Experts believe that there is potential in personalising medical treatments
As artificial intelligence (AI) tools and platforms are evolving from the labs to mainstream, entire industries across the globe are embracing it to transform operations. India is no exception, with PwC projecting local enterprises’ AI expenditure to reach USD 11.78 billion by 2025. NASSCOM, meanwhile, predicts that AI and data analytics can contribute USD 30 billion to the country’s GDP in the same period.
India’s decision-makers are aware of AI’s potential to take healthcare to new heights, and have put their money where it counts. The Ministry of Health and Family Welfare (MOHFW) and the Department of Health Research (DHR) received allocations of INR 89,155 and 2,980 crore, respectively, to undertake research and innovation-based initiatives.
The role of AI and data in healthcare
However, the use cases of AI in the health sector are not universal, but rather contextual to the focus. Academic medical institutions and large health systems, for example, have different priorities and, thus, different ways of applying AI. The latter would be focused on ensuring operational efficiency, while the former would prioritise research. Ultimately, however, both are concerned with improving patient care and accessibility to medical attention.
Regardless of priorities, AI’s potential rests largely on its ability to find patterns in tremendous amounts of unstructured, semi-structured, and structured data just like human intelligence. Healthcare organisations are massive repositories of data, but are unable to fully leverage them because they are stored in multiple formats such as images, medical claims, and clinical research trials. This is where AI can have the maximun impact helping to curate in different formats in healthcare by applying techniques such as natural language processing, image recognition, and data integration. These techniques can enable the extraction, analysis, and sharing of valuable insights from various sources of health data, such as electronic health records, medical images, and wearable devices.
Simply put, the plethora of ways AI is beginning to reshape the healthcare sector is profound. A study by PwC divides these applications into four categories; patient-centric services, care delivery, disease surveillance, and research and development (R&D).
AI’s sophisticated models can delve deep into enormous volumes of medical data to assist decision-making and provide more effective treatment plans. There is also great potential for care delivery, as many tasks traditionally performed by healthcare practitioners and technicians can now be automated, enhancing efficiency and accuracy.
In terms of disease surveillance, AI enhances epidemic intelligence and prevention measures. It combines traditional disease surveillance techniques with additional information from public sources. This multilayered data offers comprehensive insights and triggers early alarm bells to appropriate regulatory bodies, thereby enabling better preventative measures.
In terms of R&D, AI models can boost pharmaceutical research. AI models can greatly reduce experiment turnaround times, giving thorough analysis of trial results to gain valuable insights into the effectiveness and safety of different drugs.
Revolutionising patient satisfaction
The expanding presence of Artificial Intelligence (AI) in the healthcare industry promises to revolutionise patient experiences, starting with earlier detection and diagnoses. Machine learning models, for instance, are now capable of monitoring symptoms and reducing errors in diagnostic imaging.
There is also immense potential in personalising medical treatments through the customisation of care and treatment strategies based on a patient’s medical history, genetics, and social factors. This extends to around-the-clock access to personalised virtual assistants that respond to patient needs based on their records and preferences. AI can also enhance patient education by making the delivery of health information more impactful and engaging.
AI also has the potential to foster transparency regarding healthcare costs and benefiting both patients and insurers by tackling fraud and optimising coverage. Last but not least, AI can also enhance public health initiatives by mapping at-risk populations through a wide range of genetic, behavioural, and social factors.
Preparing healthcare for AI
To position themselves to get the most out of AI, healthcare organisations must prioritise data management and DataOps. DataOps is a set of practices that aims to improve the quality, speed and reliability of data analytics. In healthcare data management, DataOps can help to address some of the common challenges, such as data silos, data security, data governance and data integration. By applying DataOps principles, such as automation, collaboration and continuous improvement, healthcare organizations can streamline their data pipelines, ensure data quality and compliance, and deliver faster and more accurate insights to support decision-making and patient care.
Healthcare entities can prepare themselves for AI adoption by partnering with solution providers to unearth practical applications with clearly articulated results and viable avenues for successful AI introduction without committing valuable resources.
FAIR(Foundry for AI at Rackspace) collaborates with organizations and provides valuable support for the adoption of AI by providing expertise, resources, and solutions that are tailored to the specific needs and goals of each organization.
Looking to the future, AI offers avenues to further humane healthcare provision. Embarking on this journey will not merely introduce AI into a healthcare setting, but continuously refine its application to continually enhance patient experiences and outcomes.
