AI Revolution: From crisis to catalyst in healthcare transformation

The healthcare space is at the precipice of a colossal transition. The COVID-19 pandemic catalysed the need for innovation that betters healthcare management and delivery. Today healthcare systems worldwide are fencing with industry challenges, such as rising costs, increasing patient volumes, and the growing complexity of medical conditions. 

Leaders in the healthcare domain are constantly juggling these challenges – whether it’s about improving the quality of healthcare service, maintaining consistency in the overall standards, or carefully charting a journey keeping stringent regulatory mandates in mind. Amidst this chaos, artificial intelligence emerged as a transformative force that has reshaped the industry as we know it. 

Why AI in healthcare?

AI, with its ability to process extensive data instantaneously, has opened possibilities that a human can only dream of. Whether it’s automating tasks or extracting critical insights from gargantuan amounts of data at the click of a button, AI can effortlessly do it all. 

An increasing number of healthcare organisations today are blending AI into their operations. It is predicted that 90 per cent of hospitals will use AI for tasks such as early diagnosis and remote patient monitoring by 2025. It also adds to the business value. Wider adoption of AI could lead to savings of 5 per cent to 10 per cent in healthcare spending—roughly $ 200 billion to $ 360 billion annually in 2019 dollars — within the next five years, according to a paper recently published by the National Bureau of Economic Research. 

Ways AI can boost efficiency in healthcare ops

By leveraging AI, healthcare domains can experience a considerable improvement in their everyday workflows while also bringing down costs. Here are some ways AI can help enhance efficiency in healthcare operations:

• Administrative workflow: Clerical, administrative tasks take up a considerable amount of time and resources in the healthcare domain, most of which are rather mundane. With AI in the mix, these tasks can not just be executed efficiently but healthcare professionals can focus on direct patient care. 
• Predictive analytics: Banking on AI/ML learning and pattern formation capabilities, the systems are capable of diagnosing potential diseases, alongside enhanced diagnostics. 
• Meaningful AI conversations: AI’s LLMs (Large Language Models) and NLPs (Natural Language Processors) today are more advanced than ever. In the form of chatbots, they can offer more human-like experiences which can be used for digital consultations, or support, enabling meaningful conversations and decision-making. 

Striking the human-AI balance

Adoption of AI solutions isn’t about replacing the human workforce, but about adding a layer of support that frees the bandwidth of the existing human workforce to enhance patient interaction and care. It is important that leaders in the healthcare space carefully sift through tasks that can benefit from the AI mix and the level of efficiency and streamlining that they bring to an organisation’s workflow. 

What is equally crucial is the continuous upgrade of workforce skillsets in the form of training, workshops, etc. that can guide them in making the most of AI tools in their everyday work. This way not only will they possess the latest skillsets needed in the AI world while also retaining their essential clinical and interpersonal skills. 

Ethical considerations for AI use

Harnessing the power of AI in the healthcare domain feels like a godsend. However, it also demands careful ethical considerations, specifically in the areas of bias, transparency, and accountability. 

In the healthcare industry, patient data security and informed consent are non-negotiable. To adhere to this, there is a strong need for comprehensive anonymisation techniques. Additionally, the data that are being used to train the AI must be wary of any kind of biases. Failing to do so can lead to unfair outcomes for certain population groups. 

Healthcare leaders must also ensure that they maintain transparency in AI systems, making operations and decisions easily accessible and understandable for both patients as well as providers. 

Lastly, accountability must be duly highlighted with clear definitions of responsibilities and liabilities, along with appropriate regulations and oversight,  in the case of an unforeseen event or error.

The AI future of healthcare

The AI revolution in healthcare is set to reinforce a variety of pillars in the healthcare domain, revolutionising healthcare and patient care as we know it. The road, albeit challenging, is worth exploring. A powerful collaboration between healthcare providers, policymakers, and technologists is what charts the course for a future that both augments as well as humanises healthcare delivery.