In the media

From data to decisions: Leveraging AI for improved healthcare outcomes

06 September 2024

South Africa's Medscheme processes over 12 million claims each month, with an average of more than three claims per beneficiary among its 3.9 million members. The large volume, speed, and diverse data involved classify these entities as big data. This sets medical schemes apart from other types of insurance because they deal with massive amounts of frequent transactions.

The effective use of this data is rapidly transforming the operation of medical schemes in the field of healthcare. It entails integrating the big data with sophisticated AI and machine learning (ML) algorithms, which, however, also presents ethical and systemic challenges.

Role of AI in medical schemes

The primary role of AI in medical schemes is to analyse and interpret large datasets to improve healthcare delivery and optimise costs. Specifically, machine learning enables the identification and prediction of patterns, trends, and relationships within extensive data sets. These insights empower schemes to recommend the optimal course of action, often autonomously or with human input, to achieve desired health outcomes.

AI can predict the sequence of healthcare events following a specific diagnosis. This may include automatically enrolling a member with a chronic condition into a management program, renewing prescriptions, or coordinating rehabilitation at network facilities. This predictive ability is essential for personalising healthcare and developing individualised care plans.

The AI models are designed to continuously learn and self-improve, adjusting their predictions based on observed errors using methods like backpropagation and refining their accuracy over time. This iterative learning process enables the recommendation of the next optimal action with increasing precision, thereby enhancing the effectiveness and personalisation of the member experience.

Due to their complexity, however, the inner workings of AI models are often described as "black boxes," even for the models' creators. This lack of clarity is a significant concern in the healthcare sector, where understanding the reasons behind AI decisions is essential for maintaining patient safety and trust.

This is also changing: researchers at MIT, for example, have developed techniques to harness the power of AI models as interpreters to automate the explanation of intricate neural networks. This helps explain complex neural networks, making it easier to understand models like GPT-4.

Benefits

This will have specific benefits in medical schemes, aiding in designing an environment for transforming how care and services are provided. The changes will enhance the quality of healthcare systems, primarily in three areas.

First is the development of such platforms like AfroCentric’s Automated Health Advisory™ recommendation systems platform seek automate personalised and timely interventions using simplified communication. By using a form of generative AI, Generalised adversarial networks (GANs) models, we automate repetitive decision-making processes, sort information, create new data samples using stochastic processes, and make predictions.  This provides personalised and timely communication to members, improving accessibility and tailoring information to their specific needs, thus improving their healthcare journey.

Second, is the cost-effectiveness of AI-driven care. By predicting healthcare events and supporting these predictions with automated advisories, members are guided towards optimal, cost-effective care paths. For example, identifying members who contribute the most to service queries and healthcare costs enables schemes to address these segments holistically, thereby improving both the quality of care and efficiency. This results in reducing members’ out-of-pocket expenses and improved overall health outcomes.

Finally, AI enables the modification of members' behaviour by integrating with digital platforms and communication engines. By providing real-time, actionable insights, AI empowers members to make informed decisions about their health, fostering a more proactive and engaged member base. This shift towards a member-centric model not only enhances individual well-being but also aligns with the broader goals of medical schemes, delivering value and sustainability.

Challenges

The advent of AI also brings with it a number of challenges, for example, in terms of transparency and ethical implementation.

A responsible approach requires implementing a robust governance framework to accompany this technological progress that includes AI design principles, algorithm review, data management, risk and impact assessment, and adherence to regulatory standards. It is essential to ensure transparency and explainability of AI decisions to maintain trust and guarantee fair and equitable AI-driven recommendations.

While AI offers significant advantages in healthcare, including enhanced diagnostic precision and personalised treatments, medical schemes must pay attention to the ethical considerations and potential biases associated with its use.  This requires ensuring that the quality and diversity of their data sources are sufficient to mitigate potential biases that could undermine the efficacy of AI solutions.

To overcome these challenges, healthcare organisations and regulatory bodies must work together to implement comprehensive frameworks governing AI. In so doing, schemes can leverage the potential of big data and AI's capabilities to enhance healthcare outcomes while mitigating potential risks and ensuring equitable care for all patients.

Conclusion

The integration of AI and big data into the medical scheme industry presents a unique opportunity to transform healthcare delivery. By leveraging AI's predictive capabilities and vast data insights, medical schemes can provide personalised, efficient, and cost-effective care. However, the success of these technologies is contingent upon their ethical implementation, with a particular emphasis on transparency, accountability, and the mitigation of systemic biases. As AI continues to evolve, it promises to reshape the future of healthcare in South Africa, making it more accessible, equitable, and responsive to the needs of all members.