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First published October 26, 2006 as JAMIA PrePrint; doi:10.1197/jamia.M2169
Journal of the American Medical Informatics Association 2007;14(1):100-109
© 2007 American Medical Informatics Association

A more recent version of this article appeared on January 1, 2007
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Submitted on June 2, 2006
Accepted on October 13, 2006

Developing High-Specificity Anti-hypertensive Alerts by Therapeutic State Analysis of Electronic Prescribing Records

Svetla Gadzhanova1*, Ivan I. Iankov1, James R. Warren PhD2, Jan Stanek MD1, Gary M. Misan PhD3, Zak Baiug MBBS3, and Lorenzo Ponte MBBS3

Affiliation of the authors: 1 Advanced Computing Research Centre, University of South Australia, Adelaide, South Australia, Australia ; 2 Department of Computer Science, University of Auckland, Auckland, New Zealand ; 3 Spencer Gulf Rural Health School, University of Adelaide/University of South Australia, Whyalla Norrie, South Australia, Australia

* To whom correspondence should be addressed.

Objective This paper presents a model for analysis of chronic disease prescribing action over time in terms of transitions in status of therapy as indicated in electronic prescribing records. The quality of alerts derived from these therapeutic state transitions is assessed in the context of antihypertensive prescribing.

Design A set of alert criteria is developed based on analysis of state-transition in past antihypertensive prescribing of a rural Australian General Practice. Thirty active patients coded as hypertensive with alerts on six months of previously un-reviewed prescribing, and 30 hypertensive patients without alerts, are randomly sampled and independently reviewed by the practice's two main General Practice physicians (GPs), each GP reviewing 20 alert and 20 non-alert cases (providing 10 alert and 10 non-alert cases for agreement assessment).

Measurements GPs provide blind assessment of quality of hypertension management and retrospective assessment of alert relevance.

Results Alerts were found on 66 of 611 cases with coded hypertension with 37 alerts on the 30 sampled alert cases. GPs assessed alerting sensitivity as 74% (CI 52% - 89%) and specificity as 61% (CI 45% - 74%) for the sample, which is estimated as 26% sensitivity and 93% specificity for the antihypertensive population. Agreement between the GPs on assessment of alert relevance was fair (kappa = 0.37).

Conclusions Data-driven development of alerts from electronic prescribing records using analysis of therapeutic state transition shows promise for derivation of high-specificity alerts to improve the quality of chronic disease management activities.






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