Chris Hansen is a visionary leader in healthcare data engineering, analytics, and warehousing, leveraging his deep expertise to transform how clinical data is managed and utilized. As Head of Data Engineering at Q-Centrix, Chris plays a critical role in shaping data strategy and pioneering innovative use cases to maximize the value of data within the Q-Centrix Enterprise Clinical Data Management platform.
Q-Centrix is at the forefront of revolutionizing clinical data management, empowering healthcare organizations with best-in-class solutions to enhance patient care, streamline operations, and drive informed decision-making. Chris’s passion for harnessing data to improve healthcare outcomes has been instrumental in advancing analytics capabilities and unlocking new opportunities for hospitals and health systems.
In this exclusive Q&A, Chris Hansen discusses Q-Centrix’s approach to clinical data strategy, the evolving role of AI in data engineering, and how the company is helping healthcare providers navigate the complexities of data-driven decision-making. He also shares insights on the future of healthcare analytics and the transformative impact of innovative data solutions.
Q: Could you share a bit about your professional journey and how you first became interested in data engineering?
I started my career as a technical writer for computer manuals. I got a job writing a standards and best practices guidebook for a data department, and during that engagement, I realized that I was very interested in that work. My next role was at a startup company where I had the opportunity to wear many hats – one of which was being the data guy. I never looked back and have worked in data ever since.
I spent the first 10 years working in retail data, but when I had the opportunity to work in healthcare, I quickly saw the impact my work could have on saving lives (not just on saving money). Healthcare data is complex and messy, but I love the challenge and the improvements it can unlock when utilized well.
Q: As Head of Data Engineering at Q-Centrix, what do your main responsibilities entail? How do you prioritize tasks and projects in a complex healthcare environment?
One of my first and primary objectives at Q-Centrix was to organize data from clinical registries into a standardized data structure that could enable clinical insights and workflow improvements and be used for insights beyond registries. I led a cross-functional team in defining the data, modeling it, moving it into a new data warehouse, and validating that it was accurate. With much of that work complete, we are enabling exciting new technologies such as AI and automation.
At the same time, I had to prioritize other data needs, such as internal reporting and capturing new data sources. My team and I work closely with other business units to determine their objectives and timelines so we can decide together which projects need our attention first.
Q: Healthcare data is notoriously complex and highly regulated. What unique challenges do you face when designing and maintaining Q-Centrix’s data infrastructure?
Despite working in healthcare data for over a decade, I cannot pretend to be an expert in all aspects. My team and I have relied on support from Q-Centrix’s clinical data experts who have worked side by side with the technical team to answer questions and provide definitions. Forming relationships with our clinical teams has been pivotal in ensuring the accuracy of our data, something that Q-Centrix focuses on in every aspect of our business.
Q: Can you describe your team’s approach to ensuring data integrity and security throughout its lifecycle? Which tools or frameworks have proven most effective?
My team works closely with our security and infrastructure teams to make sure that all clinical data is moved and stored securely in accordance with HIPAA requirements. Our data is encrypted and only accessible through secure networks by approved personnel.
We have also created a de-identified database to ensure our clinical research team can study the data without encountering Personally Identifiable Information (PII). As an organization, Q-Centrix is proud to have met all requirements for SOC2 + HITRUST compliance. The highest security designation in the industry.
Q: Q-Centrix aims to help healthcare organizations improve quality and patient outcomes. How does your data engineering strategy directly contribute to achieving these goals?
Capturing and submitting registry information for a patient is only the first step in how Q-Centrix uses structured data sets to improve patient outcomes and quality. We also have used the registry data to create reports for hospitals to understand the data and its accuracy.
We are also using data to identify cases that need to be submitted more quickly and AI to automate some registry questions. When you understand that 80% of all healthcare data is unstructured, the potential uses for these structured data sets is infinite.
Q: What does innovation mean to you in the context of healthcare data?
Discovering new ways to use data to unlock healthcare improvement, efficiencies, and improved outcomes. There are many opportunities to improve healthcare through data, and innovation means always taking a creative approach to solving these problems.
Q: In an industry where patient privacy and security are paramount, how do compliance requirements shape your architecture decisions? Are there trade-offs between compliance and performance or scalability?
One of the most significant ways that the need for privacy shaped our decision-making was by creating two databases – one with patient data for approved healthcare personnel to use and the other being de-identified so that researchers and analysts could safely use the data. In general, I’ve had to give more thought to data access controls and alternatives (such as de-identification and data masking) than in any prior industries I’ve worked in.
There is a trade-off because needing to do that extra work to protect the data impacts your ability to move quickly onto new projects. However, it is pivotal and completely worth it to protect patient’s private healthcare information.
Q: Looking at the broader data engineering field, what trends or emerging technologies are you most excited about and how do you decide which innovations to bring into Q-Centrix’s tech stack?
