The healthcare supply chain is in a data crisis.
It’s not that we don’t have data—we have lots of it. But it’s hard to share and even harder to get meaning out of it. So in spite of all the dollars and effort spent on technology, you can’t get the answers you need to better run your hospital. Knowledge fails to launch.
The issue lies, not in the technology, but in the data itself.
Data within the healthcare supply chain is unstructured and inconsistent. It’s no wonder we can’t aggregate, compare and extract meaningful information.
Industry organizations are advocating product global data standards,[i] and the Food & Drug Administration now requires a unique device identifier (UDI) for medical devices.[ii] However, we are still light years away from realizing the same bar code efficiencies as your local grocery store.
Why is it so hard to reach data commonality?
- Human nature. We are descriptive, impatient beings. We like to give things nicknames. So maybe in Minnesota, clinicians call it an “under pad”; and perhaps in Tennessee, a “chuck”. And if people can’t find what they need in 30 seconds, they “free text” their own order.
- Each player has different data needs. Material codes are caught in the 20-character-long descriptive field, (a hold-over from paper-based ordering). But these cryptic abbreviations don’t make sense to clinicians looking to order an “under pad” or a “Christmas tree”. And vendors have their own codes too. Further when health systems merge or acquire, the naming conventions typically don’t match, not even close.
- Data is not shared. Departments and facilities like to keep their data to themselves. Systems don’t talk, creating data silos. And we’ve found that 40-50% of spend occurs outside the item master[iii], so it’s invisible to your internal systems and doesn’t follow the naming “rules”.
- Sheer volume of products and changes. Data is a moving target. A middle-tier IDN has 60,000-100,000 products in its Item Master, and up to 30% of it changes every year. So basically every five years, your item master is new. GPOs change 30% of contracts annually.
- Cost: There is a huge re-engineering cost to comply with a universal coding system.
So with the content mess between variables and players, how will you ever get to data consensus?
I challenge you to change the mission. Don’t try to conform data to fit your systems; find a system that accepts your data.
Today’s eProcurement systems capture and organize your current data so it’s usable and searchable. The systems harness space-age technology, like algorithms and artificial intelligence (AI), to normalize data so product attributes are structured in a consistent manner. And once your data speaks the same language, you can easily find the products and answers you need.
An eProcurement system can provide the following:
- Take away the chore of “cleaning” data. Continually update and merge data, so it stays current and accurate
- Make it easy for users to find and compare stuff. Allow people to search using real words / colloquial terms they’re familiar with; and automatically identify and group by functional similarity.
- Normalize ALL your data. Combine all internal (item, contract, charge data) and external data (GPO contracts and vendor catalogs)—into one source of data truth.
- Provide open accessibility: Make the data accessible to everyone, at any time through the cloud.
- Use your “rules” to direct employees to the best choices. Identify the contract line and price that provides the best value to your organization.
Watch this video to see how you can better utilize your current data to get the answers you need, and to launch your organization’s profitability.
[i] Healthcare Supply Chain Data Standards; Association for Healthcare Resource & Materials Management website; http://www.ahrmm.org/ahrmm/ext/supply_chain_standards/faq.html
[ii] Unique Device Identification (UDI); U.S. Food and Drug Administration website: http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/UniqueDeviceIdentification/default.htm
[iii] McKesson/Meperia assessment data from 100 mid-sized healthcare systems.