Specialty Pharmacy Data: The Golden Nugget for Manufacturers

Specialty Pharmacy Times, July/August 2015, Volume 6, Issue 4

The development and management of a successful data strategy require a team effort.

The development and management of a successful data strategy require a team effort.

Use of specialty medications to treat complex conditions such as rheumatoid arthritis, multiple sclerosis, and cancer is on an upward trajectory. One only needs to look at the current number of specialty products in various pharmaceutical manufacturers’ pipelines as an indicator that current trends are not likely to diminish in the near future.

According to the EMD Serono Digest, a small percent of patients (3.6%) account for 25% of health care costs, and it is estimated that specialty spend will quadruple by 2020 (Figure 1).1 Manufacturers of specialty products are focused on optimizing channel strategies to deliver maximum value for their respective organizations while delivering a positive patient and provider experience. That said, the breadth and depth of specialty pharmacy (SP) data could be a bonanza for manufacturers. The robust transaction-level data that manufacturers are able to procure through their SP partners can provide market intelligence that would otherwise not be unavailable. This valuable data can be mined to support achievement of critical business objectives.

{Click image to enlarge}

Determining and implementing a data strategy that will ultimately deliver desired business outcomes requires a village. The scope and effort for this task must be underestimated, as both manufacturers and SPs are faced with many moving parts that will influence the quality and completeness of the data.

There are a host of environmental factors that will have a direct influence on any data approach. Data strategies are heavily impacted by privacy regulations such as the Health Insurance Portability and Accountability Act and the Health Information Technology for Economic and Clinical Health Act, as well as state regulations on the sharing of protected health information. Patient consent is a key driver of the level of information that manufacturers can expect to receive given the increasingly more onerous scrutiny by regulators. In today’s environment, a successful data strategy requires close collaboration with both internal and external functional stakeholders. In addition, one can expect variations across the universe of SPs as a result of each pharmacy’s interpretations of the regulations governing data sharing.

The first step toward a successful data strategy is to develop a data governance approach to ensure alignment across internal functions including, but not limited to, brand teams, sales, trade/channel, legal, compliance, patient services, and information management. Once a team is formed, the next step is to conduct a cross-functional needs assessment to determine the scope of the data needs from the ultimate users, including the necessary data elements and the data reporting protocols (Figure 2).

{Click image to enlarge}

When determining the level and frequency of the data that a manufacturer desires, there are a few key questions that will inform the final data strategy:

  • What are the critical data elements needed to effectively manage the business?
  • What is the frequency of the data reporting that supports actionable strategies/tactics?
  • Does my organization have the expertise and bandwidth to aggregate, cleanse, validate, and interpret the data?

Data Scope

Manufacturers must determine what level of data is necessary to effectively manage their business to ensure optimal outcomes, both clinically and financially. The data specification will be a function of the product characteristics and the complexity of the disease state. For example, an oral tablet with minimal side effects may require less stringent reporting than a product or disease state with a more complex profile, (eg, a product that requires multiple clinical interventions or is under an FDA-mandated risk evaluation and mitigation strategy). SP data can be classified into 3 general categories:

  • Base data: data that are readily available and require minimal effort for the SP to provide
  • Enhanced data: data that are available and may require additional effort for the SP to provide
  • Customized data: data that are not readily available and would require significant effort for the SP to provide

An example of base data would be dispensed data, including elements such as product name, National Drug Code, date dispensed, quantity, prescriber information, and refill information. An example of enhanced data may be referral data, which indicates the status of the referral and includes status codes with categories such as pending, active, denied, or discontinued. To go a level deeper, sub-status codes may be captured. Sub-status code examples include benefit investigation, prior approval, appeal, and no insurance coverage. Customized data may include information captured regarding a targeted clinical intervention outcome. With respect to the data approach, manufactures should be careful not to seek to overachieve and should be realistic when choosing the scope of the data approach to meet their business needs

Data Frequency

The next decision point is the frequency of the data reporting from the SP. This will likely be a function of not only the product profile and disease state but also any additional services the pharmacy may be performing on behalf of the manufacturer. For example, if hub services are involved, there may be a need for a daily report of referral status to inform the hub of any reimbursement interventions required. If there is an adherence program for the product, including a targeted intervention, what information from that outreach may be of value to capture? It is important to note that the dynamics at the pharmacy level regarding status, shipments, and other data points are not static. The fluid nature of a daily pharmacy data feed in and of itself can contribute to an overreaction by manufacturers. For example, a referral status can change several times within a short timeframe. A weekly or monthly data frequency may provide more reliable and actionable intelligence thereby, allowing the real word dynamics to flush themselves out.

Data Implementation and Reporting

Once the data strategy is identified, the real work begins. Given the many moving parts involved in the data implementation, a defined implementation plan is critical to meet timelines and business objectives.

Typical data implementation timelines range from 30 to 120 days from start to completion, depending on the complexity of the data strategy. The expertise needed to receive, scrub, quality check, and translate the data into a usable reporting vehicle may or may not be found internally. Manufacturers must look inward to determine if they have the expertise and capacity to manage the data effectively or if external support such as a data aggregator and/or an external data reporting tool is necessary.

Once the data is received, it must be properly interpreted so that the outcome accurately represents the market dynamics and actual product performance. SPs can provide a rich source of transaction-level data that can give greate insight into brand effectiveness and support business strategies if properly understood.

SP data are the golden nugget for manufacturers and can provide invaluable and actionable insight for specialty products. The effort to identify, build, and manage a comprehensive data strategy is substantial, but the rewards of a well thought out and executed data strategy cannot be underestimated. SPT

The views expressed in this article are the opinions of the author and not of Mallinckrodt Pharmaceuticals.

Reference

  • EMD Serono Specialty Digest. 10th ed. Rockland, MA: EMD Serono; 2014.

About the Author

Maryann Dowd, RPh, has over 25 years of experience in the health care sector, spanning retail/specialty pharmacy, biotechnology, and management consulting. Maryann specializes in channel optimization providing strategic and tactical insight into the specialty distribution/pharmacy service model. She has held various leadership roles at Biogen Idec, PriceWaterhouseCoopers, and EMD Serono. She has provided thought leadership in the management of multisponsored risk evaluation and mitigation strategies programs, and led an innovative limited distribution track-and-trace program designed to mitigate diversion and counterfeiting of high target biologics. Maryann practiced pharmacy at CVS Health in Massachusetts after graduating from Massachusetts College of Pharmacy. She is a member of the editorial board of Specialty Pharmacy Times.