Monday, 23 September 2019

Commercial data imperatives for pharma in China

25 October 2013 | Opinion | By BioSpectrum Bureau

(L-R) Mr Rajesh Kuppuswamy is a senior director, Ms Ruchi Malhotra is a senior manager and Mr Arvind Dakhera is a manager for life sciences consulting at NASDAQ-listed IT and consulting firm Cognizant

(L-R) Mr Rajesh Kuppuswamy is a senior director, Ms Ruchi Malhotra is a senior manager and Mr Arvind Dakhera is a manager for life sciences consulting at NASDAQ-listed IT and consulting firm Cognizant

As Chinese pharma moves forward to become the second largest market in the world by 2015, commercial operations for the geography take center stage in an attempt to capitalize on this opportunity. In this scenario, an analytical, data-driven approach is the cornerstone for the success of any commercial organization.

Changing face of commercial operations in China
Traditionally, Chinese healthcare has been associated with limited access, high out-of-pocket spending, margin-based selling, skewed distribution of healthcare infrastructure and a fragmented distribution network. This, in turn has led the commercial function to focus on the promotion of branded generics in an urban setting.

However, in recent times, the growing prevalence of lifestyle diseases and far-reaching impact of healthcare reforms including, improving healthcare access/coverage, consolidation of distribution networks and reducing drug prices, have come to characterize the emerging state of the Chinese healthcare system.

Accordingly, the pharma sector has been reinvigorating its China strategy in terms of rationalizing product portfolio, focusing on market access, enhancing provider coverage (to include county/rural hospital coverage through internal and external field force capacity addition/enablement), leveraging partnerships (for manufacturing, BD licensing, and inventory management) and pricing competitively to garner larger market share.

The need for a comprehensive commercial data strategy
Chinese pharma firms needs to rely on significant data inputs from epidemiology patterns and insights from trade and local healthcare system to make important commercial decisions on reach, products and promotions. They also need the data to re-align their strategies with the changing healthcare ecosystem in China.

 

Companies obtain data from several disparate sources, which often use various formats or technologies, thus leading to gaps in data governance, standardization, hierarchy, and data quality/integrity. This often results in a trade-off between data overload and lack of availability, resulting in sub-optimal decision-making by various commercial functions.

To overcome data gaps, some use third-party data service agencies, while others deploy their own internal teams to collect data manually. Though this is a cumbersome and imperfect process, pharmaceutical companies often pay approximately 1% of the drug value for acquiring information. The integration of available data has therefore emerged as a complex and challenging task. These diverse commercial data inputs are essentially clustered either under master (epidemiology, therapy, product, hospital, physicians, territory, distribution, pricing, contract and NRDL/EDL) or activity data (hospital, physician and distribution activity, reimbursement and claims data).

The commercial operations franchise thus faces the challenge of devising a data strategy that optimizes coverage, productivity and demand by leveraging integrated solutions in order to develop a holistic view of masters and their relationship with activities. The figure below illustrates a data strategy framework to address these challenges, and depicts a logical scheme of steps or activities that may be followed to devise a comprehensive data strategy.

cognizant-fig-1-data-strategy-approach

There are several key considerations for each step in the above-illustrated framework. A detailed chart of the considerations can be viewed here: (Cognizant - Data strategy components and considerations).

Solutions to strengthen data governance and management
In our experience, based on assessment of data sources, strategic interventions may include creation of data governance charter or a mechanism to instill continuous improvement of data quality. They could be more tactical such as establishing customer definitions, data acquisition map and data distribution, while designing Master Data Management (MDM) and Data Warehouse (DW) solutions. Depending on the business environment and the internal organizational dynamics, a combination of solution concepts may be adopted towards addressing data governance and management challenges. The intent is to bring all entities that represent the market, modeled into a single unified data source, accessible through dashboards, standard reports or an analytics tool.

 

The various solution themes that could be used as potential interventions can be viewed here: (Cognizant - Solution Concepts).

Pharmaceutical companies need to manage the balance between a need for more granular information and insight that is concise and relevant. As the sector develops more complex business and operating models, these companies need deeper information about the patient, the payer, and the provider. The timing and quality of decisions, backed by robust data analysis, would place successful companies ahead of the others. Hence, to explore creative ways of accessing, sorting, structuring and interpreting data, the right combination of process and technology interventions is of paramount importance.

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