Artificial Intelligence in Medicine
Volume 47, Issue 2 , Pages 87-103 , October 2009

An ontology-based approach to enhance querying capabilities of general practice medicine for better management of hypertension

  • Thusitha Mabotuwana

      Affiliations

    • Department of Computer Science - Tamaki, The University of Auckland, Private Bag 92019, Auckland, New Zealand
    • Corresponding Author InformationCorresponding author. Tel.: +64 9 373 7599x88489; fax: +64 9 303 5932.
  • ,
  • Jim Warren

      Affiliations

    • Department of Computer Science - Tamaki, The University of Auckland, Private Bag 92019, Auckland, New Zealand
    • Section for Epidemiology and Biostatistics, School of Population Health, The University of Auckland, Private Bag 92019, Auckland, New Zealand

Received 31 July 2008 ,Revised 24 March 2009 ,Accepted 16 July 2009.

References 

  1. Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL, et al. Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension. 2003;42(6):1206–1252
  2. Rosamond W, Flegal K, Furie K, Go A, Greenlund K, Haase N, et al. Heart disease and stroke statistics—2008 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation. 2008;117(4):e25–e146
  3. The assessment and management of cardiovascular risk. Wellington: New Zealand Guidelines Group (NZGG); 2003. Available online at: http://www.nzgg.org.nz/guidelines/0035/CVD_Risk_Full.pdf [accessed on September 25, 2008].
  4. Burnier M. Medication adherence and persistence as the cornerstone of effective antihypertensive therapy. Am J Hypertens. 2006;19(11):1190–1196
  5. Andrade SE, Gurwitz JH, Field TS, Kelleher M, Majumdar SR, Reed G, et al. Hypertension management: the care gap between clinical guidelines and clinical practice. Am J Manage Care. 2004;10(7 Pt 2):481–486
  6. Bramlage P, Thoenes M, Kirch W, Lenfant C. Clinical practice and recent recommendations in hypertension management—reporting a gap in a global survey of 1259 primary care physicians in 17 countries. Curr Med Res Opin. 2007;23(4):783–791
  7. Didham R, Martin I, Wood R, Harrison K. Information technology systems in general practice medicine in New Zealand. NZ Med J. 2004;117(1198):U977
  8. Hassey A, Gerrett D, Wilson A. A survey of validity and utility of electronic patient records in a general practice. Br Med J. 2001;322(7299):1401–1405
  9. O’Connor MJ, Shankar RD, Tu SW, Parrish DB, Das AK, Musen MA. Using Semantic Web technologies for knowledge-driven querying of biomedical data. In: 11th conference on Artificial Intelligence in Medicine (AIME2007). Amsterdam, Netherlands: Springer; 2007;
  10. Warren J, Gaikwad R, Mabotuwana T, Kennelly J, Kenealy T. Utilising practice management system data for quality improvement in use of blood pressure lowering medications in general practice. NZ Med J. 2008;121(1285):53–62
  11. Medtech Global Ltd., http://www.medtechglobal.com/ [accessed on March 20, 2009]; 2008.
  12. Chisholm J. The Read clinical classification. Br Med J. 1990;300(6732):1092
  13. Mabotuwana T, Warren JR, Gaikwad R, Kenelly J, Kenealy T. Towards an architecture for quality audit reporting to improve hypertension management. In: Warren JR, Yu P, Yearwood J, Patrick JD, editors. Proceedings of second Australasian workshop on health data and knowledge management (HDKM 2008), vol. 80. Wollongong, NSW, Australia: ACS; 2008. p. 45–54.
  14. International statistical classification of diseases, 10th revision, 2nd ed. Geneva: World Health Organization; 2005.
  15. Stearns MQ, Price C, Spackman KA, Wang AY. SNOMED clinical terms: overview of the development process and project status. In: Proceedings of AMIA annual symposium; 2001. p. 662–6.
  16. Performance Monitoring Framework: data format standard: clinical performance indicators: HealthPAC Version 1.7; 2006 (October).
  17. WONCA International Classification Committee, http://www.globalfamilydoctor.com/WICC/ [accessed on November 19, 2008].
  18. Berners-Lee T, Hendler J, Lassila O. The Semantic Web. Sci Am. 2001;284(5):34–43
  19. LSDIS: ProPreO, http://lsdis.cs.uga.edu/projects/glycomics/propreo/ [accessed on September 15, 2008].
  20. OpenGALEN, http://www.opengalen.org [accessed on November 15, 2008].
  21. Shankar RD, Arkalgud S, O’Connor M, Boyce KS, Parrish DB, Das AK. TrialWiz: an ontology-driven tool for authoring clinical trial protocols. In: Proceedings of AMIA annual symposium; 2008. p. 1226.
  22. Goldstein MK, Hoffman BB, Coleman RW, Musen MA, Tu SW, Advani A, et al. Implementing clinical practice guidelines while taking account of changing evidence: ATHENA DSS, an easily modifiable decision-support system for managing hypertension in primary care. In: Proceedings of AMIA annual symposium; 2000. p. 300–4.
  23. McGuinness DL, van Harmelen F. OWL Web Ontology Language overview, http://www.w3.org/TR/owl-features/ [accessed on May 30, 2008]; 2004.
  24. Horrocks I, Patel-Schneider PF, Boley H, Tabet S, Grosof B, Dean M. SWRL: a Semantic Web Rule Language combining OWL and RuleML, http://www.w3.org/Submission/SWRL/ [accessed on July 25, 2008]; 2004.
