The Key to Hiring Data Scientists for Pharma

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Data analytics is one of the hottest fields out there right now. Businesses across all industries need to take advantage of the tsunami of customer data available to them, and there simply aren't enough qualified, experienced data analysts to fill that need.

For pharma, data means more than just creating warm and fuzzy experiences for consumers. Much more. It's about human lives. Integrating data across all healthcare platforms over a significant period of time — patient records, pharmacy records, clinics, lab results and more — can lead us to a new level of patient health. It enables pharmaceutical managers to develop brand strategies based on a holistic view of the customer (patient, payer, prescriber) experience without having to make leaps of faith across disparate data sources. From a patient outcome perspective, integrated data can improve the efficiency of clinical trials, and pharmacy data can even become a real-time predictor of a patient's health.

The tech industry has been hiring data analysts for a while now, people who have skills in math, statistics, machine learning, coding, domain expertise and software engineering. Of those, the big three are math, software or coding, and domain expertise.

If that person sounds like a unicorn, it's because they are. You find someone who has all of that rolled into one CV, and you've found the Holy Grail. And it's about as likely. Even if you do find them, good luck keeping them. People with experience are holding the golden ticket in the job market.

So, we're looking at needing a unicorn that everyone else needs, too. Not an ideal situation. The solution? Building a team. You don't need someone who is an expert in all of those areas. You need a team of people who are experts in each of the areas.

Here's what each of those experts can bring to your analytics team, and where they might be coming from:
  • Software expertise: You'll find these people in tech companies. They'll bring algorithm design, database creation, analytics pipelines, coding, programming language expertise and other software-related skills to the table.
  • Math or domain data science: They'll typically have worked in large companies in marketing, sales or other areas where customer data is key. Or they may be coming from academia. You may find PhDs in math or statistics. They'll add statistical analysis and knowledge of machine learning to your team.
That applies to all industries. But for pharma, it's a little trickier. The majority of experienced data scientists now come from the technology sector. But the needs of pharma in data analysis are different than the needs of Silicon Valley or even other businesses.

In pharma, data scientists come from behavioral science, economic, market research, even marketing backgrounds. It's not like they haven't done research, but the nature of the research they've done is different. When pharma is hiring data scientists, a tech background isn't enough. What's really important for pharma when hiring data scientists:
  • Knowledge of the industry. They must know the pharma space, the customers and stakeholders.
  • Knowing the nature of our data. Pharma data is unique. Unlike, for example, Facebook data, pharma data is sensitive. It's health information. It takes time for people to understand the nature of the data, its sensitivity and how to use it.
Without those two critical factors, they won't be successful. However, there's room to challenge the belief that our data scientists must be from pharma. Why? The diversity of other industries brings a diversity of thought. It results in:
  • New technology
  • New ways of thinking
  • New creativity
We can train people to understand our industry. And remember, the intense demand for data analytics is new, but the prerequisites for being a data scientist aren't new. Math, computer science, software engineering, coding. Finding people who specialize in one of those things is the key to building a solid analytics team.




Using Data Analytics to Create an Optimal Field Force

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For pharmaceutical companies, the field force, your people out there on the front lines visiting physicians and patients — is a vital, if not the most critical, part of your commercial efforts. But their jobs aren’t always easy, considering an ever-tightening regulatory landscape and access to HCPs being more restricted now than in the past. It’s not about going to the office with lunch for the staff anymore. It’s about delivering the right health care message to the right customers, at the right time, through the right channel.

How do you know if your field force is operating in the most efficient way? The good news is that there are few parts of any business that have more data swirling around than sales. Sales analytics can uncover trends, create and highlight metrics and even predict future sales performance. In other words, it tells you what’s working, what’s not and where you might improve in the future. Very often, the key to success is a cultural alignment between the sales team and the analytics team to enable active monitoring, regular measurement and optimization.

What data should I monitor?
Seeing how important data is to increasing the efficiency and impact of your sales force is the first step. There are several analytics methodologies out there to help you analyze your sales data. What sort of data should you monitor? Here are a few metrics to take into account when monitoring the health of your sales force:

Growth. How is that line moving, up or down? Tracking the growth over time and triangulating that with your sales and marketing activity can help you see which of your efforts/investments are working, and which are falling flat.

Sales per rep. This isn’t necessarily about keeping tabs on individual sales performance. You already know your seasoned pros are going to be bringing home the bacon to a greater degree than newbies. But it will give you a benchmark for those newbies to work toward.

Sales by region. Tracking how your field force is doing in certain parts of the country, or cities, or even neighborhoods is a powerful way to predict how they’ll do in the future.

Promotion responsiveness. Some sources call this a sales conversion ratio. This metric measures the amount of contacts/visits made in relation to the amount of deals closed.

Multi-channel engagements. Instead of solely focusing on F2F visits, nowadays sales professionals have a wide array of tools to increase the number of engagements as well as the quality of engagements. Tracking these activities in a standardized manner, and ideally in a single system, would be critical to understanding their promotional effectiveness and enable operational improvements.

In this current market, it’s vital for pharmaceutical companies to get the most out of sales force visits. Using data analytics is one powerful way to increase the ROI you’re getting from interactions with HCPs, creating an optimal field force that has the ability to orchestrate a customer-centric engagement journey.

