WHAT WILL YOU LEARN
Acquiring and analyzing appropriate data is the critical next step in developing a strategic plan for commercialization. Despite any previous experience, there are many scenarios to consider while selecting or purchasing data, and what might have worked in the past, could be wrong for the current launch. This whitepaper will explore the correct ways & considerations for data risk assessment to avoid the time and financial risk that can be involved in selecting and purchasing the wrong data, facilitating you to pace up with the rapidly changing pharmaceutical industry by making an informed decision.
Introduction
Acquiring and analyzing appropriate data is the critical next step in developing a strategic plan for commercialization once Phase III clinical trials are underway and the drug or biologic appears promising for FDA approval and market introduction. But what should that dataset be?
Despite any previous experience, there are many scenarios to consider in selecting and purchasing data, and what might have worked in the past, could be totally wrong for the current launch.
The rapidly changing environment in the pharmaceutical industry also brings significant impact to popular and mature datasets. Many data providers have developed innovative ways of collecting data, changing what is now available. To avoid the considerable time and financial risk that can be involved in selecting and purchasing the wrong dataset, a data assessment exercise should be done first to identify the dataset that will properly support the appropriate strategy for commercialization.
The Right Dataset
The typical decision process when selecting a dataset involves selecting the dataset type, and the vendor with the best solution, price, and terms & conditions. There are a host of dataset types: retail, non-retail, claims, formulary, medical testing, healthcare organization, specialty pharmacies, EHR/EMR records, public data, and the list goes on. There can also be several vendors offering the same dataset, and one must understand the obvious and hidden costs related to each vendor’s dataset purchase.
Essentially data assessment works to gather data-related information, perform some pre-analysis data sampling, and then develop an analysis demonstrating the pros and cons for each dataset option, including an examination of hidden costs, and also what potential solutions might exist for any shortcomings.
The goal is to create a true understanding of the data and its value and to prioritize spending and/or possibly optimize the investment to benefit broader business needs.
Ultimately data assessment allows for informed decisions so that financial commitment is based on solid analysis rather than incur the business risk that arises when making decisions based only on presumptions.
Key Considerations In Data Assessment
When purchasing data, it is important to take into consideration not just what is in the dataset itself, but a host of other factors- all of which should be examined to drive an informed decision. Understanding the elements listed below help you avoid commonly made mistakes, and enable full leverage of the data’s power, eventually leading to business success!
Business Needs
The first step in any dataset assessment is to understand the product’s characteristics – is it an oral medication or injectable – which leads to many other considerations such as where the patient will be able to purchase the drug – retail or specialty pharmacy. When the product is an injectable and cannot be used by patients on their own, data won’t be captured from pharmacies, but rather only on an outlet level.
Understanding who the stakeholders are for your product – physician, patient, payer, or hospital – and which one is the key driver will have significant impact on your marketing strategy. This can play a major role in determining what kind of datasets are needed to implement a marketing plan. One must work with the users of the dataset: Will they be internal or external to the organization or both? It is important to talk with all the potential users to clarify the problem being solved and to
Data Source
There are a number of aspects that should be clarified when selecting a vendor. These include:
- How the data is collected?
- What is the coverage?
- What is their projection methodology?
- How frequently the data is refreshed?
- What is the time lag among others?
Data Sample
Once you have qualified the data vendor through these questions, it’s always recommended to request a data sample to check the quality and to determine if it is consistent with the description. For instance, KMK had a case where one data vendor claimed that they had specialty physicians in their dataset, but in fact, about 80% of the HCPs in the sample dataset had listed their specialty as unknown.
Price
Certainly, the final cost of the data is an important consideration. Different data vendors offer their own price structures and might also offer different options to different clients. Then there are third-party fees, data update fees and some other hidden fees that might occur which should be taken into consideration. Fully understanding all these considerations will help to prioritize your spending and remain within budget limitations.
Data terms & conditions
Examining terms and conditions can be tedious, but not doing so could cause some serious issues. Some datasets have a data use condition and security requirement for data privacy. Some datasets cannot be used jointly with other datasets for a particular reason. The data vendor might not point these facts out, and if not understood, could put the product and company at risk.
Customer Support
It is critical that the vendor offers solid, on-going customer support, especially when faced with a significant event. Since data becomes the foundation of planning, if anything goes wrong within that dataset, all results will be off undermining the validity of your overall business strategy.
Authors
Jing Yu
