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CX Trends in Healthcare in the Middle East Region

The healthcare landscape in the Middle East has significantly transformed in the past few decades, driven by changing demographics and rapid digitalization. 

This blog explores the demographic insights from the region, the recent changes in digital healthcare, emerging customer experience (CX) trends, and strategies for healthcare companies to adapt.

Demographic Insights from the Region

The Middle East is a diverse region with varying healthcare needs and challenges. Understanding the demographics is crucial for healthcare providers and policymakers. Here are some key insights:

Population Growth: The demographics of the Middle East and North Africa (MENA) region show a highly populated, culturally diverse area spanning three continents. The class, cultural, ethnic, governmental, linguistic, and religious makeup of the region is highly variable.

From a CX standpoint, this poses exciting challenges for companies assisting the digitalization of the healthcare industry. On the one hand, technology needs to be modern and intuitive. On the other hand, the functionalities must be simple enough for the slightly aged population to use easily.

How Digital Healthcare has Evolved

The COVID-19 pandemic has accelerated the adoption of digital healthcare solutions in the region, as patients and providers sought to access and deliver healthcare services remotely and safely. According to a report by McKinsey, the percentage of consumers using telemedicine in Saudi Arabia and UAE increased from 9% before COVID-19 to 41% during COVID-19. Moreover, 80% of consumers said they would likely use telemedicine again post-pandemic.:

  • Telemedicine Adoption: Telehealth platforms have gained popularity, offering remote consultations, especially during the COVID-19 pandemic. OKADOC is a UAE-based platform connecting users with healthcare providers across the MENA region. OKADOC lets users find and book appointments with doctors, clinics, and hospitals online.
  • Health Apps: There’s been a surge in health and wellness apps, allowing patients to monitor their health and access information conveniently. GetBEE, a UAE-based platform that offers online consultation and coaching services, will enable users to access online sessions with experts in various fields, such as nutrition, fitness, wellness, and psychology. 
  • Electronic Health Records (EHRs): The adoption of EHR systems has improved data management and patient records accessibility.

The evolving healthcare landscape in the Middle East is leading to emerging CX trends:

As digital healthcare solutions become more prevalent and accessible in the region, customers expect more from their healthcare providers regarding quality, convenience, transparency, and personalization. Some of the emerging CX trends that are influencing the healthcare sector in the region are:

  • Customer-centricity: Customers want to be treated as individuals with unique needs and preferences. They want to have more control over their health choices and outcomes. They also want more access to information and feedback about their health status and treatment options. Nabta Health, a MENA-based application providing women’s health and wellness solutions, perfectly encapsulates this need. Nabta Health combines AI, blockchain, and IoT to offer personalized and holistic care for women. 
  • Omnichannel integration: Customers want seamless and consistent experiences across channels and touchpoints. They want to switch between online and offline modes without losing context or quality. They also want to have a single point of contact for all their healthcare needs. 
  • Value-based care: Customers want to receive value for their money. They want to pay for outcomes rather than inputs. They also want more transparency about the costs and benefits of different healthcare services. For example, the Egypt Ministry of Health’s Universal Health Insurance System is a comprehensive reform that aims to provide universal health coverage to all citizens by 2030. The system is based on a social health insurance model, where providers are contracted and paid based on the quality and outcomes of care they deliver.

To cater to these evolving trends, healthcare companies should consider the following strategies:

  • Invest in Technology: Allocate resources to implement advanced healthcare technologies such as AI, telemedicine, and EHR systems. Mantra Labs has worked extensively with prominent Healthcare providers in India and the USA to deliver top-notch successes for customers and patients. 
  • Training and Education: Healthcare professionals should be trained to use digital tools and provide compassionate care effectively.
  • Data Security: Ensure robust data security measures to protect patients’ sensitive information. 
  • Patient Engagement: Foster patient engagement through mobile apps, feedback systems, and personalized communication. Having an ecosystem approach with a 360-degree patient engagement plan is a must. 

Conclusion:

The Middle East region is at the forefront of healthcare transformation, with changing demographics and digitalization driving new CX trends. Healthcare companies that adapt and invest in these trends will meet patient expectations and provide more efficient and effective healthcare services.

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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.

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