Semantic Integration of Patient Data and Quality Indicators based on openEHR Archetypes
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Transcript of Semantic Integration of Patient Data and Quality Indicators based on openEHR Archetypes
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Semantic Integration of Patient Data and Quality
Indicators based on openEHR Archetypes Kathrin Dentler, Annette ten Teije, Ronald
Cornet and Nicolette de Keizer
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meaning-based integration required => archetypes!
Patient Data
valuable, but semantic gaps
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Quality Indicators • Should be
well-formalised: executable, sharable & comparable results
• CLIF • Research
question: archetypes?
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Outline
1) CLIF 2) Archetypes 3) Formalisation of indicator 4) “Archetyped” patient data 5) Case study & Lessons learned 6) Conclusions & Future work
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Background: CLIF – Clinical Indicator Formalisation Method
• Formalised indicator = query / queries
• Required: standard terminology for patient data
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8 Steps of CLIF
1) Encode relevant concepts in terms of a terminology
2) Define the information model <= standard 3) Formalise temporal constraints 4) Formalise numeric constraints 5) Formalise Boolean constraints 6) Group constraints by Boolean connectors 7) Formalise in- and exclusion criteria 8) Construct the denominator
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2-‐‑level Methodology: Reference Model and Archetypes
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Diagnosis Archetype
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Procedure Archetype
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Tumour-‐‑Lymph node metastases Archetype
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Datatypes
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Introducing Archetypes
• Computable specifications of clinical concepts. • Constraints (e.g. occurrence, cardinality) &
ontological definitions. • Used to record, exchange and integrate patient
data. • openEHR archetypes: enthusiastic expert
community; publicly available.
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Advantages of Archetypes with respect to Indicators
1) Sharable, defined queries 2) Knowledge-level 3) Reality check
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Sample Quality Indicator
Numerator: Number of patients who had 10 or more lymph nodes examined after resection of a primary colon carcinoma. Denominator: Number of patients who had lymph nodes examined after resection of a primary colon carcinoma. - Exclusion criteria: Previous radiotherapy and recurrent colon carcinomas
Reasons for this indicator: Evidence-‐‑based (correct staging leads to beYer outcome), requires data from several sources
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Modelling Quality Indicators in terms of openEHR Archetypes
1) Terminology <=> information model binding: diagnosis codes <=> node “Diagnosis” of the archetype “Diagnosis” procedure codes <=> node “Procedure” of the archetype “Procedure undertaken”
2) Inter-archetype relations between bound concepts.
=> Bindings and relations are the backbone of indicators (concept-level); used to build queries.
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Sample Query
Patients with “Primary malignant neoplasm of colon”:
SELECT DISTINCT ?patient WHERE { ?patient a patient:at0000.1_Patient . ?patient schemarm:links ?diagnosis . ?diagnosis a diagnosis:at0000.1_Diagnosis . ?diagnosis schemarm:value_element ?diagcode. ?diagcode a diagnosis:at0002.1_Diagnosis . ?diagcode a sct:SCT_93761005 . } ORDER BY ?patient
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Patient Data
DWH Entities Codes Mapped To Patient 1,672,104 Diagnosis 2,925,156 ICD-‐‑9-‐‑CM
(ca. 50%) SNOMED CT (via crossmap)
Operation 144,860 Dutch classification
SNOMED CT (manually, subset)
Admission 259,005 Pathology Reports
92,870 -‐‑ (Dutch free text)
• DSCA dataset: e.g. radiotherapy & number of examined lymph nodes.
• Matched based on based on sex, year of birth, operation, discharge date and procedures => 192/229 patients.
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Mapping between local Data Structure and Archetypes
Table Column Archetype Node Patient Identifier Patient Name Admission Admission Date Patient Admission Admission Date
Discharge Date Discharge Date Diagnosis Code Diagnosis Diagnosis Operation Code Procedure undertaken Procedure DSCA Radiotherapy Procedure undertaken Procedure:
fixed SCT code Multidisciplinary meeting
Procedure undertaken Procedure: fixed SCT code
Pathology Procedure undertaken Procedure: fixed SCT code
Number of exam. lymph nodes
Tumour-‐‑ Lymph node metastases
Number of nodes examined
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Archetypes & Patient Data in OWL 2
• Re-used archetype ontologizer. • Transformed patient data into OWL based on
mapping. • Loaded closure of SNOMED CT, archetypes &
patient data into OWLIM-SE 5.0
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Sample Patient Graph
patient:at0000.1_Patient
procedure:at0000_Procedure_undertaken
procedure:at0002_Procedure
rm:DV_DATE_TIME
diagnosis:at0000.1_Diagnosis
diagnosis:at0002.1_Diagnosis ln_metastases:at0000_Tumour-_Lymph_node_metastases
ln_metastases:at0001_Number_of_nodes_examined
exactly_1
exactly_1
max_1
data:patient132type
data:diagnosis_132_93761005
type
links
ihtsdo:SCT_93761005
data:SCT_93761005
type
value_element
type
data:procedure_132_50774009
type
links
ihtsdo:SCT_50774009
data:SCT_50774009
type
value_element
type
data:procedureTime_132_50774009type
time
2010_05_26T00:00:00hasTime
ihtsdo:SCT_284427004
data:lymphnodeexamination_132
type
links
data:SCT_284427004
typetype
value_element
data:examinationTime_132type
time
2010_05_27T00:00:00
hasTime
data:metastases_132
type
links
links
data:nodeNumber_132type
items
12
hasNumber
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Proof of Concept: Calculating the Indicators
Indicator / Results
Our Result DSCA Publicly Reported
Lymph nodes 85,71% (42/49) 80,00% (43/54) -‐‑ Meeting 91,66% (22/24) 100% (21/21) -‐‑ Re-‐‑operation 1,66% (1/60) 9% (7/75) 8,33% (20/240)
One of the problems (meeting indicator): DSCA: Colon sigmoideum <=> DWH: “Malignant neoplasm of rectosigmoid junction” mapped to both colon and rectum via crossmap…
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Lessons Learned from Case Study
• High coverage of Clinical Knowledge Manager; extending an archetype straightforward
• Intuitive mapping/modelling at knowledge-level • Archetype Ontologizer useful, OWL easy to work
with • Minor difficulties with datatypes; inter-archetype
relationships? • High data quality required for re-use; problem-
oriented patient model • UMLS mapping better
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Conclusions
• Archetypes are suitable to bridge the gap between clinical quality indicators and patient data.
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Future Work
• Effect of data quality on reliability/validity of indicator results
• Sharable queries: Who wants to run these or other indicators on his/her archetyped data?
• New opportunities for automated reasoning at: • patient-data level (infer implicit knowledge; validate data
based on archetypes; data-driven, bottom-up data entry), • archetype-level (infer subsumption and equivalence
relationships between archetypes) and on the • boundary between both: detect semantically equivalent
constructs!
• And: More bindings required => next presentation!