Why Data Quality is an Achilles Heel in Pharma?

Mar 29, 2022 | Pharma Data Quality and Governance Solution
Sayantani Sen

Why Data Quality is an Achilles Heel in Pharma

Why Data Quality is an Achilles Heel in Pharma 1024 731 deepak

Background to Data Quality Management in Pharma

Pharma companies are constantly in a race to beat competitors. The need for deeper and faster insights from the ever-expanding data sources is an imperative that they are constantly focused on. Analytics and insights are only as good as the data. Here lies the problem with good quality data for pharma. These companies have spent millions of dollars on various initiatives to improve the master data management (MDM) and data quality processes. Despite these efforts, the problem is far from solved. If anything, the problem seems to be taking on larger proportions with newer, more complex, and larger datasets coming up on one hand, while there is an increase in demand for accurate and clean data from business users, who are adopting more complex analytical approaches. This has meant investing heavily into data quality and governance software in recent years.

The overall data quality tools market has expanded from USD $610.2 million in 2017 to 1376.7 million by 2022 at a CAGR of 17.7% from 2017 to 2022. Some of the complex data quality platforms available in the market are Alation, a platform for broad range of data intelligence solutions that are fundamental to data search and discovery, data governance and stewardship alongside digital transformation, Ataccama ONE is another comprehensive data management and governance platform that includes master data management and data quality capabilities. The Collibra platform describes the structure of a piece of data and its relationship with other data. It assesses how and where data is stored and is used keeping business users in mind.

Problem of Variable Datasets-Specialty and Primary Care Data

Analytics managers in pharma are constantly trying to derive better and faster insights with newer and more non-conventional data sources. However, data operations processes are unable to cope up with this demand. A robust, dynamic and pharma specific data quality solution is not available. A combination of multiple factors including technology and process weaknesses coupled with diverse motivations of IT and business teams has led to this influx of data quality solutions into the market. However, none of these are purpose built for pharma, where there is a lot of complexity with each data source, and the data quality checks need to go beyond traditional IT checks such as count, LOV etc., This is further attributed by lack of data exchange standards, limited interoperability of data across systems, and an explosion in volume as well as complexity of data sets such as patient data, claims data and EMR/EHR data sources.

Inaccuracy and incompleteness of account masters pose a huge threat to segmentation and targeting in Pharma. This flows down to incentive compensation processes where sales need to be tracked.

The problem is more pronounced in the specialty pharma market, a rapidly growing pharma segment making up over 20% of the overall drug spend today and poised to grow to 50% in the years to come. The global specialty pharma market is expected to be of USD $568 billion by 2026. However, the emerging pharma segment comes with its own set of challenges, especially with data collection and consistency. The complex distribution model of specialty drugs, which involves specialty pharmacies, alternative sites of care, specialty physician, and distributors has resulted in multiple points of data generation, with no standards for data exchange.

The specialty distribution model also comprises full line wholesalers (such as AmerisourceBergen, McKesson etc.), Specialty distributors (such as ASD Healthcare, Curascript SD etc.), large and small specialty pharmacies chains, hospital pharmacies, and the physician clinics. While designing a specialty distribution model is important for the successful launch and continued success of a specialty drug, an equally important aspect is setting the data strategy for specialty drugs.

Some of the key success drivers for a specialty pharma company are:

  1. Reimbursement support (HUB services)
  2. Effective contracting
  3. Customized targeting
  4. Patient education and monitoring

The data collection points in the specialty data distribution model include syndicated data providers, infusion centres, wholesalers/specialty pharmacy distributors, specialty pharmacy hubs, individual specialty pharmacies, physicians, co-pay partners and call centres. Mastering data across so many entities and maintaining data quality is a huge challenge for all pharma companies.

