Poor data quality is one of the greatest obstacles to an efficient supply chain. Proprietary identification schemas, incomplete data, moving and comparing data across organizations and manual data entry create too many opportunities for loss of data integrity.
Let’s trace some of the data flow:
A manufacturer creates a product and assigns an internal proprietary identification number to the item in its enterprise resource planning (ERP) system. A Global Trade Item Number (GTIN) may or may not be assigned. Over time, the manufacturer may create multiple catalog numbers for the same item, and the number used to order the item via electronic data interface could be different. The product may or may not be labeled with a bar code, and multiple bar codes may be used. The manufacturer catalog number along with the price and unit of measure (UOM) are sent to GPOs and distributors. There are often conversion issues between text and numerical data. The UOM may or may not follow the American National Standards Institute (ANSI) standard. Packaging string data may be missing or incomplete. Distributors also send the GPO their proprietary catalog number for the product, along with their UOM and contracted price. A distributor may repackage the product creating a new UOM. Each organization will have its own description for the product. The GPO publishes the catalog numbers, UOM and price to its provider members and the GPO membership, and pricing data are entered into the manufacturer and distributor systems. The providers enter the catalog numbers into their ERP systems, assigning another proprietary identifier, along with UOM and price. Purchase orders are created in the ERP systems, and fax and email are often used to communicate the orders with suppliers. All parties maintain their own proprietary location identifiers. Received goods are often relabeled with the provider’s proprietary identifier.
As depicted above, data quality issues can affect nearly every point in the supply chain, for example:
- Vendor part number, price and UOM discrepancies
- Manual purchase order validation and invoice reconciliation
- Errors in ordering and order fulfillment
- Longer days’ payable outstanding
- Inability to determine on-contract spend and perform spend analysis
- Issues with data exchange
- Expired inventory and inability to effectively address product recalls
- Manual relabeling of product
- Incorrect patient charges
Fortunately, forward-thinking manufacturers, distributors, providers, and organizations such as GS1 and the Food and Drug Administration are solving these issues with data standards and more automated processes. GTIN is a globally unique product/package identifier that will transform the healthcare supply chain and eliminate many of the issues listed above. The Unique Device Identifier (UDI) being established by the FDA makes the GTIN (or Health Industry Business Communications Council) identification mandatory and will further the ability to track expiration date and lot/serial number for improved expiration and recall management. And mandatory machine-readable identification will enable product tracking from purchasing to patient. A Global Location Number (GLN) is used as a consistent standard to identify delivery locations and to replace custom account numbers. Other industry standards such as the ANSI UOM standard and the United Nations Standard Products and Services Code (UNSPSC) allow more precise identification and categorization of items. And the GS1 Global Data Synchronization Network as well as the FDA Global Unique Device identifier database will allow more up-to-date and automated sharing of data.
The efficiencies and cost reductions from these standards will be substantial. But to realize the benefits, organizations must incorporate and utilize the standards in their systems and processes. This will take many years, so in the short term (and into the future) healthcare can learn from other industries by implementing other data quality management techniques.
For example, ROi is implementing a Master Data Management (MDM) program. This comprises several data management practices. A single item master is used throughout the enterprise. A data governance committee approves item descriptions and UNSPSC classifications, and data stewards are responsible for improving and maintaining data quality. A central data quality group audits and reports on data quality.
Also central to the MDM program, Optimé Supply Chain and ROi are developing an MDM tool that enables the normalization and standardization of data elements. The application accepts data loads from multiple sources using standard extract, transform and load capabilities. As the data are loaded, the application allows – and assists through user-defined business rules – the creation of master records and aliases. Hierarchical rules are used to ensure data attributes are populated according to level of trust in the source systems, and probability matching is used to find aliases. Suppliers and items are the current focus of the MDM effort.
Another example of an automated data quality technique that ROi is implementing is the data quality check. As data flow through interfaces or the data warehouse, a series of business rules are applied to the data, termed a “data quality check.” If the data fail to pass a quality check, they are flagged with either a warning or an error. Error records are diverted to a “holding cell,” and email notifications are automatically sent to data stewards who must correct the data before it can resume the data flow. Other data quality checks run across systems in batch mode. The number and types of warnings and errors are recorded to analyze, measure and improve processes and systems managing data and data quality overall.
In conclusion, this article has presented a sample of the data quality issues being encountered, and solutions being implemented, in healthcare today. Tremendous opportunities exist, and those organizations that invest in data quality will realize immediate benefits and help transform our national healthcare system into the future.
Paul Helmering is Vice President of Technology, Information & Business Solutions for ROi, the St. Louis-based supply chain organization founded by Mercy, which integrates supply chain functions traditionally performed by commercial distributors, GPOs, manufacturers and consultants.
