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Setting the Standard for
Quality Supply Chain Data
Data is Power
Kathleen Garvin
Program Manager, DoD/VA Data Synchronization, DoD Medical Logistics
Board of Directors, Coalition for Healthcare eStandards
HIMSS Annual Conference 1
February 25, 2007
Agenda
• Broken data in healthcare supply chain
– Recognition of need for quality data
– Current state of bad data Building to
Data Quality
• Need for Quality Data
– Organization asset
– How to Achieve
– Benefits
• Taking action
– Industry lessons learned
– DoD data quality efforts
– Need for Standards
2
Healthcare Supply Chain Data Broken
Enormous and Growing
– $1.9 trillion spent on healthcare in 2004; 16% of GDP
– $200 billion+ spent annually on supplies
Inefficient and Fragmented
– More than 5,800 hospitals in the U.S. buying from more than 22,000
manufacturers and distributors
– Error prone, paper-based manual processes
– Limited information
– Supply utilization information is not available in most organizations – not
tracked, not recorded or not used
– Low levels of IT investment
– Lack of industry standards
Sources: 2004 Centers for Medicare and Medicaid Services;EHCR 1996; industry publications 2003 3
Mounting Cost Pressures
Hospitals face tremendous cost pressures
– Falling revenue
– Increasing malpractice costs
– Decreasing reimbursements while improving quality of patient
care
– Increasing costs of products and services
60% of hospitals are losing money or breaking even
– Hospitals can achieve substantial savings and improve clinical
practices by better managing their labor, supplies, equipment
and facilities
Sources: HFMA 2003; AHA report 2001
4
Looking to Supply Chain for
Opportunities
Supply chain represents 1/3 of hospital’s total spend, second
only to labor
– Hospitals overpay 2 to 7 % for contracted med/surg supplies
– ½ of hospital purchases are made off contract
– Manual processes take 40% of buyer time, 68% of AP time
– 35% of orders are reworked due to errors
Suppliers Transportation Manufacturers Distributors Retailers Consumers
5
How Do We Get Data
• Manufacturer
– Generates product data to sell and market products
• GPO & Distributor
– Changes/creates/manipulates data to meet customer
base / regional needs
• User – Provider
– Creates internal data item masters/catalogs in order to identify products,
request & fill patient needs +
Objective should be: At the end of the data chain,
accurately & consistently identify the products that
touch the patient
6
Healthcare Supply Chain Data
BAD DATA
Can’t ID Source
Multiple Manufacturer Names Slows Operations
Multiple Product Numbers What is it?
Inconsistent Item Descriptions What you “see” may not be
what you want or get
Packaging Issues Order 50 receive 500
or
Order 20 Cases receive 20 Boxes
Contributes to multiple issues:
- Rogue Purchasing
- “Unofficial Inventories”
- Distrust of the supply chain 7
Bad Data: for Example….
Data Integrity? Multiple Mfgs Names (3M 300+)
“We must have unique identifiers on who the vendors are, on who the providers
are, on what the products are. “
“Cleaning up Supply Chain Data”, Materials Management in Healthcare, 7 Sep 2006
8
Bad Data: Multiple Product #s
SYRINGE INSULIN & NEEDLE PLASTIC U100 0.50ML 28GA x 1/2IN
Business Name Item Number Type Item Number
BD Mfg Catalog Number 329461
BD GTIN 00382903294619
BD GTIN 30382903294610
BD GTIN 50382903294614
CARDINAL HEALTH PV Order Number BF329461
OWENS & MINOR PV Order Number 0722329461
OWENS & MINOR PV Order Number 0723329461
AMERICAN MEDICAL DEPOT Vendor Catalog Number 777127217
AMERICAN MEDICAL DEPOT Vendor Catalog Number 777127218
GOVERNMENT SCI SOURCE Vendor Catalog Number FSC1482679CS
GOVERNMENT SCI SOURCE Vendor Catalog Number FSC1482679PK
ALLIANCE JOINT VENTURE Vendor Catalog Number 888021932
THOMAS SCIENTIFIC Vendor Catalog Number 8938M25
THOMAS SCIENTIFIC Vendor Catalog Number 8938M28
VWR INTERNATIONAL Vendor Catalog Number BD329461
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Every level within the Supply Chain has their own # to include Hospitals !!
