Automated Mapping of Pharmacy
Orders from Two Electronic Health
Record Systems within the
STRIDE Clinical Data Warehouse
Penni Hernandez, N.D., R.N., Tanya Podchiyska
Susan Weber, Ph.D., Todd Ferris, M.D., M.S.
Henry Lowe, M.D.
Center for Clinical Informatics, Stanford University,
Stanford CA
© 2009. All rights reserved.
Outline
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STRIDE Overview
Problem Definition
Strategy
Terminology Standard Selection
Methodology
Matching Algorithm
Applied Examples
Use of SNOMED-CT® for drug classification
Evaluation Methodology
Results
Conclusion
11/06/09
Stanford University Medical Center
 A complex research-intensive organization
 Lucile Packard Children’s Hospital (LPCH)
 Stanford Hospitals & Clinics (SHC)
 Stanford School of Medicine
 Two different EHR systems
 Cerner at LPCH, Epic at SHC
 Clinical researchers expect an integrated and
coherent environment that supports their
needs
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STRIDE Overview
 Stanford Translational Research Integrated
Database Environment (STRIDE)
 Standards-based research and
development project supporting clinical
and translational research
 Clinical data warehouse (CDW) containing
clinical information on over 1.3 million
patients
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STRIDE Overview
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Problem Definition
 Users expect an integrated environment and
the ability to search medications by Generic
Ingredient, Brand Name and Drug
Classification
 Hospitals operate different EHR systems with
different drug information providers
 SUMC hospitals are cooperating to share
content but their pharmacy data is not
interoperable
 Solution needed to be rapidly implemented,
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Strategy
 Define research use cases and
requirements for data retrieval and
interoperability
 Merge the pharmacy order data into a
single standards-based model that will
support integrated representation
 Goal was to achieve complete mapping of
each hospital pharmacy order to a RxNorm
Ingredient (IN)
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Terminology Standards Selection
 Selected RxNorm for drug representation
 Full model with explicit relationships
 Interlingua between source vocabularies (e.g.
SNOMED-CT®)
 Robust coverage of medications in U.S.
 No license fees and actively maintained by NLM
 Selected SNOMED-CT® for drug
classification
 Selected FDA Structured Product Labeling
(SPL) data standard for Route of
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Drug Representation Strategy
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Methodology
 Selected HL7 v2.3 SUMC Pharmacy Order
(RDE) messages as the initial source of
data
 Targeted segments:
 Pharmacy Encoded Order (RXE) segment
 Pharmacy Component (RXC) segment
 Pharmacy Route (RXR) segment
 Developed an algorithm using PL/SQL to
match message segments to RxNorm
atoms of type “IN”
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Data Elements
Header Data
HL7 v2.x Pharmacy Order Message
MSH|^~\&|EPIC|PHARM|STRIDE|SOM|20050403001656|S3820351
|RDE^O01|4830577|P|2.4|||^M
PID|||9999999^^^SHC^SMRN||HERNANDEZ^PENNI^M||19451120|
F|||8472 Dartmouth^Boulder^CO^80305^^^^||(303)5555555||E|MINXX|03822739-203|999-99-999||||^M
PV1|||POINT OF CARE^ROOM^BED|
ORC|NW|8392009287^|||||^EVERY 3 HOURS PRN^^ 2080726000^^
Norm^PRN||20080724|||||||||^Hernandez^Penni^M^^^^^SH
C^^^^MSPV||(303)555-5555 |||||||STANFORD
HOSPITAL|||^M
RXE|^ONCE&^X1^200901210030^200901210004^Fax^|1834^IBUP
ROFEN 100 MG/5ML PO SUSP^ADS|95||3^mg|Shake well.