Challenges in Existing Data Management and Quality Processes

The analytics maturity is therefore limited in many specialty-pharma companies, owing to challenges in the data management and quality. Data ownership is usually with the IT Teams or the business teams, depending on organization structuring. IT Teams, however, are not specialized to handle the specialty data. Despite the transformation from primary care to specialty pharma, IT teams continue to use trusted and tested legacy systems and processes that been developed and perfected with the primary care data. These systems and processes often fall short while handling specialty data.

There are a lot of differences in specialty and primary care data, which warrant different processes to be developed. One of the key differences is the need for an account master in specialty pharma which is detailed to a suite number level, while primary care relies more on physician masters. Physician mastering is relatively easier since standards such as NPI, state license, DEA etc. are easily available. With account mastering, the only identifiers if present, are HIN and DEA. The accuracy of these identifiers, even when present, is subject to doubt since it is dependent on the data collection points — distributors in this case. In the specialty pharma, every vial counts since they are high value-low volume products and incentivizing sales reps for each vial is crucial.

Methods and Approaches to Manage Data Challenges

One of the ways companies try to cope up with this problem is by relying on their sales teams. This poses a new problem — maintaining accuracy and defining trumping rules become exponentially more complex, which means a high manual stewardship process, at least till rules and logic is established for mastering account level data. Another issue with this method is that the contracting teams are constantly challenged with inaccurate data. Contracting teams must classify accounts as 340B and non-340B accounts depending on the type of patients who are visiting these accounts for infusion. This requires accurate patient flow data and a harmonization of data between 867, DDD, CARS (or other accounting systems) and ex-factory. The 340B and non-340B classification is a serious compliance requirement and errors in reporting this data can lead to compliance violations.

The need for accurate and mastered data also spans to the managed care, medical affairs, and sales teams. Specialty pharma claims are usually medical claims, as they need to be mostly administered and are buy and bill products, which is where the Hubs come into picture. Hub data is another source of external data. Medical affairs, managed care and sales teams need to view integrated data across claims (SHA or IMS), Hub and Sales (DDD/867/852).

Specialty pharma companies are currently handling these challenges through various methods and approaches, most of which are temporary fixes and are bound to create large issues in scalability. Specialty pharma companies with high IT spend are investing in more IT manpower, and platforms such as Reltio, VEEVA etc., while companies with low IT spend are finding work around ways and using external integration and mastering vendors such as LiquidHub and ValueCentric.

Summary of the Current Landscape

Pharma companies, specialty or otherwise are trying to cope with the data quality challenges by implementing various off-the-shelf data quality solutions. The challenge with these solutions is that they are not purpose built for pharma. They suffice in supporting typical ETL level IT Checks such as count, LOV, etc., However the issue in pharma data sources goes beyond just counts or list of values. Each data source requires a customized set of business level checks. For instance, in DDD, it does not suffice to just count the number of outlets. Addition of a few outlets would still pass the IT check thresholds, but these outlets could end up adding a huge number of sales, resulting a change in market share. For call activity, it does not suffice to just check if all entries have been made. Instances such as detail without call record must be checked. For specialty pharma data (867), a sudden purchase of a huge number of vials indicates possible purchase for clinical trial or erroneous product purchase. Such rules are not built into the off-the-shelf data quality platforms. They need expertise to set up comprehensive data quality checks – In defining every data quality check for every single data source. This expertise can only be provided by a group of business analysts from different functions working with the data governance & data quality teams. This is very resource intensive and is rarely feasible.

Mastering Data Quality and Governance: The Road Ahead

The problem with most approaches to handle data challenges is that they are temporary and short-term fixes. Such approaches either create more vendors and challenges to manage internal and external syndicated data sources with these vendors or create high dependency on vendors which become cost prohibitive over time. Data quality platforms available in the market are not customized to pharma data sources. Investing in such products oftentimes does not lead to significant improvement of data quality. Business teams continue to suffer with poor quality of data. Also, most of these platforms are implemented, managed, and monitored by IT Specialists, while the business teams need access to such a platform to check the quality of the data they are using. They need unique data quality insights into each data source they use. Hence, a highly customized platform purpose built for pharma is the need of the hour.

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