David P Peiris1, MBBS, MIPH, FRACGP;
Rohina Joshi1, MBBS, MPH, PhD;
Ruth J Webster1, BMedSc, MBBS, MIPH;
Patrick Groenestein1, MBBS, PhD, FRACP;
Tim P Usherwood2, MD, FRACGP, FRCP;
Emma Heeley1, BSc, MSc, PhD;
Fiona M Turnbull1, MBChB, FAFPHM, PhD;
Alexandra Lipman1, BAppSc(Phty), MIPH;
Anushka A Patel, MBBS, PhD, FRACP
1The George Institute for International Health, University of Sydney, Sydney, Australia
2Sydney Medical School-Western, University of Sydney, Sydney, Australia
Corresponding Author:
David P Peiris, MBBS, MIPH, FRACGP
The George Institute for International Health
University of Sydney
PO Box M201 Missenden Rd
Sydney
Australia
Phone: +61 2 99934500
Fax: +61 2 99934502
Email: dpeiris [at] george.org.au
ABSTRACT
Background: Challenges remain in translating the well-established
evidence for management of cardiovascular disease (CVD) risk into
clinical practice. Although electronic clinical decision support (CDS)
systems are known to improve practitioner performance, their
development in Australian primary health care settings is limited.
Objectives: Study aims were to (1) develop a valid CDS tool that assists
Australian general practitioners (GPs) in global CVD risk management,
and (2) preliminarily evaluate its acceptability to GPs as a point-of-care
resource for both general and underserved populations.
Methods: CVD risk estimation (based on Framingham algorithms) and
risk-based management advice (using recommendations from six
Australian guidelines) were programmed into a software package. Tool
validation: Data from 137 patients attending a physician’s clinic were
analyzed to compare the tool’s risk scores with those obtained from an
independently programmed algorithm in a separate statistics package.
The tool’s management advice was compared with a physician’s
recommendations based on a manual review of the guidelines. Field test:
The tool was then tested with 21 GPs from eight general practices and
three Aboriginal Medical Services. Customized CDS-based
recommendations were generated for 200 routinely attending patients
(33% Aboriginal) using information extracted from the health record by a
research assistant. GPs reviewed these recommendations during each
consultation. Changes in CVD risk factor measurement and
management were recorded. In-depth interviews with GPs were conducted.
Results: Validation testing: The tool’s risk assessment algorithm
correlated very highly with the independently programmed version in the
separate statistics package (intraclass correlation coefficient 0.999).
For management advice, there were only two cases of disagreement
between the tool and the physician. Field test: GPs found 77% (153/200)
of patient outputs easy to understand and agreed with screening and
prescribing recommendations in 72% and 64% of outputs, respectively;
26% of patients had their CVD risk factor history updated; 73% had at least
one CVD risk factor measured or tests ordered. For people assessed at
high CVD risk (n = 82), 10% and 9%, respectively, had lipid-lowering and
BP-lowering medications commenced or dose adjustments made, while
7% newly commenced anti-platelet medications. Three key qualitative
findings emerged: (1) GPs found the tool enabled a systematic approach
to care; (2) the tool greatly influenced CVD risk communication; (3)
successful implementation into routine care would require integration with
practice software, minimal data entry, regular revision with updated
guidelines, and a self-auditing feature. There were no substantive
differences in study findings for Aboriginal Medical Services GPs, and the
tool was generally considered appropriate for use with Aboriginal patients.
Conclusion: A fully-integrated, self-populating, and potentially Internet-
based CDS tool could contribute to improved global CVD risk management
in Australian primary health care. The findings from this study will inform a
large-scale trial intervention.
J Med Internet Res 2009;11(4):e51
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