AI in healthcare

The digital health industry has accelerated significantly in recent years, and - according to the latest data - is projected to grow at a CAGR rate of 18.6% from 2023 – 2030.

Shameem C Hameed, founder and CEO of blueBriX, believes that AI is and will continue to be one of the major drivers of growth in the digital health industry from 2023 to 2030. However, it is not the only factor.

Digital health and ageing populations

While AI has the potential to revolutionise various aspects of healthcare, such as diagnostics, personalised medicine, drug discovery, and remote patient monitoring, there are a number of different factors that will actually contribute to or be considered as key drivers for the accelerated growth in digital health, with a prime example being ageing populations.

According to the latest data from the World Health Organisation, 1 in 6 people will be aged over 60 by 2030, with the number of people in this age category forecast to more than double to 2.1 billion by 2050. As such, the global healthcare industry will be required to adapt to this demographic shift, ensuring it can effectively care for growing numbers of people with conditions that commonly develop with age.

Here, advances in digital health, like AI based solutions, could play a fundamental role in delivering remote or tele-based care to those with ‘geriatric’ conditions, thereby ensuring patients receive the support they need without draining the time and resources of healthcare professionals within hospital settings.

With rising healthcare costs, increased demand for IoT and wearable devices, advances in genomics and precision medicine, and regulatory changes also considered as key drivers for growth, it could easily be argued that AI actually offers the solution and will, therefore, be part of a broader ecosystem of factors that contribute to the overall expansion of the digital health industry.

The future of AI in the healthcare sector

I believe that AI will play an increasingly important and exciting role within the healthcare industry over the coming years, and – if developed and used correctly - has the capability to transform vital functions and improve the standards of healthcare worldwide.

For example, where AI algorithms can analyse medical imagery and other diagnostic data with high precision, it can also accelerate the process of drug discovery, development, and trials - all leading to improved patient outcomes and reduced healthcare costs.

Furthermore, AI also has the potential to deliver personalised healthcare by analysing genomic data and other relevant information to tailor treatment to individual patients, while helping to predict and improve patient outcomes when such data is reviewed on a larger scale.

However, the use of AI within healthcare isn’t restricted to improving patient outcomes. The right AI powered tools are also likely to be used to deliver enhanced patient care, with IoT, wearable devices, and AI-powered healthcare assistants on hand to provide remote support and patient monitoring, thereby reducing the burden on providers without compromising standards of care.

Finally, AI will also have its part to play in improving operational efficiencies within healthcare settings by optimising workflows and streamlining tasks such as appointment scheduling and billing, while also helping to detect and prevent security breaches in highly sensitive data systems.

It’s important to note that, while the future of the healthcare industry looks bright with AI, the successful integration of AI in healthcare will rely heavily on collaboration between technology developers, healthcare providers, and regulatory bodies alike. Furthermore, as AI continues to advance, ethical considerations and data privacy concerns will also need to be addressed to ensure responsible and equitable use of AI in any healthcare setting.

A question of trust

I think that both healthcare professionals and patients alike may be hesitant to trust AI-driven systems, particularly when it comes to critical decision-making processes. For example, although many people are amazed at what AI can do, most remain sceptical and do not trust in its capability, as demonstrated by the concept of self-driving vehicles.

That said, the introduction of platforms like ChatGPT have started to shift this opinion in recent months, with many people now using AI as a tool for research and inspiration in both their professional and personal life.

Ultimately, there is a lot to gain from AI in healthcare, particularly in emerging countries like India, where AI-based tools could deliver healthcare support, information, and guidance en masse - and at an affordable cost. The key to success, however, is ensuring that the use of AI remains positive and that it is not subject to evil or misuse.

Integrating AI into existing digital health solutions

There are many challenges to consider when integrating AI into existing digital health solutions and the wider healthcare industry. From experience, I believe that the following are the most common and problematic:

• Data privacy and security: Ensuring the privacy and security of sensitive patient information is crucial when integrating AI into healthcare systems. Strict regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States and the General Data Protection Regulation (GDPR) in the European Union, must be adhered to, which can be complex and time-consuming.
• Interoperability: Healthcare systems often involve multiple entities, including hospitals, clinics, laboratories, and pharmacies, each with their own data formats and systems. Integrating AI solutions requires seamless communication and data sharing across these entities, which can be challenging to achieve.
• Regulatory approval: Obtaining regulatory approval for AI-driven digital health solutions can be a lengthy and costly process. Regulators need to ensure that these solutions are safe, effective, and compliant with relevant regulations.
• Technical expertise: Most Pharma or healthcare providers do not have the internal skillset, resource, or expertise in both tech and healthcare to develop, implement, and maintain AI solutions that will effectively connect and integrate with their existing healthcare ecosystem.

Adaptation for brand loyalty

Currently, digital health solutions launched by pharma are mostly very static and cater to only one type of patient and limited phases of their treatment journey. As a result, patient retention in digital health initiatives is low, with studies showing an 80% drop-off rate in patient app usage. This can be a significant setback for ROI on digital health spend.

With the help of AI, pharma can launch patient engagement apps that are personalised and relevant to each individual patient. These apps can deliver the right message at the right time to the right patient, thereby increasing patient engagement and retention.

We have seen how personalisation has transformed consumer habits, such as engagement with and use of social media apps, which has had both positive and negative impacts. As per the recent publication from McKinsey, the same AI methods used to increase user engagement with social media apps can be leveraged for improved patient engagement through treatment and lifestyle habits, increased innovation and collaboration, and patient adherence and support.

Ultimately, by adapting to and integrating AI-enabled digital health solutions, pharma can create a more customer-centric approach, improve product development, and streamline operations. This can help them increase brand loyalty, drive revenue growth, and maintain a competitive edge in the market, meaning AI is something to be embraced, rather than ignored.