Driving Insights to Action, and Beyond
The theme for this year’s DIA Conference in Chicago was “Driving Insights to Action,” a timely piece of advice for clinical research and operations worldwide. With all the digitized clinical and patient information available, you would think that both insights and action were easily achieved, but unfortunately that is not what Sponsors and CROs are conveying. This was evidenced in many of the sessions at this years DIA.
According to multiple presentations at the event, sponsors and CROs alike are working on improvements in areas such as patient engagement, big data and better analysis to deliver insights.
Patient engagement requires patient-focused input and the ability to coordinate that information with drug research and development. Information coming from international patient engagement efforts further complicates the picture. The combination of data from multiple systems, CRA actions, and now patient devices, provides one reality but direct input from the patient adds a new dimension. In one presentation, Deborah Collyar of PAIR advocated that a patient-focused approach (“one and done” blood tests, a single eHR correctable by patients, and a jointly-created consent agreement) would not only give patients a voice, but would better validate the data coming from the patient side.
With the introduction of wearables, big data has expanded the realm of non-standard clinical data. Patient input can come from many sources—including monitors, Facebook and through CRA interactions—and sponsors need to be prepared to access, integrate and quickly use that data. There is technology that automates aggregating data within silos (such as within a single system or study), but the most optimal solutions handle cross-system and cross-study data integration easily as well. This reduces the time to analyze and understand the trends and impacts of issues such as drug interactions, particularly when using multiple CROs for site and subject management. While big data automation can help bring mobile and social data into systems for analysis, it takes automation to aggregate the data and the expertise of clinical operations and medical review teams to understand and correlate the relevance of that data.
Valuable insights can only come from timely and appropriate analysis. This is key to understanding not only patient and medical data, but also clinical and operational data. Both Transcelerate and Tufts Center for the Study of Drug Development offered key advice at DIA. Transcelerate, with a significant part of its focus on information sharing, process harmonization and sponsor efficiencies, provides KRI guidance to both sponsors and CROs. Tufts proposed a Patient-Centric Initiative Metrics Toolkit, using metrics on cost, speed, quality and impact to help measure “return on engagement” in patient-centric analysis. These presentations clearly spoke to the need of real-time, real-world information in order to achieve the insights required for today’s clinical environment.
All of these areas are individually very complex. However, if broken down into steps, based on the needs of the protocol and objectives of the study, actions become do-able and measurable. For instance, in a previous post, DIA global chief executive Barbara Lopez Kunz called out three key steps on how to start the process of becoming patient-centric: 1) Define the meaning of patient engagement, 2) Consider various approaches to get meaningful outcomes, and 3) Use metrics that show the value of the engagements. These steps, in combination, can lead a sponsor directly from insight to action.
In addition, when evaluating patient engagement, big data and deep analysis, there are some commonalities among these three areas, particularly:
- Aggregating and managing a disparate set of data
- Gleaning appropriate insights from data
- Being able to respond and take action on those insights
If you look at many of the traditional solutions posed at this year’s DIA, they focus on delivering some aspect of these three common areas but they typically silo their data management, so that sponsors and CROs are looking at one system’s or one study’s data – not an aggregated view across all. They also will advocate that you need to create your own analytics. While this may sound attractive, it can add significant time and cost delays if the vendor’s solution requires a lot of customization, integration across dissimilar data and systems, unincorporated process, and resources to build and maintain the solution.
The market has moved on. It’s great to note that innovative vendors have delivered prebuilt analytics and KPIs based on industry standards to jumpstart the work required to enable insights. Add to that the more modern integration and process capabilities that can join all types of data for the most complete analysis. And finally, the ability to view data quickly and graphically (rather than reading and trying to combine tedious spreadsheet data), plus collaborative task management, enables not just action but quick action among all parties—from medical teams to clinical operations, and including outsourced resources.
This year’s DIA opened a few doors to new automation and collaboration capabilities—moving the needle beyond “insight to action” and on to faster, smarter clinical trials.
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