Published on Nov 9, 2023

Innovation is the lifeblood of the healthcare industry, constantly pushing the boundaries of what is possible and improving patient outcomes. But with the ever-increasing complexity of healthcare, more is needed than to rely on intuition and experience to drive innovation.

According to research published by Harvard Business Review in 2023, 94% of respondents agreed that data-driven healthcare creates new opportunities for patients and doctors, allowing them to benefit from more personalized healthcare approaches. However, while the intent to adopt data-driven decisions is at its strongest, the same survey lists that only 16% of these companies can be considered mature regarding their data strategy. Just a 1% increase from a similar survey conducted four years ago.

Data-driven decision-making is now the new pulse of healthcare innovation, revolutionizing the industry in ways we never thought possible.

The Power of Data

Healthcare data

Data is the foundation of healthcare innovation and an accelerator in global data volumes. About 30% of the world’s data volume is generated by healthcare, and by 2025, the CAGR data for healthcare will reach 36%—more than any other industry—according to RBC Capital Markets.

Today, with the rise of electronic health records and the digitization of medical information, we now have access to an unprecedented amount of data. This data is used to identify patterns, trends, and insights that were previously hidden, allowing for more informed decision-making and ultimately improving patient outcomes.

The Role of Analytics

More than data is needed to drive innovation. It must be analyzed and interpreted to extract meaningful insights. Advanced analytics tools and techniques allow healthcare organizations to make sense of their data and identify patterns and trends that would be impossible to detect with the human eye.

Our clients, Abbvie, leveraged our advanced analytics and AI-driven platform to draw meaningful insights from existing research and patient data for their healthcare services. It helped them make data-driven decisions, leading to more efficient and effective processes, better patient care, and, ultimately, improved outcomes.

Collaboration and Innovation

Healthcare innovation is not a one-person job. It requires collaboration and partnership between stakeholders, including healthcare providers, researchers, technology companies, and patients.

Digital companies can play a significant role in facilitating collaboration in the healthcare industry. Here are a few ways they can help:

1. Virtual Collaboration Platforms: Digital healthcare companies can develop and provide virtual collaboration platforms that allow healthcare professionals, researchers, and other stakeholders to connect and share information effortlessly. These platforms can include features like secure messaging, video conferencing, and document sharing.

The Lazard Healthcare Innovation Consortium is a prime example of this collaboration in action. This consortium brings together leading healthcare organizations and technology companies to drive innovation and improve patient outcomes. By working together and sharing data and insights, these organizations can develop new treatments and technologies that are difficult on their own.

Closer home, the Connect 2 Clinic platform built by Mantra Labs boasts collaboration-focused features that help its doctor base of over 40,000 to share information and insights digitally easily.

2. Integration of Electronic Health Records (EHR): Digital healthcare companies can develop platforms integrating electronic health records from different healthcare providers. This integration enables seamless sharing of patient information, enhancing collaboration among healthcare professionals and improving the continuity of care.

Leading digital healthcare company Innovacer provides a Health Pulse platform that helps unify patient records from multiple touchpoints track and provides insights from the same.

Mantra Labs has recently helped a leading insurance giant integrate ABHA, the government’s initiative towards creating unique health IDs for all Indian citizens, with their health insurance platform. This will help make access to patient history and relevant data more seamless.

3. Telemedicine and Remote Monitoring: Digital healthcare companies can offer telemedicine solutions that allow patients to consult with healthcare professionals remotely. This not only improves access to healthcare services, especially in remote areas but also promotes collaboration by enabling healthcare professionals to collaborate on patient care across different locations.

The Future of Healthcare Innovation

Data-driven decision-making is not just a trend in healthcare; it is the future. As technology advances and more data becomes available, the potential for healthcare innovation is limitless. With the help of advanced analytics and collaboration, we can expect to see even more groundbreaking treatments and technologies that will revolutionize the industry and improve patient outcomes.

The Importance of Data Security

Data security

With the increasing reliance on data in healthcare, it is crucial to ensure that this data is kept secure.

Healthcare organizations must invest in robust data security measures to protect patient information and maintain patient trust. This includes implementing encryption, access controls, and regular security audits. By prioritizing data security, healthcare organizations can continue to leverage data for innovation without compromising patient privacy.

Recently, in India, there was a breach that led to the compromise of data of 81.5 million Indians and their AADHAR details. This is a worrisome fact considering the government’s bid to move towards digital health records.

In 2022, 49.6 million Americans were affected by healthcare data breaches. Hacking ranks as one of the most damaging and impactful types of data breaches for healthcare payers and providers.

Companies like Cisco, Symantec, and McAfee provide cybersecurity solutions that combat modern challenges.

It’s essential for healthcare organizations to carefully evaluate their security needs and choose a solution that best fits their requirements.

In conclusion, data-driven decision-making is the new pulse of healthcare innovation. By leveraging data, analytics, and collaboration, healthcare organizations can drive innovation and improve patient outcomes.

As we continue advancing technology and data, the potential for healthcare innovation is limitless. Are you ready to embrace data-driven decision-making in your organization?

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?

Data-Driven Decisions: The New Pulse of Healthcare Innovation

The Power of Data