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CX Trends for Healthcare In India, 2022

Deloitte study shows, that only 34% of consumers believe they get the information they need in virtual settings, and 56% believe they don’t get the same level of service. This created an opportunity for healthcare providers to rethink and improvise their practices to better connect with patients on their preferred channel and provide them with information and more personalized care.

Patients’ perceptions of healthcare services have shifted recently, prompting healthcare professionals to adapt to the latest technologies in order to improve the current healthcare infrastructure. Utilizing Metaverse’s virtual environment, AI, IoT devices, and collaborating with the government’s own NDHM are the latest trends in healthcare.

Let’s take a closer look at how these trends are being used to improve the entire consumer experience.

Artificial Intelligence 

Artificial intelligence (AI) is one of the major trends driving healthcare’s digital transformation. Diagnostics, academics, therapy courses, and other fields of healthcare have already implemented it. However, continuous R&D is being done to explore more possibilities for implementing AI technologies in Healthcare.

Currently, AI has proved to be very useful in diagnosing and treating diabetic retinopathy which is a major cause of blindness in India due to the huge diabetic population. For example, Netra.AI, an AI platform, can identify a healthy retina from an unhealthy one with the help of AI algorithms making use of specialized low-powered microscopes with cameras attached to them. Quickly generated reports on this platform enable optometrists to provide instant counsel to patients needing a referral to the hospital.

Computer Vision technology integrated within Conversational AI bots and virtual assistants helps medical professionals to diagnose certain diseases via remote counseling. Many experts are researching how to make the most out of Computer Vision in the field of cancer detection, surgery, and dermatology.  

Read more about how Computer Vision is transforming healthcare.

NDHM

The National Digital Health Mission with the help of the United Health Records system aims to address the lack of coordination between healthcare providers, payers, and patients. UHR brings together the electronic medical records of a patient and, most importantly, data from a cohort of different online and offline touchpoints frequented by patients. Furthermore, each person will be given a unique health ID that may be linked to the health IDs of their entire family to provide a complete picture of their medical history.

NDHM will help patients and healthcare professionals by improving longitudinal health record management and making it easier to store and share health records. Patients will be able to browse nearby healthcare providers while on the road. The NDHM framework’s features will go a long way toward ensuring that the Indian healthcare industry has a consistent experience.

Metaverse

Virtual Reality, Augmented Reality, Mixed Reality, AI, and digital currencies are all part of the Metaverse. It’s a web-based collection of interconnected locations. The metaverse is the result of the convergence of three major technological trends: telepresence (which allows people to be together virtually even if they are physically separated), digital twinning, and blockchain (which allows us to create a distributed internet), all of which have the potential to impact healthcare. Together, they have the ability to open up new channels for delivering treatment, lowering costs, and significantly improving patient outcomes.

Apollo Hospitals Group has partnered with 8chili Inc, a deep-tech start-up based in California, to enable patient involvement in the metaverse. Virtual reality (VR) will be used to provide pre/post-operative patient counseling, increasing patient involvement and offering skill mastery for hands-on training for healthcare staff.

IoT and Wearables

In the recent couple of years, wearable technology has become increasingly popular amongst urban populations for health and wellness tracking. Patient monitoring for chronic illness as well as post-op care has become easier for healthcare professionals through IoT devices and wearables. Some IoT solutions use artificial intelligence (AI) to offer clinicians early warnings based on a patient’s vital signs. Many healthcare startups such as Health Care At Home India Pvt Ltd (HCAH) are working towards setting up home ICU units and other treatment infrastructure that can monitor patients’ vital parameters thus enabling a proactive approach to treatment review and modification.

To keep track of patients recovering from high-risk treatments, Manipal Hospitals began using a remote monitoring service linked to Google’s Fitbit devices. These gadgets collect data from patients such as heart rate, oxygen saturation, sleep quality, steps, and pain score, which is then shared with nurses and doctors via an online monitoring service.

According to MarketsandMarkets, the market for wearable medical devices was estimated at $16.2 billion in 2021, with a CAGR of 13.2 percent expected to reach $30.1 billion by 2026. The rise of lifestyle-related illnesses (such as hypertension), the growing need for home healthcare, and the desire to improve patient outcomes are all factors driving this market expansion. 

Conclusion: Future of Healthcare Technology

With collaborative care models, data privacy becomes a major concern, and the danger of data loss can have serious ramifications for both the patient and the healthcare organization. 

Privacy of data can be ensured through a few specific security measures such as encryption of data and algorithms that enable access by authorized personnel only, regular monitoring of data and information to detect any data compromises, and training healthcare professionals about their data usage, access restrictions, and data security requirements. 

Ultimately, customer experience (CX) has risen to the top of the healthcare agenda because it affects every step of the patient’s journey, from interactions with doctors, nurses, and other care providers to insurance companies, pharmacies, hospitals, labs, and other healthcare businesses. R&D in every aspect of the above trends is happening at a rapid pace. Soon, a day will come when a doctor sitting in his clinic miles away from a rural village monitors a patient’s health and provides him with counsel and treatment.

