Q&A
By Team Panalgo

Building a Real-World Evidence Center of Excellence: Lessons from the Frontlines

A Q&A with Margaret McDonald, Head of Apex Data Consulting, LLC, following a 17+ year career at Pfizer.

Margaret McDonald is an accomplished analytic research scientist, healthcare executive and former hospital laboratory administrator with a Ph.D. in epidemiology, specializing in real-world data and analytics (RWD). She has expertise in global data strategy and establishing and leading an ecosystem of external strategic partnerships/ alliances. Margaret has experience conducting research and interpreting findings, leading clinical research teams and initiatives, strategically expanding and leveraging RWD assets for global research market adoption.

Q: What is driving the need for life sciences organizations to invest more in analytics?

A: Several issues are highlighting the growing importance of analyzing real-world data (RWD) to generate real-world evidence (RWE). Increased focus from regulatory agencies, intense public scrutiny of drug prices and healthcare costs and the shift to value-based care are among the concerns making an analytics capability critical for life sciences companies. And that’s prior to the arrival of COVID-19 which has increased the urgency of implementing analytics initiatives to gain important insights from RWD/RWE. Increasingly, companies are embarking on unique research collaborations and data-sharing initiatives to develop treatments for not only COVID-19, but other diseases.

Q: Why are organizations establishing Centers of Excellence (CoEs) to meet this need for increased analytics bandwidth?

A: Life sciences companies are beginning to recognize that it’s more efficient and economical to have CoE’s that can grow and support many different areas within the organization. Analytics is not a function-specific endeavor, and as additional sources of RWD become available, centralization streamlines activities and strengthens the lines of access to data for those groups that need it. I call it the democratization of data and if you can use the data more broadly across the organization, you can realize a higher payoff for the investment in analytics.

Q: How would you define a CoE?

A: I was at a conference several years ago where a speaker noted that everyone seemed to have a different definition of a CoE. He said he found the best one on Wikipedia which defined a CoE as “a team, a shared facility or an entity that provides leadership, best practices, research, support and/or training for a focus area.” I believe that’s a good place to start.

Q: How does a CoE accelerate speed to insights when it comes to RWD/RWE?

A: By having the appropriate personnel and technology to understand the short- and long-term needs, pain points and goals of the other groups in the organization. Speed to insights is really the goal for life sciences companies because the ones that finish first win and the ones that finish last lose. The CoE is key to making sure you come in first.

Q: How do you go about setting up an analytics CoE?

A: First you need to find the right personnel to staff it. Some of the positions you will likely need are programmers, and individuals with HEOR, commercial or statistics background. This will all depend on the specific needs of your organization. During the growth phase, it’s likely that people will assume multiple roles on the team. Look at other departments in your company and you may find people that can fill some of the roles you will need in your CoE.

Q: What are the challenges to establishing a CoE?

A: Probably the greatest challenge is gaining support from the top. The mandate for a CoE must come from the C-Suite and the executive leadership team who set it as a critical goal. Many groups in a company are doing different things with RWD/RWE but establishing the CoE must be an organized effort directed from the highest levels of the organization. With this C-Suite support, a clear remit for the CoE is established, eliminating confusion over who does what among different teams in the organization and streamlining processes. This is important for efficient CoE operations.

Q: How important is it to identify the internal customers who will be relying on a CoE?

A: It's one of the most important things you can do. Even though you may not have dozens of departments within the organization, it is still vital to prioritize the groups that most need analytics support. It’s not only important to identify your internal customers, but it’s also crucial to determine what they need. Some may need basic analytics support while others may want more sophisticated output, for example, to support product strategies. Still, others may be strictly report consumers. It all depends on their roles and responsibilities within the organization. It is also important for the CoE to have members who can communicate the value of the CoE, listen to the analytics needs expressed by the teams and “sell internally” to help groups better understand their analytics needs and how the CoE can help.

Q: How critical is identifying data needs?

A: Without the right data, you won’t be able to generate the RWE insights you need to answer the questions being asked of the team. That starts with evaluating the data you have access to both internally and externally. The data you ultimately identify must be useful to answer the questions your users have today, but also potential questions they are likely to have in the future. Buying whole databases can be expensive so you can often get the data you need by acquiring data extracts or contracting to have use of a specific data for a defined period. Working with a vendor that can help you decide the most appropriate data set for your needs and identify the sources of that data can also be beneficial as you grow your RWE CoE.

Q: What options do you have if you don’t have the technology or human resource firepower to fully support a CoE?

A: As you grow your CoE, you should partner with a company that is expert in RWD/RWE, is proactive and has a robust platform that is adaptable to your current and future needs. This is why at Pfizer we chose Panalgo and IHD. They had more available data sources already mapped and were always proactively looking for different kinds of data. They have terrific communications with customers and are constantly improving their platform to meet the growing analytics needs of its customers.

Partnering with Panalgo really helped us grow our team because of the ease of use of the platform. It’s user-friendly making it easy to get people to use it to gain very sophisticated analyses quickly without a lot of programming. It saved us enormous amounts of time. It not only helped grow our data sources but increased our capabilities to do rapid analyses in-house.

Q: How do you ultimately prove the value of an analytics CoE?

A: The results of the use cases ultimately does that. Routinely helping teams, answering descriptive questions to provide necessary insights more quickly, producing RWE to support their product strategy or carrying out a study more rapidly. The more groups that use it, the more overall value it provides.

From an economic standpoint, being able to conduct analyses in-house rather than using outsourcing can provide a significant ROI. For example, at Pfizer we did a majority of our analyses in house, but there were some analyses and studies we outsourced. Once we began partnering with Panalgo and IHD, we were able to bring more of those projects in house. Conservatively we saved 40% to 50% of the cost of outsourcing. Combining the cost savings with the additional speed to insights we were getting resulted in an impressive ROI for the company.


 


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