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From data to decisions: using GenAI in population health management

Discover how Generative AI is revolutionizing healthcare by boosting data integration, enhancing patient care, accelerating drug discovery, and improving public health outcomes.

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CIOL Bureau
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The Indian economy is undergoing a profound transformation as digitalisation reshapes traditional industries, priming its landscape for a more technology-centric future. Among the segments recording substantial change propelled by Generative AI (GenAI), healthcare remains one of the frontrunners.

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Potential for Transformation

In India, GenAI has the potential to limit the demand-supply gap in clinical and non-clinical staff. A report notes that the use of GenAI in healthcare and life sciences could boost India’s GDP by an incremental $64 billion by 2030. Accordingly, integrating GenAI into the nation’s healthcare ecosystem holds the long-term potential to transform the industry. 

Integrating GenAI in healthcare is particularly essential due to the aging populace, rising prevalence of chronic diseases, and longstanding health disparities that put a substantial strain on the healthcare system. Public and private healthcare entities are increasingly expected to deliver efficient, patient-centric care while surmounting varied healthcare hurdles. Herein, GenAI is perfectly poised to improve patient outcomes, enhance operational efficiencies and bolster public health programmes.

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Understanding GenAI’s role in public healthcare requires analysing and addressing several elements that impact health outcomes. These include socioeconomic conditions, environmental influences, lifestyle behaviours, genetic predispositions and more. However, are healthcare systems in India and other geographies primed to meet the challenge?

Meeting Current Challenges

Take one of the most urgent universal challenges now affecting the global populace – climate change. The World Health Organization reveals that more than 7 million annual deaths occur due to climate change-linked health issues. Yet, globally, healthcare systems struggle to manage restricted resources while meeting the growing demand for personalised care. Studies show that 60% of healthcare entities confront challenges in the integration of health data, hindering their capacity for informed decision-making to deliver effective care.

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In this context, GenAI provides a promising solution by boosting data integration, predicting disease outbreaks and accelerating R&D to enhance the efficiency of healthcare systems and curb costs. While similar statistics are currently not available in India, a study (quoted in the Journal of the American Medical Informatics Association) revealed that AI-driven tools could curb annual healthcare costs by as much as $16 billion in the US.

Keeping the benefits in mind, healthcare organisations should deploy these advanced technologies to obtain deeper insights, enhance decision-making and, thereby, deliver better care outcomes to their communities. Managing public health effectively calls for a seamless collaboration between patients, providers and allied stakeholders.

In such situations, GenAI is perfectly placed to meet common challenges in the domain by ensuring an efficient, more integrated and impactful approach to public health management.

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Data Integration and Contextualisation

GenAI integrates data effectively from diverse sources, including electronic health records, social determinants of health, environmental variables, etc. Such comprehensive data fusion allows a more holistic view of public health. Contextual understanding is then driven by analysing large datasets, which helps GenAI discover patterns and correlations that typically escape human eyes. This facilitates more informed decision-making and precise interventions.

Risk Stratification

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Early identification and proactive management of disease are primary pillars of risk stratification. By analysing patient data, GenAI can pinpoint people with a high risk of developing specific ailments or suffering adverse health outcomes. Early identification facilitates timely intervention with preventive measures. Thanks to GenAI’s predictive abilities, healthcare providers can anticipate health issues, manage chronic conditions proactively and prevent ailments, ensuring cost savings plus better patient outcomes.

Personalised Care Plans and Interventions

This approach is based on customised care and a preventive focus. With GenAI, personalised health plans can be curated as per each patient’s distinct needs, preferences and risk factors. A personalised approach enhances patient engagement and compliance with treatment plans. Coming to preventive care, GenAI helps limit the incidence of disease and improves public health outcomes by discovering preventive care opportunities through screenings, vaccinations and lifestyle interventions.

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Patient Engagement

AI-powered digital assistants offer personalised support and health information to patients. AI assistants answer queries, fix appointments and remind patients about medication and appointment schedules. The delivery of accessible, real-time support improves patient satisfaction levels, ensuring higher engagement vis-à-vis care plans and health management.

Operational Efficiencies

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Greater operational efficiencies are ensured with streamlined administration and optimised resource allocation. Since administrative tasks like claims processing, coding and scheduling are automated via AI, healthcare professionals remain free to focus on patient care. Resources can also be allocated more efficiently as data is analysed in real time, lowering operational costs and boosting patient outcomes.

As healthcare companies deploy the power of GenAI in integrating data, stratifying risk and engaging patients more effectively, it helps establish a collaborative care system that adapts to evolving public health needs. 

Decoding GenAI’s Applications

Today, GenAI is transitioning rapidly from a conceptual word to an indispensable tool that is redefining healthcare deliveries and outcomes. While multiple applications are possible with GenAI, there are some interesting empirical use cases.

For instance, the pandemic highlighted major vulnerabilities in the global healthcare ecosystem. The sheer lack of preparedness saw the world struggle to mobilise resources to combat the spread of the virus. Both healthcare organisations and governments were hampered by inadequate data and the lack of predictive tools. In such scenarios, GenAI can act as a robust tool to analyse huge datasets, including mobility patterns and epidemiological trends, helping predict outbreaks before any escalation. Its actionable insights can also allow healthcare systems to foresee pandemics, efficiently allocate resources and respond proactively, curbing both the economic and societal impact.

Another excellent use case is in expediting the discovery and development of drugs. Traditional drug discovery methods are costly and time-consuming, generally requiring a decade to launch any new product in the market. Conversely, researchers can identify promising compounds speedily and more efficiently as GenAI tools swiftly process immense datasets from genomic research, clinical trials and current drug databases. In this way, GenAI can accelerate drug discovery innovation, helping meet pressing healthcare needs.

In Summation

To leverage the full potential of GenAI in healthcare, companies must make investments in requisite infrastructure while integrating advanced AI solutions and inking strategic collaborations with the best technology providers. The right partner is crucial to address ethical concerns like data privacy issues and algorithmic bias, which is imperative to foster trust and ensure the equitable, transparent use of AI. This will help in driving faster adoption while harnessing GenAI’s transformative power.

By managing the complexities of modern healthcare issues responsibly, governments and healthcare organisations can use GenAI to boost preparedness, personalise care and lower overall costs. The future of healthcare undoubtedly lies in deploying newer technologies while simultaneously delivering more productive and purposeful care outcomes.

Authored By: Ravinder Singh – SVP, Citius Healthcare Consulting and Jeewan Mohta – Senior Healthcare, Citius Healthcare Consulting