Give with food or milk.|||1|||BV9999999^
Hernandez^Penni^^^||||||||||||||||||^ADVIL, MOTRIN^M
RXR||PO^Oral|||^M
RXC||1834^IBUPROFEN 100 MG/5ML PO SUSP^ADS|5mL|||^Advil,
MOTRIN^M^M
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Matching Algorithm
 Algorithm Input
 RXE and RXC data fields
 Give Code – local identifier for the drug order
 Give Text – drug name, form and strength
 Give Alt Text – alternate representation of “Give
Text”
 Multi-ingredient drug orders are split by a
series of delimiters before a match is
attempted
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Matching Algorithm
 Ignoring case, compare only alphanumeric
characters to the “STR” column in the
RxNorm concept table “RXNCONSO”
 If a match is found, then the RXNREL table
is used to navigate to the RXCUIs for the
clinically active ingredients
 If a match is not found, then the last word
in the original string is stripped and another
attempt is made for matching
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Branded Pack Example
 Hospital Input String: LO OVRAL-28 TABLET
 loovral28tablet (no match)
 loovral28 (match for RXCUI = 749787)
 Identified BPCK:
 {21 (Ethinyl Estradiol 0.03 MG / Norgestrel 0.3 MG
Oral Tablet) / 7 (Inert Ingredients 1 MG Oral Tablet) }
Pack [Lo/Ovral 28 Day]
 "has_tradename" GPCK:
 {21 (Ethinyl Estradiol 0.03 MG / Norgestrel 0.3 MG
Oral Tablet) / 7 (Inert Ingredients 1 MG Oral Tablet) }
Pack
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Branded Pack Example
 "contained_in" SCDs:
 Ethinyl Estradiol 0.03 MG / Norgestrel 0.3 MG Oral
Tablet
 Inert Ingredients 1 MG Oral Tablet
 "constitutes" SCDCs:
 Ethinyl Estradiol 0.03 MG
 Inert Ingredients 1 MG
 Norgestrel 0.3 MG
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Branded Pack Example
 "ingredient_of" INs:
 Ethinyl Estradiol (rxcui= 4124, SNOMED CT concept_id=15432003)
 Inert Ingredients (rxcui=748794)
 Norgestrel (rxcui=7518, concept_id=82240008)
 SNOMED drug classes:
IS-A
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Progestin preparation (product)
Oral contraceptive preparation (product)
Contraceptives (product)
Reproductive system drug (product)
Estradiol preparation (product)
Sex hormone product (product)
Hormone preparation (product)
Hormones, synthetic substitutes and antagonists (product)
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Use RXCUIs to Traverse to SNOMED-CT®
•RXCUI unifies all
strings
representing
Hydrocodone no
matter what the
content source
•Leverage mapping
to navigate from
RxNorm to
SNOMED-CT®
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Evaluation Methodology
•Found ingredient,
verified correct = TP
•Found ingredient,
verified incorrect = FP
•Found no ingredient,
verified correct = TN
•Found no ingredient,
verified incorrect = FN
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Evaluating Multiple Ingredient Drugs
•Found both ingredients,
verified correct = TP
•Found only one ingredient,
verified incorrect = FP
•Found both ingredients
but one was incorrect
verified incorrect= FP
•Found neither ingredient,
verified incorrect = FN
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Results
LPCH
%
Total
Number
8895
93.28
True
Negative
316
False
Positive
305
True Positive
Total
Number
•Algorithm correctly
False
20
Negative
Pharmacy Orders
SHC
%
Total
Number
%
6665
92.70
2230
95.06
3.31
270
3.76
46
1.96
3.20
240
3.34
65
2.77
mapped
approximately
0.21
15
0.21 93% of5SUMC
0.21
•No suitable RxNorm concept could be found for 3% of
SUMC Pharmacy Orders
•Approximately 4% required algorithm adjustments
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Characterization of True Negatives
Ambiguity in
Original
Message
Outside the Scope Limitation of
of RxNorm
Algorithm
Non Drug
Orders
•Seasonal
vaccine
•Total
parenteral
nutrition
•Peritoneal
dialysis
solution
•Iron, no salt
•Vitamins (> 4
ingredients)
•Over the
counter (OTC)
product
•Investigational
Drug
•Non-US Drug
•Dummy
Order
•Devices
•Supplies
•Non Drug
Kit
•Valid
Abbreviation
•Fragment,
beyond
machine
readability
•Local name
for a custom
compound
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New Pharmacy Orders by Month
10400
•Algorithm is
in production
10300
10200
10100
New
Mapped
Orders
New
Unmapped
Orders
Reviewed
Orders
10000
9900
9800
9700
9600
July
August September
11/06/09
•Ongoing
review of new
Pharmacy
Orders takes
less than
4
hours/month
Value Proposition to STRIDE
 Utilization of linkages within RxNorm
 Brand Name (BN) to Ingredient (IN)
 Ingredient (IN) to SNOMED-CT® Product
 Aligned with emerging standards
 Flexibility to incorporate other drug
information sources (e.g. Clinical
documents)
 Support search by drug concept
 Potential interoperability with other data
11/06/09
More Information
 Stanford Center for Clinical Informatics
 http://clinicalinformatics.stanford.edu
 Contact information:
 Penni.Hernandez@stanford.edu
 RxNorm
 http://www.nlm.nih.gov/research/umls/rxnorm/
 RxNav
 http://mor.nlm.nih.gov/download/rxnav/
11/06/09