Bad Data: Inconsistent Item
Descriptions
J&J in GHX TROCAR ENDO 5mm 100mm STABILITY SLV DILATING
RADPQ DISPO
J&J Direct ENDOPATH*DILAT TIP TROC 5MM LG
Cardinal HC TROCAR DILATING TIOP 5MM
Owens & Minor TROCAR ENDOPATH 5MM LNG DILATE TIP
= ?
10
Bad Data: Packaging Issues
J&J Ethicon Y426H
DoD Master BX of 36
Cardinal PK of 1, BX of 36
O&M EA of 1, DZ of 12, BX of 36
J&J GHX BX of 3
J&J Packaging Feed DZ 1, BX 3, CA 48
11
The Bad Data Story Continues….
How Data is Created
• Manual entry
– Fat Fingering errors multiplied across the supply chain
– Too many people have access to item master
– If can’t find a record, enter another (duplicate)
• Old, outdated catalogs
• Everyone creates their own (hospital, distributor, mfr)
But is it the “right” data …….? 12
Bad Data in Item Masters!
“…there was some sort of data entry issue for just about
every item….”
“…discontinued or duplicate listings…piling up like old
leaves, ties up orders and prevents use of the data…”
“Most hospitals are besieged by dirty files…”
Page, Leigh. “The high cost of dirty data.” Materials Management in Health Care. November 2005.
13
Signs Data are Dirty
• Clogged files
• Can’t find items
• Off-contract orders
• Missing fields
• Discrepancies
14
Effects of Bad Data
Adversely Impacts:
• Patient Safety
• Customer Support
• Operations
• Financial
• Clinical
• Technology
Need examples of each for narrative
15
Dirty Data – At What Cost?
• OVERPAYMENTS: Payments are higher than negotiated rates
because item entries contain incorrect information.
• LOST SAVINGS: Total volume is split among two or more duplicate
entries, barring entry into higher GPO tiers.
• SLOW-PAY PENALTY: Hospital doesn’t qualify for supplier’s fast
payment discount because accounts payable has to reconcile
inaccurate payment data.
• LOST OPPORTUNITY: Dirty data cannot be linked to financial and
clinical systems to study costs per procedure, per department, per
facility and per doctor.
• WASTED TIME: Clinicians are confused by order information and
accounts payable clerks have to devote time to reconciling
discrepancies in the order and invoice.
Page, Leigh. 16
“The high cost of dirty data”.Materials Management in Health Care.November 2005 superimpose over examples?
The Problem with Data
• “Care delivery organizations…require the equipment,
applications, procedures and policies to… make sure
that data is captured and interpreted correctly….”
Gartner Hype Cycle for Healthcare Provider Technologies, 3 July 2006
• “Data quality and existing MMIS systems limit health
care providers’ supply chain efforts….”
Lang, Mary Beth. “Tightening the Supply Chain”, Materials Management Magazine, March/April 2006
17
The Need for Data Quality
Data that is……
• Accurate / verifiable
• Universally accepted
• Sourceable / available throughout supply
chain
• Identical from Industry to the User
18
Data as an Asset
• Accountability from data entry clerk to CEO
• Methodology, not just technology
• Business Intelligence - Decision Support Systems
$ Tangible costs
Intangible costs
– Poor customer satisfaction
– Customer loyalty
– Reputation
– Patient safety
19
Informationweek, 30 Aug 2004
What to Do About Data Quality
Is There a better Way?
• Clean
• Standardize
DATA
• Synchronize SYNC
• Pilot Product Data Utility
20
Importance of Data
Multi-Industry Problem – Awash in Data
Does your company plan to invest in improved
data management?
64%
Telecom
61%
Travel or tourism
60%
Retail
59%
Financial services
59%
Utilities
25%
Public sector % of respondents answering yes
21
Source: Data QAS survey of 550 data management professionals, June 2005; informationweek.com April 10, 2006
Quality Data – Recognition of Benefits
What benefits has your organization derived from
rich, quality enterprise data?
