs designed to reign in total cost of care. With many health plans already involved in care delivery as "payviders," these organizations are well positioned to continue making the push away from fee-for-service. We anticipate hospitals and health systems faced with labor shortages and rising costs, historically lagging when it comes to value-based payment arrangements, will feel the pressure from health plans – as well as their own balance sheets – to consider accelerating the move. To make this transition successful, healthcare leaders will seek out advanced technologies to support clinical staff tasked with new workflows and documentation requirements. 2023 saw a rapid acceleration in technology ranging from software to pharmaceuticals. 2024 promises to be a year where we witness further advancement and new dilemmas that arise from maturity and widespread adoption of these early developments. With a foggy macroenvironmental future, we anticipate healthcare will double down on total cost of care initiatives, with emphasis on tech-enabled, value-based care strategies.'

ChatGPT

ChatGPT

In 2023, artificial intelligence (AI) made significant strides in healthcare, with developments expected to reshape healthcare delivery in the future. As the healthcare industry continues to evolve in its understanding and use of AI, three key trends are anticipated for 2024, according to Ronen Lavi, CEO and co-founder of Navina, an AI technology developer focusing on primary care data.

"Meaningful Use" of AI:

• The year 2023 witnessed the integration of AI into mainstream workflows across various industries, including medicine.
• AI functionalities, such as ambient dictation, task management, and clinical decision support, became prevalent in healthcare.
• With electronic health records now widespread, clinicians are increasingly turning to AI-powered systems for data summarization and insights.
• Anticipated trend: Regulators are expected to incentivize the meaningful use of AI solutions that provide a comprehensive view of patient information, aiming to enhance safety and quality of care.

Explainable AI (XAI) Dominance:

• In 2023, healthcare leaders became more sophisticated in their understanding of AI's capabilities and limitations.
• Despite innovative applications, there is a growing wariness toward "black box" AI systems in healthcare.
• The reality is that AI will augment, not replace, human interaction in clinical settings, and concerns about AI-driven insights' risks and regulatory liabilities have heightened.
• Anticipated trend: Responsible, explainable AI will be crucial for clinician adoption, with AI systems that provide clear explanations of their insights gaining preference.

Greater Adoption of Tech-Enabled, Advanced Payment Models:

• In 2023, rapid inflation affected costs across labor, procedures, and drugs in healthcare.
• Significant movement in the value-based care investment space occurred, with organizations like Aledade securing financing and Walmart reportedly considering acquiring ChenMed.
• Anticipated trend: Rising costs will drive health plans and providers towards increased adoption of value-based care models, with a focus on controlling the total cost of care. Hospitals and health systems may accelerate the move towards value-based payment arrangements, leveraging advanced technologies to support new workflows and documentation requirements.

As the healthcare industry grapples with the challenges and opportunities presented by AI, 2024 is expected to witness further advancements and dilemmas arising from the maturity and widespread adoption of AI technologies. In the face of an uncertain macroenvironment, healthcare is projected to intensify efforts in total cost of care initiatives, emphasizing tech-enabled, value-based care strategies.