I am excited about new cloud automation technologies that will handle some of the more repetitive aspects of the job. One innovation I have been following recently is Zero-ETL from AWS, which has the potential to free my team up from some of the more mundane aspects of moving data into the data warehouse so that they can focus on creating analytics and providing decision support for the business.
I have also been following AI developments closely and am excited by the early promise of processing text and summarizing data. So much healthcare data is contained in unstructured blocks of physician notes or lab results, which AI could help break down into structured elements. Of course, proper measures must be taken to ensure that patients' private records are not being used to train AI models, risking accidental future exposure, so AI security is an important future development as well.
Q: Data analytics and AI are reshaping many industries. From your vantage point, how do you see them transforming data engineering within healthcare specifically?
AI has the potential to excel at making decisions regarding how data should be organized and structured. For example, AI could parse through a data set and determine the best data types for each element of data. It also shows incredible promise in summarizing and organizing text, so I expect we’ll soon see models that can provide diagnosis and procedure codes based on physician notes.
While I watch AI with great excitement, I also recognize an extreme excitement about AI – which has the unfortunate consequence of creating an environment of buzzwords and over-promises. I am old enough to have lived through the “Big Data” craze when many people predicted those technologies would completely replace traditional data engineering.
That obviously didn’t happen, but what did is that people discovered what Big Data tools were best suited for, and the technologies behind them have since significantly enhanced data engineering rather than replaced it. I suspect we are living through a similar phase with AI right now.
However, the industry will soon discover what it excels at and what it is not well suited for, and then we will use the best models to create the next generation of tools. At Q-Centrix, our current approach is a thoughtful use of AI as a support for our team of clinical data experts.
Q: Collaboration is key for large-scale data initiatives. Which teams or departments at Q-Centrix do you work most closely with, and how do you ensure smooth communication?
Cross-functional teamwork is pivotal when working with data. My team does not create any data – it is sourced from our products and systems. My team also does not use most of the data we work with – rather, it is provided to the business users and leaders to provide insight and enable them to make the best decisions.
My team collaborates closely with our product teams to understand how their data is organized and used so we can source it efficiently. We work closely with the end users to understand what they are trying to accomplish so that we can provide all the data needed in a report or dashboard.
For clinical data, my team works closely with clinical subject matter experts who can explain the data and unwind the complexity. At Q-Centrix, we decided to embed some of these clinical experts directly with my team of data experts to provide governance and validation to their work. I regularly check in with the leaders of the teams that my team works with to ensure that their needs are being met and that communication is flowing smoothly.
Q: What’s been your proudest moment or achievement at Q-Centrix so far?
I am incredibly proud of our clinical data warehouse team. They have excelled at working across teams with data architects, clinical subject matter experts, data engineers, data scientists, and clinical researchers to create a robust data structure that securely stores all our clinical data. And I’m not the only one to recognize them. In 2024, they won a Tech in Motion Community Choice Timmy Award for Best Technology Team!
Also, as a member of the Q-Centrix DEIB team, I am incredibly proud to see people from so many diverse backgrounds working together to organize and structure clinical data in a way that will significantly impact the industry. No one person can think of all the aspects that a healthcare database needs to cover. Having so many perspectives has been a huge advantage in designing and building the most robust system we can.
Q: Could you pinpoint a project or milestone that stands out?
Q-Centrix has used HL7 interface data for years to pre-populate the answers to specific registry prompts. That was already a significant enhancement to the process of manually entering every piece of data. But we knew we could do more with that rich clinical data, so we got to work with our product teams to enhance our case creation and case-finding processes based on the data we were receiving.
We were able to produce cases faster, with fewer manual steps, and with more accuracy than our previous methods. This accomplishment demonstrates the power of data to improve workflow and let team members focus on less redundant tasks.
Q: Finally, looking ahead, where do you see healthcare data engineering in five years?
Thanks to likely improvements through AI, I think healthcare data engineering will be able to shift focus away from much of their manual work.
There will be less need to store and process large blocks of healthcare text when AI can process it and break it into relevant and meaningful components. I don’t envision AI replacing data engineers but rather taking over the repetitive aspects of the job so that our people can focus on delivering even more robust data structures to help inform the best possible healthcare decisions.
Q: What steps is Q-Centrix taking now to stay ahead of future challenges and capitalize on upcoming opportunities?
Q-Centrix provides many opportunities for team members to learn and stay ahead of new technologies. We have dedicated days to develop new ideas and provide regular training on new technologies.
We have dedicated internal channels for learning and offer team members the chance to present internally how they’ve utilized technology to solve problems. We are investing heavily in the future by bringing in new teams dedicated to AI.
This investment enables us to evaluate new technologies quickly and make decisions about the long-term support of the company’s evolving needs. I am confident that through these efforts, we will continue to be able to stay at the forefront of clinical data management.