To learn more, please read the PMSA journal article “Sales Analytics and Big Data Developments Needed Now to Address Practitioner-Identified Emerging Biopharmaceutical Sales Force Strategic and Operational Issues”, PMSA Journal, Spring 2017.




Integrating Multiple Data Sources to Better Understand Patient Journey

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Finding patients who are good candidates for any given therapy has always been a primary goal for pharmaceutical marketing teams. The trick is reaching the right patient at the right time. Just as important, convincing the insurance company to pay for the therapy. It has never been more critical, given the strategic shift toward highly specified, personalized therapies like immunotherapy, gene therapy and rare disease treatments. These high-cost therapies can save lives, but insurance companies usually require extensive proof that the patient meets the clinical requirements.

So on one side of the coin you have pharma marketing teams and patients wanting to get into these therapies as quickly as possible, and on the other, you have the payers saying, "Hey, slow down, let's make sure this person really fills the bill." What you need is a comprehensive understanding of patient journey supported by data, and lots of it, to connect these two realms.

Unfortunately, identifying the right patients through traditional commercial data sources is difficult at best. People are misdiagnosed and undiagnosed. Access to electronic medical records is limited and data is unstructured. Lab test results don't show up in traditional reimbursement claims and data vendors have fragmented coverage. The full patient journey is incomplete given all these data limitations. The key to create a comprehensive patient journey is to truly integrate data from multiple sources over a significant period of time. Such sources include but are not limited to:

  • Retail and mail order pharmacies
  • Wholesalers
  • Specialty pharmacies
  • Hospitals
  • Community-based offices
  • Clinics and other healthcare delivery facilities
  • Clinical registries
  • Electronic medical records
  • Lab data

Integrated data requires unique identifiers for each HCP and patient to allow linking of the information across all sources. By doing this, any addition of new data sources, such as biomarkers, would go through a standard mastering process so integration with existing data assets becomes feasible. This approach enables pharmaceutical managers to develop brand strategies based on a holistic view of the customer (patient, payer, prescriber) experience without having to make leaps of faith across disparate data sources. From a patient outcome perspective, integrated data can improve the efficiency of clinical trials and pharmacy data can even become a real-time predictor of a patient's health.

The opioid epidemic is something we all have read or heard about in the news these days. Data analytics can help identify providers, physicians, patients and yes, even pharmacies, that are not acting responsibly in the dispensation of this medication. It can also help identify at-risk patients who are likely to misuse their medication, to ferret out who is most likely to stop taking their meds, and also uncover the reason why, such as forgetfulness. It can enable HCPs and patients to set up alerts, provide automatic refills or long-term (90-day) fills to avoid the problem of the patient running out of their medications. If high drug cost is the reason, you can also identify sources to help defray that cost.

If you're interested in reading more about this topic, please read “A New Path to Understanding the Physician’s Decision Journey Using Simulated Patients” (PMSA Journal Spring 2017) & “National Lab Test Database Provides Valuable Marketing Insights From All Stages of the Patient Journey” (PMSA Journal Spring 2015) from our resource library to help you gain a deeper understanding of what’s possible and how others are addressing this critical issue.




Visualizing Healthcare Data for Business Decision-Making

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Data analytics make the world go 'round across all sectors of business, from marketing to retail to manufacturing and beyond, and even to healthcare. For pharma teams, analyzing and utilizing data may be more important than it is for any other industry, because while it's beneficial for companies to understand their customers' needs, behaviors and expectations in order to deepen customer relationships and offer the right products or services at the right time, for healthcare, even more importantly, we're talking about people's lives. Better patient outcomes mean a better quality of life, and analyzing patient data can help us make that happen.

Data visualization, a powerful way to use and analyze data, is the process of taking data and putting it into a visual context to make it understandable, clear and user friendly. It allows pharma teams to better use the data in patient care in a just-in-time clinic environment. You know what they say about a picture being worth a thousand words.

Why is visualizing the data important? According to the Journal of the American Health Information Management Association (AHIMA), data visualization allows us to "see patterns and relationships in large volumes of data that may not be easily seen in the raw data reports." This can illuminate emerging trends that might not be apparent. It's a powerful way to use data to identify issues early, before they become widespread problems.

Data visualization is being used in many aspects of healthcare, including in patient care, healthcare delivery, public health and more.

The idea is to gain insights, glean as much from the data as possible and use that information to improve people's health, and in doing so, their lives.

According to PMSA, four categorizations for HCP/patient analytics currently exist:
  • Prescriptive, which identifies actions
  • Predictive, which examines likely scenarios
  • Diagnostic, which is a historical view of performance
  • Descriptive, what is happening now
Typically, the pharmaceutical industry uses diagnostic and descriptive analytics to monitor brand activity and marketing trends, and uses prescriptive and predictive analytics to understand market activity and forecast brand performance. With data visualization, we can present and manipulate data. It's especially useful for the business to have the ability to drag and drop insights, filter by specific metrics or attributes and create graphics in order to better help physicians and patients.

To learn more, read a case study highlighted in the PMSA Journal, Building an Oncology Data Visualization Platform to Leverage Integrated Patient Data and Analytics.