1  /  25

Semantic  Integration  of  Patient  Data  and  Quality  

Indicators  based  on  openEHR  Archetypes Kathrin Dentler, Annette ten Teije, Ronald

Cornet and Nicolette de Keizer

2  /  25

meaning-based integration required => archetypes!

Patient  Data  

valuable,  but  semantic  gaps

3  /  25

Quality  Indicators •  Should be

well-formalised: executable, sharable & comparable results

•  CLIF •  Research

question: archetypes?

4  /  25

Outline

1)  CLIF 2)  Archetypes 3)  Formalisation of indicator 4)  “Archetyped” patient data 5)  Case study & Lessons learned 6)  Conclusions & Future work

5  /  25

Background:  CLIF  –  Clinical  Indicator  Formalisation  Method

•  Formalised indicator = query / queries

•  Required: standard terminology for patient data

6  /  25

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

7  /  25

2-­‐‑level  Methodology:  Reference  Model  and  Archetypes

8  /  25

Diagnosis  Archetype

9  /  25

Procedure  Archetype

10  /  25

Tumour-­‐‑Lymph  node  metastases  Archetype

11  /  25

Datatypes

12  /  25

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.

13  /  25

Advantages  of  Archetypes    with  respect  to  Indicators

1)  Sharable, defined queries 2)  Knowledge-level 3)  Reality check

14  /  25

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

15  /  25

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.

16  /  25

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

17  /  25

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.  

18  /  25

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  

19  /  25

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

20  /  25

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

21  /  25

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…  

22  /  25

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

23  /  25

Conclusions

•  Archetypes are suitable to bridge the gap between clinical quality indicators and patient data.

24  /  25

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!

25  /  25

Questions?

k.dentler@vu.nl  -­‐‑  hYp://www.few.vu.nl/  ̃kdr250/archetypes/