Tag Archives: clean data

Enhance the Effectiveness of Your Supply Chain with Clean Data

By Shon Wettstein, Vice President of Business Development and Product Management

Clean data could be defined as accurate catalog fields that reduce transactional discrepancies, promote in-depth organizational reporting and assist in the implementation of standardized product categories across your enterprise. Clean data is always a work in progress, not just an outcome. So what are some of the main issues or problems around “item enhancement,” and data cleansing in your item master? And why is it so important? 

Who benefits from high quality item master data? The answer is…EVERYONE! The benefits of high quality data are nearly endless. From scheduling cases to prepping cases to usage and documentation, to purchase order processing and transport, to payment process and transport, all the way downstream to the most important benefactor – the patient.

Every single day the healthcare supply chain wastes 24-30 percent of supply administration time on data cleansing and corrections, costing the healthcare industry billions of dollars. What can be done?

Adopting GS1 standards for healthcare is a first step. GS1 is the primary organization that also defines the standards for the UPC that you see on all retail products. The major elements of the GS1 standards are the 3 Gs which help in the identification, locations and data attribute accuracy of products in the supply chain.

By having a unique product number for every item, it will allow for the free flow of products and associated information from the point of manufacture to the point of use. Key elements of this also include unique markings that allow for the easy capture of information on the product and the sharing of that information throughout the supply chain.

How can you further begin to build a framework for item master efficiency? First, establish data governance. Make sure that item master files have consistent established rules regulated by a few within the organization. You should also be managing data at all points of entry, whether coming internally from supply chain or the stakeholder. Also, establish a consistent process and procedure in areas of requisitioning, order fulfillment, purchasing and accounts payable, and identify opportunities to collaborate with other departments that are using supply chain data, such as surgery system, patient charging system, etc.

Next, you should develop basic item master policies that include things such as product series consistency, units of measure, descriptions, etc. Adhere to the policies, and track the exceptions.

In conclusion, the item master is your organization’s information source for some of the most important supply chain activities – procurement, charge master comparison and/or linking, data standardization and value analysis. If this data is disorganized and contains errors, then it will be very difficult to improve operations and control costs in your facility. Maintaining your item master will help you use the right products that not only drive cost containment but provide quality care and improve patient outcomes.

Read our full article Item Master Efficiency and Clean Data. By leveraging industry standard data sources such as GUDID, GDSN and First DataBank, Intalere OptiMIM Advantage combines data to a single trusted source that will improve operational efficiencies and reduce financial, safety, regulatory and product risk. Intalere OptiMIM Advantage also enables the supply chain to more efficiently leverage data to perform analytics of supply items to reduce supply costs, increase procure-to-pay (P2P) efficiencies, and improve integration and accuracy on point-of-use capture. Contact Intalere to learn more.

Intalere Member Best Practice Spotlight: Keep It Clean – Maintaining a Clean Item Master

ReadingHospital_evening_7x5As we recognize National Healthcare Supply Chain Week, we focus on how Reading Health System instituted a process to cleanse their item master and ensure quality data.


Data is the primary source for decision making in every institution – invalid or inaccurate data contributes to unreliable results and when data is unreliable, predictive models become undependable and calculations are less precise. Maintaining clean data is a challenge.


Reading Health System’s data cleanse plan consists of a repeated process – Analyze, Standardize, Implement and Monitor:

  • Analyze data to identify the outliers.
  • Standardize, Standardize, Standardize – Standardization is a requirement to keep data clean.
  • Implement – Create scripts and workflows to standardize the flow of new and updated data into the system. Removing manual reporting and keying is integral in cleaning and maintaining clean data.
  • Monitor – Set up periodic reviews, daily, weekly, monthly and yearly, so that inconsistencies can be monitored before they become a problem.


Reading Health System receives accolades from its data recipients, vendors and consultants for the quality of their data. The data which consists of 45,000 items has been evaluated as 92 percent clean, one of the highest they have seen.

About Reading Health System

Founded in 1867, Reading Health System is a not-for-profit tertiary healthcare system providing services to approximately 800,000 people in Berks, Chester, Lancaster, Lebanon, Lehigh, Montgomery and Schuylkill Counties. Reading Health System is the largest employer in Berks County with more than 7,000 employees. The mission of Reading Health System is to provide compassionate, accessible, high quality cost effective health care to the community without distinctions to race, color, age, creed, handicap, sex, national origin, or economic status; to promote health; to educate healthcare professional and the public; and to participate in the appropriate clinical research. Reading Health and its physicians are committed to supporting the health and wellness of our community.

Read more Intalere member success stories, including best practices now!