How Just-in-Time Data Can Clear the Path to First Drug Launch

It goes without saying that all biopharma organizations want to use insights from their data to ensure that their commercialization plans are on track. But there is also little doubt that many struggle with this goal. In many cases the problem lies with the manual processing of disparate data. This data, created by countless employees, customers, and partners, is being generated on numerous devices and in different formats. It resides on a variety of systems and is being used on a number of different applications that don’t talk to one another.

As a result, many organizations are looking to data models to understand these assets better. Data models are a framework for data to be used within information systems by providing specific definition and format. If a data model is used consistently across systems, then compatibility of data can be achieved. If the same data structures are used to store and access data, then different applications can share data seamlessly.

While many pharma organizations capture data requirements at a very detailed level, all too often the data model may be too specific to those data sources; thereby, not allowing for change nor enabling customizations. The result is a data model that is incapable of growing with the organization’s needs as it progresses from clinical trials to commercialization. And if the organization switches or adds vendor partners at any time in the process, there is often considerable cost to make all the data sources cooperate.

The need for flexible KPIs

Typically, the sheer growth of both data and personnel through the pre-launch stages requires flexible key performance indicators (KPIs). These KPIs are being tasked with tracking historical data and uncovering insights needed by the teams that are responsible for a successful product launch, such as medical affairs, sales, marketing, and operations.

But organizations often encounter issues in reporting KPIs because of data discrepancies they experience, or unforeseen circumstances they don’t allow for. Pretty much everything about 2020 is a classic example. As organizations headed into the new year, any hypothesis they had about relevant KPIs quickly became invalid. If your KPIs are not flexible, and if you haven’t thought through the potential impacts of changing conditions well in advance, it could pose a risk to both your product launch and to your market share.

This problem typically affects smaller pharma organizations more often than larger ones. Larger firms tend to have made a significant data management investment, generally have large data teams, and they usually have successfully proven processes with earlier drug trials and product launches. On the other hand, smaller and emerging biopharma organizations generally don’t have any of these benefits. Many work with a consulting company that suggests a data model for just one use case, let's say, either for commercial or for clinical. So, there is no single unified data model that will serve the organization into the future. That's where most of the emerging biopharma companies struggle.

Like other industries, pharma organizations have no choice but to master their data if they are to effectively compete in the industry, and ultimately, delight customers and to grow. The key is to find a way to standardize their data and make it easily accessible and user-friendly to those who need it. That, in turn, will speed time to value as the organization takes drugs from clinical trial to commercialization.

Tackling the challenge of data complexity

As PwC points out,1 the commercial environment is getting harsher, as healthcare payers impose new cost constraints on healthcare providers and scrutinize the value medicines offer much more carefully. They want new therapies that are clinically and economically better than the existing alternatives, together with hard, real-world outcomes data to back any claims about a medicine’s superiority.

As a result, it is much more critical for pharmaceutical companies to leverage data to their advantage, but to do so they must first resolve the problem of data complexity. Many factors contribute to this difficulty, including the sheer volume of data that biopharma organizations generate due to the different applications and devices that often don’t talk to each other. This makes it time-consuming, resource-intensive, and very expensive to gain business insights from data on an organization’s products, partners, customers, and more.

For many organizations, the solution to managing and analyzing this data and achieving business insights in real time is a common data model approach. A common data model can ensure that business users have a single, trusted source of data from which all applications and BI tools can work from. This benefit also works to the organization's benefit as they go from clinical trials to commercialization strategy.

In simple terms, a common data model unifies hundreds of data sources into a standard view with no procedural manual ETL (Extract Transform Load) process required. In that way, when business teams are sharing their experiences, they will all be talking the same language. That enables the organization to develop a real sense of how to extract insights from the data, and to do so quickly and cheaply.

This is important for the use of KPIs because they need to be very flexible. The KPIs that work with the development of a generic drug may not work with a brand -name drug, and KPIs change as the product moves from one company to another. As a result, biopharma organizations should invest in a unified data platform that will serve the data needs of all teams. That will prevent data silos, and everyone will have access to a single source of truth information at any point of time. This data model will onboard the data sources from all vendors, including new or changing vendors.

On top of that, if the KPI or the business needs change, they won’t break the existing implementation. This will offer two key values to the business side. One is the speed of data integration or data onboarding, and the second is the speed of getting insights, which is time to value.

Four key KPIs and their reliance on data

There are four key areas where pharma organizations need data insights to drive their KPIs including:

• Clinical Operations

When we talk about clinical operations, people want to know how many sites have been activated; what is the enrollment percentage; how many patients are discontinuing; and how many protocol deviations are happening. Similarly, when they move to the study and patient level, they want to track the project, the cycle time, if there is any enrollment gap in inpatient trials, and if there is any reason that participants might drop off.

The challenge is that these KPIs would change if, for example, the enrollment gap logic percentage for one drug in a study is different than the enrollment gap logic percentage for another drug in another therapeutic area. Different teams would have varying requirements. Some would want to average track enrollment gap percentage at the regional level while others would want to average track them as an area, territory, or a country level.

• Medical Affairs

Goals here are to increase awareness about any drugs in development, about upcoming drugs, or about research that's happening around a potential drug product. Insights needed include what kind of topics are getting good response or bad response from carriers. Most companies also do sentiment analysis to determine what topics are trending or not trending, and to determine in what regions to help salespeople structure their conversations with customers.

Finally, companies also conduct 3rd party surveys to engage with key opinion leaders (KOLs). Upon receiving KOL responses, it's very important to link that feedback to CRM data to have a 360 view of KOLs. Based on survey insights, the medical science liaison (MSL) interactions should be customized and personalized for each KOL and provide a unified view to MSLs for all KOL interactions irrespective of the source of information, be it CRM or survey.

• Market Access:

Understanding this is key to making sure that patients have a drug available when they need it, starting from when a doctor writes a prescription, to the order being processed, to the pharmacy or the distributor providing it. So, prescription to refill is one of the key features that organizations care about, and several KPIs might be involved in the process.

Another is the patient journey, where organizations want to see how many patients have been diagnosed, how many patients have been treated, and how many patients have switched treatments. So, they track all those things, and they make sure that most of the patients are sticking to the design that was initially prescribed.

• Commercial Operations:

The end goal is obviously to make the product commercially successful. Toward that end, the first step is to ensure that the right patients are targeted. Pharma companies need to track what percentage of doctors are engaged with, talked to, and at what frequency they are reached. They also need to report if they are not reaching them, or if they are inactive, and then develop a new strategy to reach them. And that’s just on the call side. Then there are other doctors who the company has met, had good discussions with, but who are still not writing prescriptions for the drug.

It's also important to track which doctors are early adopters for a drug, which are not, who is moving ahead with prescribing it and those that are moving away from the new drug. Typically, these activities are tracked using the sales dashboard as it provides a good insight for both sales and revenue forecasts.

The other thing that is very important for commercial success is to track the competition in terms of which drugs are doing well in certain regions. As such, pharma companies need to track the winners and the losers in various categories, and track hospital and doctor activity in terms of what is being prescribed.

That will provide insights on what trends are driving the industry. As a result, the commercial team can work with the marketing team to make sure that they are targeting the right patients with the right drugs in the right markets.

Lets’ face it, lengthy time cycles and a lack of resources should never prevent biopharma organizations from leveraging insights from data to ensure their commercialization plans are on track. Implementing an efficient means of unifying disparate data and allowing teams to utilize this single source of truth data will allow them to track KPIs more effectively and get key insights when they need them. More importantly, they can be key to removing the commercialization bottleneck that prevents critical medications from entering the market.

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