2014 11 - #AMIA2014 Panel - Local Terminologies and Standards - The Regenstrief Experience

8/10/2019 2014 11 - #AMIA2014 Panel - Local Terminologies and Standards - The Regenstrief Experience

1/43

Daniel J. Vreeman, PT, DPT, MScAssociate Research Professor

, Indiana University School of Medicine

Associate Director for Terminology Services, Regenstrief Institute, Inc

2014

Squaring the circleManaging local healthcare terminologies in the age of standardizationThe Regenstrief Experience

#AMIA2014#S02


2/43

Overview

1. We love standards2. We love our local terminology

3. Six lessons weve learned

photo via Vernio77
http://www.flickr.com/photos/verino77/


3/43

Informatics Pioneers

40 years of EMR work

Indiana Network for Patient Care

Regenstrief is the Data Switzerland

Most comprehensive and longest tenured Health InformationExchange (HIE) in USA

Crash Test Dummies


4/43

A Humongous Database

200+ source systems16+ million patients

5.6 billion results164 million text reports

more than 1 million transactions/day


5/43

photo via pagedooley

Standards let us (re)use the

data for everything

Clinical care, public health reporting, quality

management, clinical and epidemiological research, etc
http://www.flickr.com/photos/pagedooley/8435953365/sizes/o/in/photostream/


6/43

Data Standards UsGetting the data when and where it is needed

R


7/43

Long History

ASTM 1238-88

HL7 (O&O, V3, etc)

LOINCUCUM

Collaborator with other SDOsHL7, IHTSDO, RSNA, SCO


8/43

Logical

Observation

IdentifiersNames and

CodesA universal code system that facilitates exchange,

pooling, and processing of clinical data.


9/43

1

2

3

4

5

6

7

8

4/2

4/19

95|1

.0

5/24

/199

5|1

.0a

6/2

3/19

95|1

.0b

6/28

/199

5|1

.0c

7/14

/199

5|1

.0d

9/15

/199

5|1

.0e

12/21

/199

5|1

.0f

4/28

/199

6|1

.0g

8/29/19

96|1

.0h(

2)

12/3/19

96|1

.0h(

3)

1/8/

1999

7|1

.0i

10/9/

1997

|1.0j-a

5/1

/199

8|1

.0k

2/12

/199

9|1

.0l

7/23/

1999

|1.0

m

8/6

/199

9|1

.0m

a

2/4

/200

0|1

.0n

6/1

3/20

00|1

.00

1/1

/200

1|2

.00

1/3

1/20

01|2

.01

7/1

/200

1|2

.03

1/1

/200

2|2

.04

2/1

/200

2|2

.05

2/1

9/20

02|2

.06

8/1

/200

2|2

.07

9/1

/200

2|2

.08

5/1

/200

3|2

.09

10/1

/200

3|2

.10

1/1

/200

4|2

.11

2/1

/200

4|2

.12

12/1

/200

4|2

.13

12/1

/200

4|2

.14

6/1

/200

5|2

.15

12/1

/200

5|2

.16

6/1

/200

6|2

.17

12/21

/200

6|2.

19

6/21

/200

7|2.

21

12/03

/200

7|2.

22

7/10

/200

8|2.

24

1/10

/200

9|2.

26

07/06

/200

9|2.

27

12/30

/200

9|2.

29

2/26

/201

0|2.

30

6/15

/201

0|2.

32

12/29

/201

0|2.

34

06/30

/201

1|2.

36

12/30

/201

1|2.

38

6/30

/201

2|2.

40

12/30

/201

2|2.

42

06/29

/201

3|2.

44

12/26

/201

3|2.

46

06/26

/201

4|2.

