October 30, 2017
CWWeekly presents this biweekly feature as a spotlight on issues that executives in clinical research face. This week, writer Barbara M. Bolten spoke with Gary Palgon, vice president, Healthcare and Life Sciences of Liaison Technologies.
Q: What are the data challenges life sciences organizations face in the clinical R&D phase?
A: Life sciences and healthcare organizations are going through a digital transformation. A major challenge they face is dealing with the amount and the different types of clinical data now available. Companies are taking advantage of semi-structured data such as clinical notes, as well as totally unstructured data such as images. As companies make progress in being able to access unstructured and semi-structured data, they will be able to apply them to improve the drug development process.
Another challenge is integrating data from multiple sources. Pharmaceutical companies no longer perform all their clinical trials in-house. They may outsource trials to multiple CROs, and those CROs may use different ways to access data. For example, they use various electronic data capture (EDC) systems for getting information from patients and clinical trial sites. While all of these systems are gathering the same types of data, they interpret the data differently, so the challenge to make the data contextually useful. Once the data is integrated, transformed, translated and harmonized, the challenge is how to use the data to perform analytics. The goal is to use the data to make better, faster decisions and to improve patient outcomes.
Historically, pharmaceutical organizations used on-site software such as middleware, which acts as a bridge between applications. They had full control over the system, and were dealing mostly with internal data. Now that sponsors outsource to CROs and other vendors, they have less control.
Also, that on-site software is old and needs to be updated. So companies have three models to choose from. The first is to upgrade or get new software to run in-house. In that case, they will need to employ people that know how to run it, have a data center to support it and have responsibility for data integration. The second model is to use a cloud-based solution, in which the middleware is running in the cloud. The company does not have to worry about software upgrades, but they will need to perform the data integration. The third model is to use a managed service model, which uses a cloud-based platform to run the middleware, but also provides data integration as a service that includes the support of skilled experts.
Q: How can pharma collaborate and integrate information from a variety of data sets and management tools?
A: Over the past few years, a majority of hospitals and a high percentage of medical practices have switched to electronic health records (EHR) or electronic medical records (EMR) systems. It would be nice to be able to pull data from the EHR or EMR of a patient engaged in a clinical trial and transform it into data that is relevant. We are making progress in establishing those connections between EMRs and EDCs and building longitudinal records. When the sponsor moves into the clinical trial process, they would like to be able to access whatever data is produced along the way. The data is coming in from a variety of sources, such as from patients with wearables, as well as from multiple applications in healthcare organizations.
The problem is that, historically, the data is trapped inside these applications.
The world overall is now moving toward a data-centric view, in which applications are built on-demand and access to data is easier. These systems, which are offered by Liaison and other providers, allow untethered access to a data layer. Now pharmaceutical companies are moving in the same direction. They are switching from an application-centric model, in which data was trapped in the application, to a data-centric model, in which applications can easily access the data the company needs.
Q: What can life sciences organizations do to ensure the highest level of security for data?
A: Companies are moving in the direction of being able to access a patient’s longitudinal record for clinical trials, and more applications, including EMRs, may be running in the cloud. Every link will require the same level of precaution to protect that data. Companies have to comply with HIPAA and FDA regulations in the U.S., and the EU Data Protection Directive in Europe, for example. Global pharmaceutical companies that need to be able to move certain types of sensitive data between Europe and the U.S. must also comply with the Privacy Shield (formerly the Safe Harbor) framework.
If the sponsor is running their own software and doing their own integration, they take full responsibility for security. If they are using the model in which the software is running in the cloud but they are performing their integration internally, security will still be a burden for the company. In the managed services model, the service provider is responsible for ensuring compliance, security, and privacy. We require employees to complete security training, certify the technology platform annually and test it throughout the year. The managed services model is the simplest way for companies to address all of these security challenges.