Innovating Clinical Data Use for Hospitals, Payers, and Patients Through the Q-Centrix Research Network

Milton Silva-Craig, CEO of Q-Centrix, discusses the recently launched Q-Centrix Research Network and how it addresses clinical research barriers, data safety, and quality assurance. 

Transcript: 

Please introduce yourself by sharing your name, title, organization, and experience. 

My name is Milton Silva-Craig and I've been the CEO of Q-Centrix for almost 10 years. My professional background has broadly been in the health care IT market, and I’ve worked in medical imaging systems, revenue cycle management, and now clinical data management. 

Please share a brief overview of the Q-Centrix Research Network and how the platform partners with pharmaceutical companies/trial sponsors.  

First, I want to give some context and background on Q-Centrix and who we are. We're experts at curating high-quality clinical data. We leverage a series of capabilities that begin with our clinical data experts. We have more than 1,200 clinical data specialists with expertise in cardiology, oncology, trauma, stroke, orthopedics, etc. You name the clinical area, and we likely have expertise there. Our software platform helps efficiently capture and structure data for utilization and provides insights into that clinical data. Our primary purpose is to help our customers, which are hospitals, generate greater value from their clinical data. 

That data is used in many ways. Sometimes it is utilized for reporting purposes, such as when hospitals send data to CMS to track clinical performance. The data could be submitted to a professional society associated with gathering data for a new medical device. Data could be submitted to a professional society to help the hospitals improve care. However, our expertise lies in extracting and curating high-quality clinical data.  

The relevance of Q-Centrix Research Network is that we created it to further extend the value of this clinical data by helping our partners do more with their data, including connecting them with life sciences companies to run more research programs. Our assistance could be applied to specific research data sets. In short, what we're trying to do with the Q-Centrix Research Network is to help hospitals' communities, and patients participate in more potentially life-saving research efforts by utilizing the data that they already have and, in turn, helping life sciences companies identify potential sites and patients that would be appropriate for their trial opportunities. 

How does the Q-Centrix Research Network uniquely address the common barriers faced when conducting clinical research? 

Starting with the hospital perspective, a known issue for hospitals is the amount of time and focus needed from their resources, especially in today's clinically constrained resource environment. For example, in the case of research, do you have appropriate team members who are spending time looking at patient rosters and examining the inclusion and exclusion criteria, which are becoming even more detailed? Then, identifying eligible patients, spending time contacting those patients, and trying to interview and enroll them. 

Our hospital partners and life science partners identified a fundamental issue: getting the right patient at the right time. A lack of appropriate patients continues to plague the industry, often leading to many failed or restructured trials.  

There's this enormous need for both hospitals and sponsors to be more data-driven when planning trials and searching for patients. That's where the Q-Centrix research network plays a role. We utilize clinical experts and technology to drive data to assist in the planning and identification of patients. We aim to reduce the trial’s cycle time while still meeting stringent criteria. That is what is unique about our Q-Centrix Research Network. 

How many patients and hospital partners are involved in the platform? How is their data safely collected and shared with the Q-Centrix Research Network? 

We just launched the Q-Centrix Research Network and publicly announced 2 enrollees last month. The first is Carle Foundation Hospital out of Urbana, Illinois. The second is Kootenai Health Hospital in Coeur d'Alene, Idaho. We also have 8 that are actively involved in the enrollment process.  

We found that there's a lot of demand. Many hospitals operate in a budget-constrained environment for resources, but they're still very interested in increasing their research programs. On the flip side, life sciences companies, sponsors, and medical device companies are interested in a range of explicit research data sets. That's the premise of bringing these worlds together.  

Relative to the second part of the question on data safety, we've employed rigorous standards since our inception a little over 13 years ago, and it encompasses a couple of items. First, we must consider the totality of the HIPAA requirements and what is necessary to operate in a world with those requirements and processes. Requirements include meeting stringent information security standards from the hospitals where they screen and approve the resources that will touch the data. Another requirement also includes our internal processes as an organization. We perform an annual audit each year for SOC 2 + HITRUST.  

