=Paper=
{{Paper
|id=None
|storemode=property
|title=Ontologies Put More Meaning into Meaningful Use
|pdfUrl=https://ceur-ws.org/Vol-952/paper_25.pdf
|volume=Vol-952
|dblpUrl=https://dblp.org/rec/conf/swat4ls/AdamusiakSFS12
}}
==Ontologies Put More Meaning into Meaningful Use==
https://ceur-ws.org/Vol-952/paper_25.pdf
Ontologies put more meaning into Meaningful Use
Tomasz Adamusiak1 , Naoki Shimoyama1 , Alexandra Fuiks1 , and
Mary Shimoyama1,2
1
Human and Molecular Genetics Center, Medical College of Wisconsin, Milwaukee, WI,
United States
2
Department of Surgery, Medical College of Wisconsin, Milwaukee, WI, United States
{tadamusiak,shimoyama}@mcw.edu
Abstract The Health Information Technology for Economic and Clinical Health
(HITECH) Act, enacted as part of the American Recovery and Reinvestment Act
of 2009, positioned the Meaningful Use of interoperable Electronic Health Re-
cords as a critical goal and encouraged nationwide EHR adoption. The Consol-
idated Health Informatics (CHI) initiative recommended the following three ter-
minologies for EHRs: SNOMED CT, LOINC, and RxNorm to meet the Meaning-
ful Use objectives. All three are integrated within the Unified Medical Language
System (UMLS), designed and maintained by the US National Library of Medi-
cine.
The Clinical Informatics team at the Medical College Wisconsin is developing
ClinMynEHR, a clinical data portal created to annotate EHRs for selected pedi-
atric patients treated at the Children’s Hospital of Wisconsin. Data for the system
consists of many clinical and referral documents the patients have accumulated
along their clinical odysseys. ClinMynEHR consists of a comprehensive clinical
database, query and reporting tools, and incorporates phenotypes, clinical meas-
urements, lab test results, medications and other clinical information standardized
through Meaningful Use ontologies integrated within the UMLS.
Introduction
Informatics have a long history in medicine [10], but with some notable exceptions
[14] their impact on medical practice was relatively low and predominantly motivated
by advances in medical imaging. The U.S. health care system is now in the process of
nationwide transformation through the adoption of Electronic Health Records (EHRs).
The federal EHR Incentive Programs only this year provided payments to more than
100,000 health care providers for their implementation and Meaningful Use certified
EHR technology [2].
The 2009 HITECH Act introduced the concept of Meaningful Use of information
technology in health care. The definition of Meaningful Use in this context is complex
and consists of several objectives and measures the providers have to demonstrate in
three stages and within strict timelines in order to be eligible for early adopter incent-
ives and later on to avoid penalties for non-compliance. From the standpoint of semantic
interoperability perhaps the most interesting are the recently released Meaningful Use
Stage 2 Rules as they define the mandatory vocabularies to be used in EHR data ex-
change [1].
SNOMED CT (Systematized Nomenclature of Medicine, Clinical Terms) is the
most comprehensive, multilingual biomedical terminology in the world. It provides
terms, synonyms and relations covering a number of clinical domains including dis-
eases, findings, and procedures [17]. LOINC (Logical Observation Identifiers Names
and Codes) is a universal standard for identifying laboratory observations. It can be con-
sidered the lingua franca of clinical observation exchange as it has more than 15,000
users in 145 countries [13]. RxNorm is a standardized nomenclature for generic and
branded drugs, as well as drug delivery devices [16]. All three terminologies are integ-
rated within the UMLS (Unified Medical Language System) maintained by the National
Library of Medicine (NLM) [11].
ClinMynEHR is our clinical research portal for information on selected pediatric pa-
tients with suspected genetic disorders treated at the Children’s Hospital of Wisconsin.
This group of patients is unique as making a definitive diagnosis often requires extens-
ive workup and involves disparate health care providers. Clinical documents within the
system are manually annotated with ontology terms from SNOMED CT, LOINC and
RxNorm. In our experience, these ontologies provide sufficient coverage for disease
phenotypes, lab results, procedures, and medications for our research related use cases.
Manual curation, while time consuming, is more flexible and provides higher precision
and recall than currently available text-mining algorithms. We envision that in the fu-
ture it would be possible to import annotated data directly from the hospital’s EHR. In
that sense, using Meaningful Use ontologies is a means of future proofing our system.
Next-generation phenotyping
Hripcsak et al. postulated that with the unprecedented amount of clinical data becoming
available we will also need a paradigm shift in how we approach the valuable inform-
ation locked in current generation EHRs and novel phenotyping methods that take into
account often incomplete or inaccurate, complex data [7].
Clinical narrative in its raw form is generally not amenable to computational ana-
lysis. On the other hand, structured data entry has its own disadvantages as it is more
time-consuming [12,4] and offers less flexibility and expressiveness in data capture
[15,9]. Unfortunately, the use of narrative encourages redundancy through copy-and-
paste [5,21,6], and some parts of the records exist solely for medicolegal, reimburse-
ment, and regulatory requirements [3].
As much as 16% of clinical notes may never be read by anyone [8]. Clinically
important documents related to continuity of care (signouts) are often not recorded
electronically, as they are traditionally not included as part of an official medical record
[20,18]. Finally, some relevant information is invariably lost when laboratory tests are
sent to external reference laboratories or when patients fill their prescriptions outside
provider’s network [12].
Not all information in electronic health records is likely to be relevant. It is reason-
able to expect that some of the information can be ignored depending on the application
context. Quality assessment can be to some extent standardized using dedicated instru-
ments, such as the one developed by Stetson et al. [19].
Conclusion
There are some unique challenges in applying ontologies to clinical data as well as how
we traditionally perceive health records as simple collections of documents. Enabling
interoperability on unprecedented scale, widespread use of standard and universal on-
tologies will be the driving force behind the transformation of health care and the more
meaningful use of health information technology.
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