Greater confidence in analytic systems 76%
Less time spent reconciling data 70%
Fewer problems with conflicting data 69%
Increased customer satisfaction 57%
Reduced costs
56%
Increased revenue
30%
Other
5%
22
Source: Data Warehouse Institute Survey, 2005; Materials Management in Health Care July 2006
A.T. Kearney Report
Grocery Industry
• Lost sales: $40 Billion (3.5%)
– Supply Chain information inefficiencies
• 30% Item data incorrect
– Correcting errors cost $60-$80 per item
• 25 minutes per SKU per year
– Manually cleansing out of sync data
• 60% Invoices generated have errors
– Correcting errors $40 to $400 each error
• New product rollouts take 11 weeks
– Inefficient and error prone data exchange
23
Other Industries: Data Sync Results
• Proctor &Gamble data sync Pilot with their customer H.E. Butt*
– 75% reduction in invoice discrepancies
– 30% improvement in the # of accurate purchase orders received
– 80% improvement in "speed to retail" for new items/price changes/ promotions
(from 10 days to 2 days)
• Additionally P&G expects synchronization to:**
– Save minimum of $25M a year & eliminate 30-50K hours in transcription work
• Wal-Mart data sync w/P&G resulted in :***
– Reduced data maintenance time from 15-30 days down to 1 day
– Achieved 98% up-to-date synchronization
– 15% increase in market share (up from 5% in new item introduction)
• In the food industry, Sara Lee reported:****
– 59% reduction in cost mismatches; errors resolved in 2 vs. 10-30 days
– Item mismatches were eliminated.
– Errors resolved in 2 days vs. 10-30 days
• Consumer Product Goods Manufacturers increased new product market share by 5-
15%. *****
* Proctor & Gamble/H.E. Butt UCS II Data Sync Case Study
** Action Plan to Accelerate Trading Partner Electronic Collaboration”, A.T. Kearney, GMA-FMI 24
*** Retail Systems 2002 Conf, Randy Salley, VP Merchandising Wal Mart
**** Martha Uhlhorn, EVP eCommerce & Category Mgt, Sara Lee presentation to the Magazine Publishers of America 3/18/2002
***** Medical Product Data Utility Feasibility Study, Hagemeier, Apr 03
Data Synchronization
= Value Proposition
Time Shelf-tag
Logistics costs Out-of-Stocks
handling item data & Scan Errors
and Inefficiency
2-4% reduction 1000s of
5-10% reduction
1%+ reduction
hours saved
Finance time Warehouse Speed to market
Inventory Costs
Audit fees Receiving
Storage Costs 2 week
Reconciling Invoices 1000’s of
.5-1% reduction reduction
5-10% reduction hours saved
25
Source: ATKearney Study 1/12/03 on behalf of FMI/GMA/UCC
Data Quality – The Foundation
• Quality Data is the precursor to accurate,
synchronized, centralized product data in patient
Electronic Health Records
• RFID
• FDA recognizes the need for standardized
synchronized quality data as precursor to UDI
• Business Intelligence
26
Managing Spend Performance
in Hospitals
Spend Analysis
Business Intelligence
– Optimize supplier negotiations
– Manage contract compliance
– Identify price overpayments
– Identify new opportunities
Source: “Deep-sixing dirty data” Rick Dana Barlow, HPN Nov 2005
27
DoD’s Need for Quality Data
Deployments Challenge
• Multiple Organizations (DOD, VA, FEMA)
• Short Notice & time frame for delivery
• Medical/Surgical Items Ordered with
Varying Descriptors, Identifiers, etc.
• Many Obsolete Product IDs
• Many Undecipherable Product IDs
• Resource-Intensive Cross-Referencing
for Equivalents
• Common ID and/or Synchronized Data
Would Have Increased Efficiency and
Improved Response Times
GOAL: Increase the combat readiness of America’s fighting
28
forces in both contingencies and wartime operations
DoD Business Case for Data Quality
• Improve Contingency/Wartime Operations Responsiveness
• Improve Supply Chain Efficiencies in Peacetime Operations
• Reduce Cost of Healthcare Delivery in DoD
CROSS SPECTRUM READINESS ENABLER 29
DoD Data Synchronization
Pilot Program Phase I
• Proof of Principle for Industry (Ongoing for 3 years)
• Manufacturer as source of data (23 mfgs, 2 prime distributors)
• Central Data Repository (Product Data Bank)
– Data Disconnects, Packaging levels, Audit Tool certification
– Feedback to Partners
30
DOD Data Synchronization
Value Proposition Example:
• Data Sync application tools identified
– Savings opportunities
• Better contract price available
• Saved $9.7M so far at 29 hospitals
– Opportunities to increase eCommerce
• Available from eCommerce sources
• Moved $2.6M to eCommerce sources
• Created robust DoD & VA Med Surg product data bank of 1.7 million + records
– Accurate master records for 93% of DoD buys
– Joint DoD & VA access to wealth of pricing, packaging, product ID information
• Created active collaboration with Healthcare Industry
– Two on going pilots with Mfgs, distributors etc
– PDU as goal within Healthcare Standards Organizations
– Single Federal voice for data quality (DoD/VA/FDA)
31
DoD Lessons Learned …
• DoD’s UPN Initiative 1993
• DoD Pilot PDU Phase I revealed:
– Distributors have the most accurate data
– Missing packaging levels throughout trading partners
– Bad or incomplete item descriptions throughout all levels
– Obsolete products still in the system
– UOM confusion/misuse
– Mfg naming problems especially at customer level
– Mfg UPN/bar codes not implemented
– Wrong prices being charged customers
32
DoD Lessons Learned …
• Cleaning and standardizing in-house data is not enough.