You can't get through a SOC 2 + HITRUST audit unless all your processes are well-documented and followed. This includes simple steps like the security training that we do annually for our team members, processes like intrusion detection testing vulnerability in your systems, spam audits, and more. You integrate these steps into your daily operations to ensure you're operating at the consistently high-quality safety standard required. 

We also use Amazon Workspaces to create a virtual desktop environment between hospitals and our team members to protect the data further. Part and parcel of that is the de-identification process, another HIPPA requirement, which is the removal of all the patient identifiers. This is necessary for conducting any statistical verification of the de-identification. We use a range of technologies, statistical processes, and experts to ensure that the data is secure while still preserving the ability to have rich research data. That leads to other technologies like tokenization and how you link the identified data together. But all of that, before we even started Q-Centrix Research Network, we do as part of our core business. 

After 30 years, I will tell you it never stops. You must constantly adhere to the information security standards and be proactive because there will always be some bad actors. You can never really spend enough time, energy, and investment in this area. If you deal in the movement of clinical data, it’s important to be secure, safe, and trusted by your partners in that endeavor. 

What steps does Q-Centrix take to ensure the quality and accuracy of the clinical data it provides?  

Ensuring the quality of the data is a little bit different than the safety of the data. This is also at the heart of the issue of how this data is utilized. No hospital can afford to submit poor-quality data, full stop. It doesn’t matter if it is being sent to CMS, a value-based care program, or a sponsor running a trial. The quality of data is always critical.

We have a large recruiting team of clinical data experts who are screened for knowledge, experience, certifications, etc. They are then consistently trained through our Q-Centrix Institute. This is a mechanism to ensure that we consistently have high standards for procuring data. 

Then, we can go into the actual process and technology related to data quality assurance. There's a statistical method that's used in our industry called inter-rater reliability. It is a statistical method that measures the degree of agreement among independent observers who rate, review, and code a sample of data that has already been curated by one of our team members. Having another set of eyes on the data is super important. Unfortunately, many hospitals today don't go through those steps. For us that's the basis of our business—that’s the mechanism we utilize to ensure the accuracy of the data, especially because sponsors want high-quality, research-grade data.   

How else can this new platform benefit hospitals, pharma/device companies, and patients? 

The purpose of a hospital is to provide high-quality care to its patients. The fact that they can now participate in a platform like the Q-Centrix Research Network means they are bringing trial opportunities to their communities and patients, which ultimately helps in the care of the patients. It certainly helps in the reputation of the hospital and its commitment to improving care.  

The pharma and device companies benefit from access to the patients and high-quality data. We repeatedly hear from life sciences companies that they believe in real-world clinical data but often question the validity of the data sets they use. We believe we're the best at curating high-quality clinical data, and we can derive benefits not only for hospitals and their patients but also for their community and life sciences companies.  

Do you anticipate that clinical data-sharing initiatives like Q-Centrix Research Network will continue to gain momentum in health care?  

Yes, but with the caveat that it should be done for the right purpose and with the right care. We must ask, how does a patient, a provider, and a sponsor positively benefit from whatever the sharing activity is? If it's one-sided and asymmetrical, that will hinder advancement. I think that when there is equilibrium and benefits for all the parties, it leads to more sharing if it's done with trust and care. 

High-quality clinical data is one of the most important ingredients to improving health care, but the quality of this data is challenging. Most of the data generated within a hospital is unstructured and used in multiple hospital information systems. The idea that data sits nicely in one information system couldn't be further from the truth. The data is also entered into those systems by multiple clinicians. Normalizing and structuring data is challenging but important because data is so forensic, it gives tremendous insight into the story of a patient's health care journey, and can be helpful to so many others.  

Is there anything else you’d like to share? 

I think the concept of how we leverage data and insights to drive consistent quality health care for all is a tremendously rewarding endeavor. We’re at an exciting and interesting stage of innovation in health care. We’re at the precipice, and I'm excited about what the future holds and, to some extent, what part we play in that ecosystem.