In the dynamic world of health tech, navigating the complexities of clinical data while driving meaningful innovation is a significant challenge. The pressures of maintaining data quality, ensuring interoperability, and delivering accurate, actionable insights can be overwhelming. However, NLP-driven data normalization tools, combined with clinical AI, provide a path to managing these challenges while unlocking new opportunities for growth and efficiency.
At IMO Health, we leverage rich, highly nuanced clinical terminology, extensive domain knowledge, and IMO Clinical AI, to expertly structure and operationalize clinical data. This approach generates sharper insights and informs more intelligent decision-making. But we understand that success isn’t just about adopting the latest technologies—it’s about integrating the right ones that enable your organization to innovate more efficiently.
This article provides resources with practical insights and strategies to enhance your health tech innovations while maintaining the highest standards of data quality.
Understanding the role of data normalization and NLP in healthcare
Clinical data is inherently complex, and as your health tech solutions scale, this complexity only increases. Unstructured data, in particular, poses significant challenges. Without proper management, it can quickly lead to a “dirty” data lake—an overwhelming pool of inconsistent, incomplete, and unusable information.
Our eBook, “Avoiding the dangers of a dirty data lake: The crucial roles of NLP and normalization,” explores how AI-powered processing tools can transform chaotic data into clean, structured information that drives better decision-making. By grounding our approach in practical experience, we offer insights that you can apply to your own data management practices.
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Avoiding the dangers of a dirty data lake: The crucial roles of NLP and normalization
A case study in success: CyncHealth’s improved clinical data quality
CyncHealth, a regional health information exchange (HIE), faced significant, albeit relatable, challenges with data quality. Their partnership with IMO Health was built on a shared commitment to overcoming these obstacles through the thoughtful application of data normalization and NLP.
In our case study, we detail how this collaboration improved data accuracy and operational efficiency, ultimately leading to better patient outcomes. This is not just a success story; it’s a real-world example of how our tools can be used to help solve the kinds of problems you may be facing in your own organization.
But don’t just take it from us, check out this on-demand webinar where CyncHealth’s Chief Data Officer, Naresh Sundar Rajan, shares how automating patient data cleanup allowed them to focus on improving services, products, and end-user satisfaction.
Navigating the data maze: Practical guidance for complex challenges
Transforming data into actionable insights is often complicated and challenging, with twists and turns that can leave organizations feeling lost. Inconsistencies in data capture, a lack of standardization, and interoperability issues only add to this maze-like issue.
In our on-demand webinar, “Navigating the data maze: AI, NLP and value set solutions for clinical data challenges,” we provide practical guidance on how to use AI and NLP in healthcare, underpinned by IMO Clinical AI, to navigate this complexity.
Grounded in the experience of our experts, this webinar offers insights that are directly applicable to the obstacles you’re facing today. It’s not just about learning new tools; it’s about understanding how to apply them effectively to achieve your goals.
Expert insights
Here are some of the insights and best practices designed to help you overcome the intricacies of clinical data to scale your solutions effectively:
Extracting value from unstructured data: Many health tech companies struggle with unstructured data. In our expert-led article, Figure it out or fail: Extracting the value from unstructured data, IMO Health’s Senior Director of Clinical Informatics, Amol Bhalla, MD, M.SCI, MHSA, MBA, offers practical insights into how NLP can help you turn this data into a valuable resource.
Avoiding the pitfalls of code crosswalks: Code crosswalks may seem like a quick solution for data normalization, but they often create more problems than they solve. In our blog, Proceed with caution: Why code crosswalks are built to fail, we share our first-hand experiences and lessons learned, helping you to avoid these common mistakes.
Maintaining clinical terminology: Keeping your clinical terminology up to date is a significant challenge in health tech. The article, What makes clinical terminology so tough to maintain, provides a deep dive into this issue, offering practical strategies that we’ve developed through years of experience.
Learning from failure: This industry is littered with the remnants of ill-fated startups. In 4 reasons why healthcare startups fail, we share insights into these failures and how you can avoid them by focusing on data quality and thoughtful scaling.
Security, scalability, and integration
As you grow and seek to enhance your health tech solutions and get to market faster, it’s essential to build on a foundation of security, scalability, and seamless integration. These are not just technical considerations—they are critical to ensuring that your innovations are sustainable and can meet the demands of the healthcare industry.
Security and compliance: Data security is paramount at IMO Health. Our SOC 2 Type 2 and HIPAA certification demonstrate our commitment to rigorous standards. This level of security is crucial as you scale, ensuring that your data is protected and compliant with industry regulations.
Seamless integration with Snowflake: Growing effectively requires cohesion with the right platforms. Our Snowflake integration bridges the gap in healthcare data standardization, allowing you to leverage your existing infrastructure while enhancing data quality.
Lessons from aviation: Sometimes, the best insights come from outside your industry. Explore how the aviation industry’s approach to data governance can provide valuable lessons for health tech companies.
- Scaling with SaaS: Learn how SaaS solutions can help you scale efficiently while maintaining control over your infrastructure.
Achieving scale with terminology-based normalization: A strong foundation in terminology-based standardization is essential for expanding health IT solutions. Our blog, Achieving scale in health IT: The power of terminology-based normalization solutions, offers insights into how you can leverage these tools to support your growth.
Optimizing your tech stack: Finally, don’t overlook the potential of your existing technology. The article, Looking inward: How your tech stack can help improve data quality, provides practical advice on how to make the most of your current tools to enhance data quality and operational efficiency.
Conclusion: Work smarter, not harder, and get to market faster
In the complicated and demanding field of health tech, success isn’t just about innovation – it’s about strategic, sustainable growth. By integrating NLP-driven data normalization tools and clinical AI into your processes, you can overcome the challenges of clinical complexity, maintain high data quality, and ultimately deliver better outcomes.
