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AI enables better, more inclusive healthcare

AI enables better, more inclusive healthcare

Time of India6 days ago

The pandemic was pivotal to
healthcare
's digital transformation. Overnight, locked-down populations switched to telemedicine to consult their doctors.
The pharmaceuticals industry leveraged the computing power of advanced digital technologies – cloud computing, machine learning and advanced analytics – to crash vaccine development timelines.
India used its vast digital public infrastructure to manage the world's largest vaccination drive against Covid-19.
This also proved beyond doubt the role of digital technologies in bridging the healthcare gap, which in 2021 saw 4.5 billion people worldwide having inadequate access to essential health services. Here are some ways in which healthcare systems can use advanced technological solutions to progress towards
universal health coverage
:
Improving access to healthcare services and medical records
Enabling online consultation, patient monitoring and treatment, telemedicine is dramatically easing healthcare access for patients who live in remote, underserved locations, or are excluded due to other reasons.
The telemedicine market in India, estimated at $1.10 Bn in 2022, is projected to grow rapidly to touch $5.15 Bn in 2030. The government is spearheading the growth in digital health: The Health Ministry's national telemedicine initiative, eSanjeevani, has served more than 377 million smartphone users thus far.
The Ayushman Bharat Pradhan Mantri Jan
Arogya Yojana
, offering free treatment benefits to those above the age of 70, had digitally onboarded 370 million users at the end of March 2025.
Smart, wearable fitness devices and mobile health apps can track health and physiological data, such as heart rate, blood pressure, blood oxygen level, calories burned and sleep pattern, which can be shared with healthcare providers for regular monitoring and proactive intervention in time of need.
What's more, AI-powered health coaches analyse a range of health and fitness parameters to offer personalised recommendations and motivation for improving diet, exercise, sleep and wellness.
An important way to streamline healthcare delivery is ensuring seamless information flows across the healthcare ecosystem. AI, combined with other digital technologies such as blockchain, integrates data across healthcare entities, including clinics, hospitals, testing labs and insurance companies, to improve care coordination, reduce duplicate testing, and allow patients unbroken access to health records from any location.
The use of a blockchain ledger protects sensitive healthcare data from tampering, theft and accidental exposure, while allowing it be securely accessed by owners (patients) and their authorised healthcare providers.
Easing the burden on resources
Weak affordability, poor awareness, and lack of proximity are major reasons why so many people do not get adequate medical care. However, an overburdened healthcare system is also a big contributor to the healthcare gap.
The
World Health Organization
projects a shortage of 11 million health workers by 2030, mainly in the poorer countries. Artificial intelligence solutions can address this challenge to a great extent by automating myriad tasks to free up time that physicians and other healthcare workers can utilise for attending to patients.
The latest AI tools can analyse massive medical information, including imaging data, to produce diagnoses that are faster and more accurate than what human beings can achieve, even in the case of complex ailments such as cancer, neurological disorders and cardiovascular disease.
Hospital analytics solutions can analyse patient traffic, hospital capacities, and other data to optimise resource allocation and improve patient coverage.
Natural Language Processing – a specialised branch of AI that enables machines to understand and communicate in the natural language used by humans – can further ease the burden on providers: By automatically extracting critical data from patients' health records at the point of care, NLP frees up physicians' time and also keeps clinical documentation up-to-date.
Providers can also use NLP to process unstructured patient data to identify suitable candidates for clinical trials much more efficiently. NLP-based translation apps can break down language barriers between care givers and patients to improve communication and treatment delivery.
Predicting disease incidence and resource needs
Predictive analytics solutions leveraging AI can analyse population data to identify at-risk segments, enabling proactive, targeted intervention. At an individual level, the solutions can identify patients with a higher likelihood of developing complications or needing readmission, and recommend preventive measures, including lifestyle changes and follow-up care.
Predictive models play a key role in precision medicine by helping to personalise treatment plans – for example, what medicine to use, in what doses – based on patients' health parameters and response to earlier treatments.
Other applications of predictive AI include forecasting resource requirements and predicting metrics, such as bed occupancy, to optimise staffing and allocate hospital resources effectively.
Medical NLP is supporting predictive analytics by uncovering hidden patterns in reports faster and more accurately, to yield new insights into diseases and treatments that providers can leverage to improve healthcare outcomes.
Universal Health Coverage by 2030, featuring among the United Nations' Sustainable Development Goals, is still a distant dream. Worldwide, billions of people continue to lack access to medical care.
Even as governments and healthcare systems take action at multiple levels, from expanding medical infrastructure to improving awareness, to address this challenge, artificial intelligence and other advanced digital technologies can come to their assistance by improving access, automating activities and anticipating resource requirements.
This article is written by
Venky Ananth
, EVP and
Global Head
of Healthcare, Infosys
(DISCLAIMER: The views expressed are solely of the author and ETHealthworld.com does not necessarily subscribe to it. ETHealthworld.com shall not be responsible for any damage caused to any person/organisation directly or indirectly)

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