– Very expensive to constantly cleanse data – not the answer
– Adopt standards
– Adoption of an Industry PDU is the way to achieve Quality Medical product data
for the entire healthcare community
• Synchronizing and accessing data from central utility:
– Reduces bad data
– Reduces costs
– Increases operational efficiencies
33
Product Data Utility Concept
Manufacturers
Users
Aggregators (GPOs, IDNs, etc)
Distributors
Manufacturers PDU/Repository
Users
Distributors (GPOs, IDNs, etc)
data inputs
data outputs
34
DoD Other Healthcare Pilot Phase II
Participants
On Boarding
GDSN Partner
Manufacturers
(Ontuet) +
(BD, Sage)
On Boarding Hospital
1SYNC GPO
Partner (Baptist /
(data pool) (Premier)
(Ontuet) Lawson)
35
GDSN as Potential Healthcare Industry
PDU Solution Retail
Other GDSN
Data Pools
Suppliers
A B C
A GDSN
Exchanges
SOLUTION
PARTNERS*
B
Medical +
Data Pool
SELF Distributor A
IMPLEMENTATION Distributor B
SOLUTION PARTNERS OR SELF
C IMPLEMENTATION
GPOs
*50 Companies to include IBM, Sterling Commerce, Full Tilt and Comergent 36
Healthcare Community Needs Standards
• Medical surgical community needs what the industry has for Retail and
Groceries:
– Universal Product Code (UPC)
13542-0248-98
– UPN or Universal Product Number for medical/surgical products and the
“Universal Medical Surgical Product Catalog
• Several Standards are emerging & Standard Groups are taking
action
– A move to GDSN (5000+ companies & growing – Wal Mart, CVS, Walgreen etc)
– Adoption & use of GLN, GTINs, UNSPSC
37
Data Quality - Collaboration
What’s our Agenda
• Working with Industry Standards Groups for Global
Standards
– Coalition for Healthcare eStandards
– NAHIT/Healthcare Supply Chain Standards Council
• Actively working towards an Industry Solution
– PDU Organizing Committee
Partners for an Industry Solution
38
Data Synchronization & PDU
= Quality Product Data Benefits
• Standardized ID of product information throughout the Supply Chain
• Single source & access for accurate Product Information
• Improved Patient Safety
• Easier & faster sourcing of Products
• Decreased Costs (administrative, supply operations etc)
• Enhanced application of Technologies (RFID, Bar Coding, UDI)
Integrated Collaborative Solution = BENEFITS
+ + =
39
Resolve to Move Forward
Implementation Takes Collaboration
CLEAN DATA & ADOPT STANDARDS
PDU
SYNC WITH PARTNERS
IMPLEMENT UTILITY PROCESS MANUFACTURERS GPOs
DISTRIBUTORS
ROLLOUT INDUSTRY SOLUTION
QUALITY DATA SOLUTION WILL REQUIRE YOUR SUPPORT
40
Resources
• White paper “Data Synchronization in Healthcare: A Solvable
Problem,” W. Rosenfeld and J. Seltzer, Sterling
• Coalition for Healthcare eStandards, www.chesstandards.org
• Healthcare Supply Chain Standards Coalition, NAHIT
http://www.nahit.org
• CHeS/DoD Educational Webinar series, www.chesstandards.org
• DoD web page https://DMMonline.dscp.dla.mil
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