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The Future-Ready Factory: The Power of Predictive Analytics in Manufacturing

In 1989, a missing $0.50 bolt led to the mid-air explosion of United Airlines Flight 232. The smallest oversight in manufacturing can set off a chain reaction of failures. Now, imagine a factory floor where thousands of components must function flawlessly—what happens if one critical part is about to fail but goes unnoticed? Predictive analytics in manufacturing ensures these unseen risks don’t turn into catastrophic failures by providing foresight into potential breakdowns, supply chain risk analytics, and demand fluctuations—allowing manufacturers to act before issues escalate into costly problems.

Industrial predictive analytics involves using data analysis and machine learning in manufacturing to identify patterns and predict future events related to production processes. By combining historical data, machine learning, and statistical models, manufacturers can derive valuable insights that help them take proactive measures before problems arise.

Beyond just improving efficiency, predictive maintenance in manufacturing is the foundation of proactive risk management, helping manufacturers prevent costly downtime, safety hazards, and supply chain disruptions. By leveraging vast amounts of data, predictive analytics enables manufacturers to anticipate machine failures, optimize production schedules, and enhance overall operational resilience.

But here’s the catch, models that predict failures today might not be necessarily effective tomorrow. And that’s where the real challenge begins.

Why Predictive Analytics Models Need Retraining?

Predictive analytics in manufacturing relies on historical data and machine learning to foresee potential failures. However, manufacturing environments are dynamic, machines degrade, processes evolve, supply chains shift, and external forces such as weather and geopolitics play a bigger role than ever before.

Without continuous model retraining, predictive models lose their accuracy. A recent study found that 91% of data-driven manufacturing models degrade over time due to data drift, requiring periodic updates to remain effective. Manufacturers relying on outdated models risk making decisions based on obsolete insights, potentially leading to catastrophic failures.

The key is in retraining models with the right data, data that reflects not just what has happened but what could happen next. This is where integrating external data sources becomes crucial.

Is Integrating External Data Sources Crucial?

Traditional smart manufacturing solutions primarily analyze in-house data: machine performance metrics, maintenance logs, and operational statistics. While valuable, this approach is limited. The real breakthroughs happen when manufacturers incorporate external data sources into their predictive models:

  • Weather Patterns: Extreme weather conditions have caused billions in manufacturing risk management losses. For example, the 2021 Texas power crisis disrupted semiconductor production globally. By integrating weather data, manufacturers can anticipate environmental impacts and adjust operations accordingly.
  • Market Trends: Consumer demand fluctuations impact inventory and supply chains. By leveraging market data, manufacturers can avoid overproduction or stock shortages, optimizing costs and efficiency.
  • Geopolitical Insights: Trade wars, regulatory shifts, and regional conflicts directly impact supply chains. Supply chain risk analytics combined with geopolitical intelligence helps manufacturers foresee disruptions and diversify sourcing strategies proactively.

One such instance is how Mantra Labs helped a telecom company optimize its network by integrating both external and internal data sources. By leveraging external data such as radio site conditions and traffic patterns along with internal performance reports, the company was able to predict future traffic growth and ensure seamless network performance.

The Role of Edge Computing and Real-Time AI

Having the right data is one thing; acting on it in real-time is another. Edge computing in manufacturing processes, data at the source, within the factory floor, eliminating delays and enabling instant decision-making. This is particularly critical for:

  • Hazardous Material Monitoring: Factories dealing with volatile chemicals can detect leaks instantly, preventing disasters.
  • Supply Chain Optimization: Real-time AI can reroute shipments based on live geopolitical updates, avoiding costly delays.
  • Energy Efficiency: Smart grids can dynamically adjust power consumption based on market demand, reducing waste.

Conclusion:

As crucial as predictive analytics is in manufacturing, its true power lies in continuous evolution. A model that predicts failures today might be outdated tomorrow. To stay ahead, manufacturers must adopt a dynamic approach—refining predictive models, integrating external intelligence, and leveraging real-time AI to anticipate and prevent risks before they escalate.

The future of smart manufacturing solutions isn’t just about using predictive analytics—it’s about continuously evolving it. The real question isn’t whether predictive models can help, but whether manufacturers are adapting fast enough to outpace risks in an unpredictable world.

At Mantra Labs, we specialize in building intelligent predictive models that help businesses optimize operations and mitigate risks effectively. From enhancing efficiency to driving innovation, our solutions empower manufacturers to stay ahead of uncertainties. Ready to future-proof your factory? Let’s talk.

In the manufacturing industry, predictive analytics plays an important role, providing predictions on what will happen and how to do things. But then the question is, are these predictions accurate? And if they are, how accurate are these predictions? Does it consider all the factors, or is it obsolete?

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