Operational Profiles in Software-Reliability Engineering
IEEE Software
Software Testing and Quality Assurance
Software Testing and Quality Assurance
Artificial Intelligence in Medicine
Towards an architecture for quality audit reporting to improve hypertension management
HDKM '08 Proceedings of the second Australasian workshop on Health data and knowledge management - Volume 80
A Semantic Web Technology Based Approach to Identify Hypertensive Patients for Follow-Up/Recall
CBMS '08 Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems
Journal of Biomedical Informatics
A framework for distributed mediation of temporal-abstraction queries to clinical databases
Artificial Intelligence in Medicine
Software Testing: A Craftsman's Approach
Software Testing: A Craftsman's Approach
Visualizing logical dependencies in SWRL rule bases
RuleML'10 Proceedings of the 2010 international conference on Semantic web rules
The ChronoMedIt temporal medical audit framework: progress and agenda
HIKM '11 Proceedings of the Fourth Australasian Workshop on Health Informatics and Knowledge Management - Volume 120
HIKM '13 Proceedings of the Sixth Australasian Workshop on Health Informatics and Knowledge Management - Volume 142
| Hi-index | 0.00 |
Background: Quality audit and feedback to general practice is an important aspect of successful chronic disease management. However, due to the complex temporal relationships associated with the nature of chronic illness, formulating clinically relevant queries within the context of a specific evaluation period is difficult. Methods: We abstracted requirements from a set of previously developed criteria to develop a generic criteria model that can be used to formulate queries related to chronic condition management. We implemented and verified the framework, ChronoMedIt, to execute clinical queries within the scope of the criteria model. Results: Our criteria model consists of four broad classes of audit criteria - lapse in indicated therapy, no measurement recording, time to achieve target and measurement contraindicating therapy. Using these criteria classes as a guide, ChronoMedIt has been implemented as an extensible framework. ChronoMedIt can produce criteria reports and has an integrated prescription and measurement timeline visualisation tool. We illustrate the use of the framework by identifying patients on suboptimal therapy based on a range of pre-determined audit criteria using production electronic medical record data from two general medical practices for 607 and 679 patients with hypertension. As the most prominent result, we find that 59% (out of 607) and 34% (out of 679) of patients with hypertension had at least one episode of 30day lapse in their antihypertensive therapy over a 12-month evaluation period. Conclusions: ChronoMedIt can reliably execute a wide range of clinically useful queries to identify patients whose chronic condition management can be improved.