48

LOINC Codes

Only Lab LOINC Codes

LOINC Codes Over Time by Release

laboratory terms only


10/43

35,000+ registered users in 163 countries


11/43

You might be thinkingLike LOINC, SNOMED CT, RxNORM, etcare all pretty mature now


12/43

Only crazy people wouldmanage their own terminology!

photo via Tatiana Gerus
https://www.flickr.com/photos/tgerus/14261835474


13/43

LOINC has Pretty Good Coverage

Lin MC, Vreeman DJ, McDonald CJ, Huff SM. A characterization of local LOINC mapping for laboratory tests in three large institutions. Methods Inf Med. 2011;50(2):105-14. doi:

10.3414/ME09-01-0072. Epub 2010 Aug 20. PubMed PMID: 20725694

Concept Type 63%-88%

Concept Token 72%-90%

For a big institution, if you exclude local codes not useful

for data exchange, concept token coverage can reach 99%

Theres a fair bit of highly localized contentin most systems.


14/43

The Regenstrief Dictionary

photo via muffet


15/43

Indiana Network for Patient Care

St. Vincent

St. Francis

IU Health

Wishard Health

Community

IUMG

Public Health

Global Patient

Index Concept

Dictionary

Referral Labs, RxHub, etc

Global Provider

Index


16/43

Regenstrief Dictionary

Core interface terminology

Progenitor of LOINC

More than 43,000 terms

Questions, answers, and units

HIE services and CPOE at one institution


17/43

Term Attributes

ID 13793

Universal Name CD3+CD4+ Cells

DescriptionNumber of CD4 T-cells (T4 helper cells) per volume. Both CD3+

and CD4+ are required to identify T4 helper cells.

Units #/mm3

Type (data type) 2

Code (class) t

Source IU Spec Hem

Synonyms Total T(T4); Abs T4; IU257; CD4 Abs (Helper T); Abs T (T4/CD4)


18/43


19/43

Lessons LearnedAlong the Way

photo via IMLS DCC
http://www.flickr.com/photos/imlsdcc/5637868432/sizes/o/in/photostream/


20/43

Lesson 1.Life is messy.


21/43

HL7 messages can be

syntactically correct but

semantically bastardized.

Everything is an OBX.

Everything in the NTE.

68 different terms forPathologist Name


22/43

Units of measure are unruly.

Sometimes missing (or in NTE).

Variation runs wild.>20% of Qn LOINCs had different units

Lin MC, Vreeman DJ, Huff SM. Investigating the Semantic Interoperability of Laboratory Data Exchanged Using LOINC Codes in Three Large Institutions. AMIA Annu Symp Proc. 2011;805-814.


23/43

Were Not AloneUnits for platelet counts in Mini-Sentinel

Blank FL TH/UL X10(3)

% K/CMM THOU/CMM 1000/UL

/100 W k/cmm thou/cmm X10(3)/MCL

/CMM K/CU MM thou/mm3 X10(3)/UL

CMM K/CUMM THOU/UL X10(6)/MCL

10 3 L K/MCL THOUS/CU.MM X10*9/L

10X3UL K/mcL THOUS/MCL X10E3/UL

10^3/UL K/UL THOU/mcL X1000

10*3/uL k/uL THOUS/UL X10X3

10?3/uL KU/L Thou/uL X10^3/UL

10E3/uL K/MM3 THOUSA x10

10e3/uL K/mm3 THOUSAND X10?3/ul

10e9/L LB THOUSAND/UL X10E3/UL

E9/L PLATELET CO U X10E3

BIL/L T/CMM X 10-3/UL K/A?L

bil/L TH/MM3 X 10(3)/UL K/B5L

CU MM th/mm3 X10 3

Raebel MA, Haynes K, Woodworth TS, et al. Electronic clinical laboratory test results data tables: lessons from Mini-Sentinel. Pharmacoepidemiol Drug Saf. 2014 Jun;23(6):609-18.


24/43

Lesson 2.Terminology management is hard.

A short history of modernist painting (part 2) by Mark Tansey

photo via brainblogger
http://en.wikipedia.org/wiki/Mark_Tanseyhttps://www.flickr.com/photos/brainblogger/3137589099


25/43

It might not be brain

surgery, but it requires bothexpertiseand effortthat is

easy to underestimate.

