Article

Decoding Gen AI as an enabler

For population health management (PHM)

Jeewan Mohta
Senior Healthcare Consultant,
Citius Healthcare Consulting

18 - Nov

Insights

The global AI healthcare market is expected to witness a CAGR of approximately 36-38% in the next five years.
Gen AI improves population health management by integrating data, predicting outbreaks, and enabling personalized care for efficient healthcare.
Gen AI could cut healthcare costs by up to $16 billion annually, but its success depends on strategic investments while addressing ethical concerns like data privacy.

The global economy is profoundly transforming as digitalization reshapes traditional industries, setting the stage for a more technology-driven future. Among the sectors experiencing this profound change, healthcare is at the forefront, driven by technologies like Generative AI (Gen AI).

Poised for remarkable growth, with projections indicating a CAGR of approximately 36% to 38% from 2024 to 2029^[1] – the integration of Gen AI in healthcare, especially in light of the aging population, rising chronic disease prevalence, and persistent health disparities that are putting significant strain on global health systems–has become crucial at this juncture. Government and healthcare organizations are increasingly tasked with delivering patient-centered, efficient care while overcoming the multifaceted obstacles in healthcare. In this context, Gen AI is a game-changer that is well-equipped to enhance patient outcomes, improve operational efficiencies, and support public health initiatives.

Assessing the intersection of tech and population health

Understanding Gen AI's role in healthcare requires considering population health in a broader context. Population health involves analyzing and addressing various factors influencing health outcomes, such as socioeconomic conditions, lifestyle behaviors, environmental influences, genetic predispositions, etc.

But the question arises: Are healthcare systems equipped to deliver on these?

Let’s take, for instance, one of the most pressing global challenges impacting population health today–climate change. According to the World Health Organization (WHO), climate change-related health issues contribute to over 7 million deaths annually^[2]. However, healthcare systems worldwide are under increasing pressure to manage limited resources while addressing the rising demand for personalized care. Studies indicate that 60% of healthcare organizations face challenges in integrating health data^[3], which hinders their ability to make informed decisions and deliver effective care.

Here, generative AI (Gen AI) offers a promising solution.

By improving data integration, predicting outbreaks, and accelerating research and development, Gen AI can help healthcare systems enhance efficiency and reduce costs. A study published in the Journal of the American Medical Informatics Association (JAMIA) found that AI-powered tools could reduce healthcare costs by up to $16 billion annually.

As technology and population health converge, leveraging tools like Gen AI is not only pivotal but also foundational. Public health organizations must adopt these advanced technologies to gain deeper insights, improve decision-making, and ultimately deliver better health outcomes for their communities.

Data fusion: Gen AI can effectively integrate data from diverse sources, such as electronic health records (EHRs), environmental variables, social determinants of health, and more. This comprehensive data fusion enables a more holistic view of population health.
Contextual understanding: By analyzing large datasets, Gen AI uncovers patterns and correlations that may escape human analysts, facilitating more informed decision-making and precise interventions. This contextual understanding allows for better decision-making and targeted interventions.

Early identification: Gen AI can analyze patient data to identify individuals at high risk for developing certain diseases or experiencing adverse health outcomes. This early identification enables timely intervention and preventive measures.
Proactive management: Leveraging predictive capabilities, Gen AI allows healthcare providers to anticipate health events, proactively manage chronic conditions, and prevent diseases, leading to better patient outcomes and cost savings.

Tailored approaches: Gen AI can create individualized health plans tailored to patient’s unique needs, preferences, and risk factors. This personalized approach can improve patient engagement and adherence to treatment plans.
Preventive care focus: Gen AI helps reduce disease incidence and improve population health outcomes by identifying opportunities for preventive care, such as screenings, vaccinations, and lifestyle interventions.

AI-powered digital assistants: Gen AI-powered digital assistants can provide personalized support and health information to patients. These assistants can answer questions, schedule appointments, and remind patients about their medication and appointments.
Enhanced engagement: These digital assistants improve patient satisfaction by delivering accessible, real-time support, thus fostering higher engagement with care plans and health management.

Streamlined administration: AI can automate administrative tasks such as claims processing, scheduling, and coding, freeing healthcare professionals to focus on patient care.
Optimized resource allocation: By analyzing data in real-time, Gen AI helps healthcare organizations allocate resources more efficiently, reducing operational costs and improving patient outcomes.

The COVID-19 pandemic exposed significant vulnerabilities in global healthcare systems, with the economic impact estimated at $11 trillion^[4]. One of the critical challenges was the lack of preparedness and the difficulty in mobilizing resources to contain the virus's spread. Governments and healthcare organizations had to scramble for solutions in real-time, often hindered by insufficient data and predictive tools.

In this situation, Gen AI acts as a powerful tool by analyzing vast datasets, such as epidemiological trends and mobility patterns, to predict disease outbreaks before they escalate. Additionally, it provides actionable insights that enable healthcare systems to anticipate pandemics, allocate resources effectively, and respond proactively, reducing both economic and social impacts.

For example, a Gen AI-powered model could simulate various outbreak scenarios based on specific regions, weather patterns, and population movements, allowing governments to make informed decisions about lockdowns, vaccinations, and resource deployment.

Chronic diseases such as diabetes and heart disease account for nearly $4.1 trillion in U.S. healthcare spending annually^[5], representing a significant portion of healthcare costs. Current care models often take a generalized approach to disease management, treating patients with similar conditions in a one-size-fits-all manner. However, this approach overlooks the unique genetic, environmental, and lifestyle factors contributing to each patient's health outcome.

Gen AI transforms chronic disease management by shifting from generalized treatments to personalized, data-driven care plans based on patient-specific data like genomics and medical history.

For example, in managing diabetes, Gen AI can analyze a patient's health data to recommend precise dietary and exercise interventions, predict blood sugar fluctuations, and optimize medication regimens.

Generative AI (Gen AI) is revolutionizing the pharmaceutical sector by significantly expediting drug discovery and development. Leveraging traditional methods can be time-consuming and costly, often taking over a decade to launch a new drug to market. In contrast, Gen AI tools can rapidly process vast datasets from genomic studies, clinical trials, and existing drug databases, allowing researchers to pinpoint promising compounds more effectively.

Here, a notable instance is the utilization of Gen AI to predict the three-dimensional structures of proteins with remarkable accuracy. This has been a critical milestone in understanding disease mechanisms and developing targeted therapies. Therefore, Gen AI's capability not only shortens the discovery phase but also enhances the precision of drug design, paving the way for more effective treatments.

By leveraging these advanced technologies, pharmaceutical companies can reduce the time and costs associated with drug development, ultimately bringing life-saving medications to patients faster. This transformation again highlights the potential of Gen AI to address one of the industry's most pressing challenges, i.e., the need for innovation in drug discovery to meet urgent healthcare demands.

Way forward

To unleash Gen AI's full potential in healthcare, organizations must invest in the necessary infrastructure, integrate advanced AI solutions, and form strategic partnerships with the right technology providers.

Moreover, addressing ethical concerns such as data privacy and algorithmic bias remains necessary in building trust and ensuring transparent, equitable use of AI. Subsequently, these steps make for widespread adoption and in the ability to harness the transformative power of Gen AI.

By responsibly managing the intricacies of modern health issues, Government and Healthcare organizations can leverage Gen AI to enhance preparedness, personalize care, and reduce costs. Ultimately, the future of healthcare lies not just in adopting newer technologies but also in leveraging them to deliver smarter and more purposeful care outcomes.