The impact of computerized provider order entry systems on medical-imaging services: a systematic review
- 1Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia
- 2Discipline of Medical Radiation Sciences, Faculty of Health Sciences, The University of Sydney, Lidcombe, New South Wales, Australia
- Correspondence to Dr Andrew Georgiou, Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Faculty of Medicine, University of New South Wales, Sydney NSW 2052, Australia;
- Revised 21 December 2010
- Accepted 8 February 2011
- Published Online First 8 March 2011
Background Computerized provider order entry (CPOE) systems have been strongly promoted as a means to improve the quality and efficiency of healthcare.
Methods This systematic review aimed to assess the evidence of the impact of CPOE on medical-imaging services and patient outcomes.
Results Fourteen studies met the inclusion criteria, most of which (10/14) used a pre-/postintervention comparison design. Eight studies demonstrated benefits, such as decreased test utilization, associated with decision-support systems promoting adherence to test ordering guidelines. Three studies evaluating medical-imaging ordering and reporting times showed statistically significant decreases in turnaround times.
Conclusions The findings reveal the potential for CPOE to contribute to significant efficiency and effectiveness gains in imaging services. The diversity and scope of the research evidence can be strengthened through increased attention to the circumstances and mechanisms that contribute to the success (or otherwise) of CPOE and its contribution to the enhancement of patient care delivery.
- Qualitative/ethnographic field study
- statistical analysis of large datasets
- measuring/improving outcomes in specific conditions and patient subgroups
- measuring/improving patient safety and reducing medical errors
- improving healthcare workflow and process efficiency
- machine learning
Funding This study was funded as part of an Australian Research Council Research Linkage grant (LP0989144).
Competing interests None.
Provenance and peer review Not commissioned; externally peer reviewed.