Baorto DM, Cimino JJ, Parvin CA, Kahn MG. Combining laboratory data sets from multiple institutions using the logical observation identifier names and codes (LOINC). Int J Med Inform.1998 Jul;51(1):29-37.


26/43

Ajourney, not adestination.


27/43

Vreeman DJ, Stark M, Tomashefski GL, Phillips DR, Dexter PR. Embracing change in a health information exchange. AMIA Annu Symp Proc. 2008 Nov 6:768-72.

Across many INPCinstitutions, in 2 years after

go live we saw half as manynew local terms appearas

what we started with.


28/43

Meanwhile, weve always

struggled to find adequatefunding for this work.

photo via Vincent Anderlucci
https://www.flickr.com/photos/114144234@N04/15201247915


29/43

Lesson 3.Clear descriptions are king.

photo via JD Hancock
https://www.flickr.com/photos/jdhancock/7238856518


30/43

Humans need to choose terms accurately

Clear, concise text descriptions for each

term are essential.

Descriptions are King

We didnt always understand this.

photo via _fxr
https://www.flickr.com/photos/_fxr/2359186938


31/43

Distinguishes this concept from others

Describes what the heck it is

Good Descriptions

What does it measure?What is it used for?What is the clinical relevance?How is the test performed?

photo via _fxr
https://www.flickr.com/photos/_fxr/2359186938


32/43

Lesson 4.Sometimes we bend the rules.

photo via AndYaDontStop


33/43

We love the desiderata, but

Sometimes we cant get the softwaredevelopment done right, so we make

compromises with the terminology.

Examples:

flowsheet display of tests in multiple panelswriting transforms for everythings an OBX HL7

violate concept orientation

inelegant context representation

Terms are cheap!- Paul Dexter

Cimino JJ. Desiderata for controlled medical vocabularies in the 21st century. Meth Inform Med 1998;37:394403.


34/43

Lesson 5.Work smarter, not harder.

photo via consumerist
https://www.flickr.com/photos/consumerist/438922042


35/43

Not every term has equal value

Pareto Principle

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 500 1000 1500 2000 2500 3000 3500 4000

CumulativeLaboratoryObservationVolume(%)

Number of Laboratory Observation Codes N)

Vreeman DJ, Finnell JT, Overhage JM. A rationale f or parsimonious laboratory term mapping by frequency.

AMIA Annu Symp Proc. 2007 Oct 11:771-5. PubMed PMID: 18693941.

In INPC, 80 codesmade 80% of volume


36/43

photo via Fishking_1

Upstream standardization.Ask instrument or test kit

manufacturers.
http://www.flickr.com/photos/fishking1/5304638016/sizes/o/in/photostream/


37/43

Lesson 6.Standards that get used get better.


38/43

Sharing is Caring

Standards will get better if users helpimprove them through new term requests.

Most local vocabulary work is done in

isolation at each institution and not shared.

Even among users of the same vendor.

We built this concept into LOINC license & culture from the beginning.

Novel uses of crowd-sourcing? Need a connection back to standards.On the mapping front, see https://loinc.org/community/mappings
https://loinc.org/community/mappings


39/43

Pros and ConsWeighing the benefits and costs ofmaintaining a local master dictionary

photo via neurmadic aesthetic


40/43

Pros

1. Ultimate control2. Single code system for

applications across all

content domains

Hugely valuable


41/43

Cons1. Expensive2. Cant easily use tools from

others3. Others cant use our innovation

applications as easily


42/43

Were trying to find

harmony using local termsand standards together

photo via scarlatti2004_images

We love local terms! We love standards!
http://www.flickr.com/photos/scarlatti2004_images


43/43

danielvreeman.com/speaking
http://danielvreeman.com/speaking

Documents

2014 11 - #AMIA2014 Panel - Local Terminologies and Standards - The Regenstrief Experience