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Automated attention flags in chronic disease care planning

Warren, James, Noone, Joseph, Smith, Brian, Ruffin, Richard, Frith, Peter, van der Zwaag, Berend, Beliakov, Gleb, Frankel, Heath and McElroy, Heather 2001, Automated attention flags in chronic disease care planning, Medical journal of Australia, vol. 175, no. 6, pp. 308-312.

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Title Automated attention flags in chronic disease care planning
Author(s) Warren, James
Noone, Joseph
Smith, Brian
Ruffin, Richard
Frith, Peter
van der Zwaag, Berend
Beliakov, Gleb
Frankel, Heath
McElroy, Heather
Journal name Medical journal of Australia
Volume number 175
Issue number 6
Start page 308
End page 312
Publisher Australasian Medical Pub. Co
Place of publication Sydney, N.S.W.
Publication date 2001-09-17
ISSN 0025-729X
Summary Objectives: To assess the value of computerised decision support in the management of chronic respiratory disease by comparing agreement between three respiratory specialists, general practitioners (care coordinators), and decision support software.
Methods: Care guidelines for two chronic obstructive pulmonary disease projects of the SA HealthPlus Coordinated Care Trial were formulated. Decision support software, Care Plan On-Line (CPOL), was created to represent the intent of these guidelines via automated attention flags to appear in patients' electronic medical records. For a random sample of 20 patients with care plans, decisions about the use of nine additional services (eg,.smoking cessation, pneumococcal vaccination) were compared between the respiratory specialists, the patients' GPs and the CPOL attention flags.
Results: Agreement among the specialists was at the lower end of moderate (intraclass correlation coefficient [ICC], 0.48; 95% CI, 0.39-0.56), with a 20% rate of contradictory decisions. Agreement with recommendations of specialists was moderate to poor for GPs (le, 0.49; 95% CI, 0.33-0.66) and moderate to good for CPOL (K, 0.72; 95% CI, 0.55-0.90). CPOL agreement with GPs was moderate to poor (le, 0.41; 95% CI, 0.24-0.58). GPs were less likely than specialists or CPOL to decide in favour of an additional service (P< 0.001). CPOL was 87% accurate as an indicator of specialist decisions. It gave a 16% false-positive rate according to specialist decisions, and flagged 61% of decisions where GPs said No and specialists said Yes.
Conclusions: Automated decision support may provide GPs with improved access to the intent of guidelines; however, further investigation is required.
Notes Reproduced with the specific permission of the copyright owner.
Language eng
Field of Research 080605 Decision Support and Group Support Systems
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2001, Australasian Medical Pub. Co
Persistent URL http://hdl.handle.net/10536/DRO/DU:30008424

Document type: Journal Article
Collections: School of Information Technology
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