Radiology Order Decision Support: Examination-Indication Appropriateness Assessed Using 2 Electronic Systems |
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| Institution: | 1. Imaging Institute, Cleveland Clinic Foundation, Cleveland, Ohio;2. Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, Ohio;1. Rush University, Chicago, Illinois;2. Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Missouri;3. Department of Radiology, University of Chicago, Chicago, Illinois;4. US Department of Veterans Affairs, Washington, District of Columbia;5. Department of Radiology, Mount Sinai School of Medicine, New York, New York;6. Department of Radiology, University of Michigan Hospitals, Ann Arbor, Michigan;7. Department of Radiology, David Geffen School of Medicine at UCLA, Los Angeles, California;8. Elucid Bioimaging Inc, Wenham, Massachusetts;9. Department of Radiology, Duke University, Durham, North Carolina;1. Houston VA Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas;2. Department of Family and Community Medicine, Baylor College of Medicine, Houston, Texas, USA;3. Section of Health Services Research, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA;4. Division of Radiology Informatics, Department of Radiology, University of Chicago Medical Center, Chicago, IL;1. Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts;2. Harvard Medical School, Boston, Massachusetts;3. Institute for Technology Assessment, Massachusetts General Hospital, Boston, Massachusetts;4. Harvard School of Public Health, Cambridge, Massachusetts;1. Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California;1. University of Utah, Salt Lake City, Utah;2. Cleveland Clinic, Cleveland, Ohio;3. Northwestern University, Chicago, Illinois;4. Rhode Island Hospital, Providence, Rhode Island;5. Intermountain Urological Institute, Murray, Utah;6. Albert Einstein College of Medicine, Bronx, New York;7. Emory University Hospital, Atlanta, Georgia;8. University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania;9. Scottsdale Medical Imaging, Scottsdale, Arizona;10. Duke University Medical Center, Durham, North Carolina;11. Universty of California Los Angeles Medical Center, Los Angeles, California;12. Brigham & Women’s Hospital, Boston, Massachusetts;13. University of Texas MD Anderson Cancer Center, Houston, Texas;14. Oakland University William Beaumont School of Medicine, Troy, Michigan |
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| Abstract: | PurposeThe goal of the study was to determine the effects of guideline implementation strategy using 2 commercial radiology clinical decision support (CDS) systems.MethodsThe appropriateness and insurance dispositions of MRI and CT orders were evaluated using the Medicalis SmartReq and Nuance RadPort CDS systems during 2 different 3-month periods. Logistic regression was used to compare these outcomes between the 2 systems, after adjusting for patient-mix differences.ResultsApproximately 2,000 consecutive outpatient MRI and CT orders were evaluated over 2 periods of 3 months each. Medicalis scored 60% of exams as “indeterminate” (insufficient information) or “not validated” (no guidelines). Excluding these cases, Nuance scored significantly more exams as appropriate than did Medicalis (80% versus 51%, P < .001) and predicted insurance outcome significantly more often (76% versus 58%, P < .001). Only when the Medicalis “indeterminate” and “not validated” categories were combined with the high- or moderate-utility categories did the 2 CDS systems have similar performance. Overall, 19% of examinations with low-utility ratings were reimbursed. Conversely, 0.8% of examinations with high- or moderate-utility ratings were denied reimbursement.ConclusionsThe chief difference between the 2 CDS systems, and the strongest influence on outcomes, was how exams without relevant guidelines or with insufficient information were handled. Nuance augmented published guidelines with clinical best practice; Medicalis requested additional information utilizing pop-up windows. Thus, guideline implementation choices contributed to decision making and outcomes. User interface, specifically, the number of screens and completeness of indication choices, controlled CDS interactions and, coupled with guidance implementation, influenced willingness to use the CDS system. |
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| Keywords: | CPOE utilization management decision support exam appropriateness |
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