  25. Knublauch H, Fergerson RW, Noy NF, Musen MA. The Protégé OWL plugin: an open development environment for Semantic Web applications. In: McIlraith S, Plexousakis D, van Harmelen F, editors. Proceedings of third international Semantic Web conference (ISWC), vol. 3298. Hiroshima, Japan: Springer; 2004. p. 229–243.
  26. Nyulas C, O’Connor M, Tu S. DataMaster—a plug-in for importing schemas and data from relational databases into Protégé. In: 10th international Protégé conference; 2007.
  27. Biron PV, Malhotra A. XML schema part 2: datatypes second edition, http://www.w3.org/TR/xmlschema-2/ [accessed on September 16, 2008]; 2004.
  28. SWRLTemporalBuiltIns, http://protege.cim3.net/cgi-bin/wiki.pl?SWRLTemporalBuiltIns [accessed on September 16, 2008]; 2007.
  29. Hobbs JR, Pan F. Time ontology in OWL, http://www.w3.org/TR/owl-time/ [accessed on September 14, 2008]; 2006.
  30. O’Connor MJ, Shankar RD, Parrish DB, Das AK. Knowledge–data integration for temporal reasoning in a clinical trial system. Int J Med Inform. 2008;2008
  31. ProtegeWiki: SQWRL, http://protege.cim3.net/cgi-bin/wiki.pl?SQWRL [accessed on June 28, 2008].
  32. Jess, the rule engine for the Java platform, http://herzberg.ca.sandia.gov/jess/ [accessed on June 16, 2008].
  33. Milchak JL, Carter BL, Ardery G, Black HR, Bakris GL, Jones DW, et al. Development of explicit criteria to measure adherence to hypertension guidelines. Hum Hypertens. 2006;20(6):426–433
  34. Mitchell E, Sullivan F, Grimshaw JM, Donnan PT, Watt G. Improving management of hypertension in general practice: a randomised controlled trial of feedback derived from electronic patient data. Br J Gen Pract. 2005;55(511):94–101
  35. Poluzzi E, Strahinja P, Vargiu A, Chiabrando G, Silvani MC, Motola D, et al. Initial treatment of hypertension and adherence to therapy in general practice in Italy. Eur J Clin Pharmacol. 2005;61(8):603–609
  36. Seddon ME, Marshall MN, Campbell SM, Roland MO. Systematic review of studies of quality of clinical care in general practice in the UK, Australia and New Zealand. Qual Health Care. 2001;10(3):152–158
  37. Doran T, Fullwood C, Gravelle H, Reeves D, Kontopantelis E, Hiroeh U, et al. Pay-for-performance programs in family practices in the United Kingdom. N Engl J Med. 2006;355(4):375–384
  38. Quality and Outcomes Framework guidance for GMS Contract 2008/09: delivering investment in general practice: British Medical Association; 2008. Available online at: http://www.bma.org.uk/images/QoF%20Guidance%20-%20April%202008_tcm41-182872.pdf [accessed on March 20, 2009].
  39. Campbell S, Reeves D, Kontopantelis E, Middleton E, Sibbald B, Roland M. Quality of primary care in England with the introduction of pay for performance. N Engl J Med. 2007;357(2):181–190
  40. Steel N, Maisey S, Clark A, Fleetcroft R, Howe A. Quality of clinical primary care and targeted incentive payments: an observational study. Br J Gen Pract. 2007;57(539):449–454
  41. O’Dowd A. Outcomes framework needs to be modified to reduce health inequalities. Br Med J. 2008;336:1333
  42. Heath I, Hippisley-Cox J, Smeeth L. Measuring performance and missing the point?. Br Med J. 2007;335(7629):1075–1076
  43. Fleetcroft R, Cookson R. Do the incentive payments in the new NHS contract for primary care reflect likely population health gains?. Health Serv Res Policy. 2006;11(1):27–31
  44. Riddell T, Jackson RT, Wells S, Broad J, Bannink L. Assessing Maori/non-Maori differences in cardiovascular disease risk and risk management in routine primary care practice using Web-based clinical decision support (PREDICT CVD-2). NZ Med J. 2007;120(1250):U2445
  45. Millett C, Gray J, Wall M, Majeed A. Ethnic disparities in coronary heart disease management and pay for performance in the UK. Gen Intern Med. 2008;24:8–13
  46. O’Connor MJ, Tu SW, Musen MA. The Chronus II temporal database mediator. In: Proceedings of AMIA annual symposium; 2002. p. 567–71.
  47. Johnson PD, Tu S, Booth N, Sugden B, Purves IN. Using scenarios in chronic disease management guidelines for primary care. In: Proceedings of AMIA annual symposium; 2000. p. 389–93.
  48. Boaz D, Shahar Y. A framework for distributed mediation of temporal-abstraction queries to clinical databases. Artif Intell Med. 2005;34(1):3–24
  49. Horrocks I, Patel-Schneider PF. A proposal for an OWL rules language. In: Proceedings of thirteenth international World Wide Web conference (WWW). New York: ACM; 2004. p. 723–31.
  50. E-Pharmacy—perspectives and implementation considerations: a report by the NZ Health IT Cluster. Wellington: Ministry of Health; 2006. Available online at: http://www.healthit.org.nz/download/files/E_Pharmacy__Perspectives_and_Implementation_Considerations___Final___June_06.pdf [accessed on September 20, 2008].
  51. Rothe U, Muller G, Schwarz PE, Seifert M, Kunath H, Koch R, et al. Evaluation of a diabetes management system based on practice guidelines, integrated care, and continuous quality management in a Federal State of Germany: a population-based approach to health care research. Diab Care. 2008;31(5):863–868

PII: S0933-3657(09)00096-7

doi: 10.1016/j.artmed.2009.07.001

Artificial Intelligence in Medicine
Volume 47, Issue 2 , Pages 87-103 , October 2009