PMID- 25011429
OWN - NLM
STAT- MEDLINE
DCOM- 20150512
LR  - 20191210
IS  - 1872-8243 (Electronic)
IS  - 1386-5056 (Linking)
VI  - 83
IP  - 10
DP  - 2014 Oct
TI  - Validating an ontology-based algorithm to identify patients with type 2 diabetes 
      mellitus in electronic health records.
PG  - 768-78
LID - S1386-5056(14)00103-8 [pii]
LID - 10.1016/j.ijmedinf.2014.06.002 [doi]
AB  - BACKGROUND: Improving healthcare for people with chronic conditions requires 
      clinical information systems that support integrated care and information 
      exchange, emphasizing a semantic approach to support multiple and disparate 
      Electronic Health Records (EHRs). Using a literature review, the Australian 
      National Guidelines for Type 2 Diabetes Mellitus (T2DM), SNOMED-CT-AU and input 
      from health professionals, we developed a Diabetes Mellitus Ontology (DMO) to 
      diagnose and manage patients with diabetes. This paper describes the manual 
      validation of the DMO-based approach using real world EHR data from a general 
      practice (n=908 active patients) participating in the electronic Practice Based 
      Research Network (ePBRN). METHOD: The DMO-based algorithm to query, using 
      Semantic Protocol and RDF Query Language (SPARQL), the structured fields in the 
      ePBRN data repository were iteratively tested and refined. The accuracy of the 
      final DMO-based algorithm was validated with a manual audit of the general 
      practice EHR. Contingency tables were prepared and Sensitivity and Specificity 
      (accuracy) of the algorithm to diagnose T2DM measured, using the T2DM cases found 
      by manual EHR audit as the gold standard. Accuracy was determined with three 
      attributes - reason for visit (RFV), medication (Rx) and pathology (path) - 
      singly and in combination. RESULTS: The Sensitivity and Specificity of the 
      algorithm were 100% and 99.88% with RFV; 96.55% and 98.97% with Rx; and 15.6% and 
      98.92% with Path. This suggests that Rx and Path data were not as complete or 
      correct as the RFV for this general practice, which kept its RFV information 
      complete and current for diabetes. However, the completeness is good enough for 
      this purpose as confirmed by the very small relative deterioration of the 
      accuracy (Sensitivity and Specificity of 97.67% and 99.18%) when calculated for 
      the combination of RFV, Rx and Path. The manual EHR audit suggested that the 
      accuracy of the algorithm was influenced by data quality such as incorrect data 
      due to mistaken units of measurement and unavailable data due to 
      non-documentation or documented in the wrong place or progress notes, problems 
      with data extraction, encryption and data management errors. CONCLUSION: This 
      DMO-based algorithm is sufficiently accurate to support a semantic approach, 
      using the RFV, Rx and Path to define patients with T2DM from EHR data. However, 
      the accuracy can be compromised by incomplete or incorrect data. The extent of 
      compromise requires further study, using ontology-based and other approaches.
CI  - Copyright (c) 2014 Elsevier Ireland Ltd. All rights reserved.
FAU - Rahimi, Alireza
AU  - Rahimi A
AD  - UNSW, School of Public Health & Community Medicine, Sydney, Australia; Isfahan 
      University of Medical Sciences, Health Information Research Centre, Isfahan, 
      Iran; UNSW, Asia-Pacific Ubiquitous Healthcare Research Centre, Sydney, 
      Australia.
FAU - Liaw, Siaw-Teng
AU  - Liaw ST
AD  - UNSW, School of Public Health & Community Medicine, Sydney, Australia; UNSW, 
      Centre for Primary Health Care & Equity, Sydney, Australia; General Practice 
      Unit, South Western Sydney Local Health District. Electronic address: 
      siaw@unsw.edu.au.
FAU - Taggart, Jane
AU  - Taggart J
AD  - UNSW, Centre for Primary Health Care & Equity, Sydney, Australia.
FAU - Ray, Pradeep
AU  - Ray P
AD  - UNSW, Asia-Pacific Ubiquitous Healthcare Research Centre, Sydney, Australia.
FAU - Yu, Hairong
AU  - Yu H
AD  - UNSW, Centre for Primary Health Care & Equity, Sydney, Australia.
LA  - eng
PT  - Journal Article
PT  - Validation Study
DEP - 20140620
PL  - Ireland
TA  - Int J Med Inform
JT  - International journal of medical informatics
JID - 9711057
SB  - IM
MH  - *Algorithms
MH  - Diabetes Mellitus, Type 2/*diagnosis
MH  - *Electronic Health Records
MH  - Humans
OTO - NOTNLM
OT  - Diabetes Mellitus
OT  - Electronic Health Records
OT  - Ontology
OT  - SPARQL
OT  - Type 2
OT  - Validation studies
EDAT- 2014/07/12 06:00
MHDA- 2015/05/13 06:00
CRDT- 2014/07/12 06:00
PHST- 2014/02/12 00:00 [received]
PHST- 2014/06/02 00:00 [revised]
PHST- 2014/06/02 00:00 [accepted]
PHST- 2014/07/12 06:00 [entrez]
PHST- 2014/07/12 06:00 [pubmed]
PHST- 2015/05/13 06:00 [medline]
AID - S1386-5056(14)00103-8 [pii]
AID - 10.1016/j.ijmedinf.2014.06.002 [doi]
PST - ppublish
SO  - Int J Med Inform. 2014 Oct;83(10):768-78. doi: 10.1016/j.ijmedinf.2014.06.002. 
      Epub 2014 Jun 20.