Joint Design with Providers of Clinical Decision Support for Value-Based Advanced Shoulder ImagingFunding This work was supported by Merit Pilot Project (Award # PPO 15–178) from the U.S. Department of Veterans Affairs Health Services Research and Development Service. The contents do not represent the views of the U.S. Department of Veterans Affairs or the United States Government.
28 October 2019
23 December 2019
19 February 2020 (online)
Background Provider orders for inappropriate advanced imaging, while rarely altering patient management, contribute enough to the strain on available health care resources, and therefore the United States Congress established the Appropriate Use Criteria Program.
Objectives To examine whether co-designing clinical decision support (CDS) with referring providers will reduce barriers to adoption and facilitate more appropriate shoulder ultrasound (US) over magnetic resonance imaging (MRI) in diagnosing Veteran shoulder pain, given similar efficacies and only 5% MRI follow-up rate after shoulder US.
Methods We used a theory-driven, convergent parallel mixed-methods approach to prospectively (1) determine medical providers' reasons for selecting MRI over US in diagnosing shoulder pain and identify barriers to ordering US, (2) co-design CDS, informed by provider interviews, to prompt appropriate US use, and (3) assess CDS impact on shoulder imaging use. CDS effectiveness in guiding appropriate shoulder imaging was evaluated through monthly monitoring of ordering data at our quaternary care Veterans Hospital. Key outcome measures were appropriate MRI/US use rates and transition to ordering US by both musculoskeletal specialist and generalist providers. We assessed differences in ordering using a generalized estimating equations logistic regression model. We compared continuous measures using mixed effects analysis of variance with log-transformed data.
Results During December 2016 to March 2018, 569 (395 MRI, 174 US) shoulder advanced imaging examinations were ordered by 111 providers. CDS “co-designed” in collaboration with providers increased US from 17% (58/335) to 50% (116/234) of all orders (p < 0.001), with concomitant decrease in MRI. Ordering appropriateness more than doubled from 31% (105/335) to 67% (157/234) following CDS (p < 0.001). Interviews confirmed that generalist providers want help in appropriately ordering advanced imaging.
Conclusion Partnering with medical providers to co-design CDS reduced barriers and prompted appropriate transition to US from MRI for shoulder pain diagnosis, promoting evidence-based practice. This approach can inform the development and implementation of other forms of CDS.
Protection of Human and Animal Subjects
The study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects and was approved by the University of Wisconsin Institutional Review Board.
- 1 Goldzweig CL, Orshansky G, Paige NM. , et al. Electronic health record-based interventions for improving appropriate diagnostic imaging: a systematic review and meta-analysis. Ann Intern Med 2015; 162 (08) 557-565
- 2 Chou R, Fu R, Carrino JA, Deyo RA. Imaging strategies for low-back pain: systematic review and meta-analysis. Lancet 2009; 373 (9662): 463-472
- 3 Office of the Assistant Deputy Under Secretary for Health for Policy and Planning. Washington, DC: VHA National Data Page FY 2018. Available at: https://vaww.va.gov/VHAOPP/enroll01/VitalSignsNational/enrolvsnat18.asp . Accessed July 27, 2018
- 4 United States Bone and Joint Initiative. The Burden of Musculoskeletal Diseases in the United States. Available at: http://www.boneandjointburden.org . Accessed July 27, 2018
- 5 Sheehan SE, Coburn JA, Singh H. , et al. Reducing unnecessary shoulder MRI examinations within a capitated health care system: a potential role for shoulder ultrasound. J Am Coll Radiol 2016; 13 (07) 780-787
- 6 Tashjian RZ. Epidemiology, natural history, and indications for treatment of rotator cuff tears. Clin Sports Med 2012; 31 (04) 589-604
- 7 Lenza M, Buchbinder R, Takwoingi Y, Johnston RV, Hanchard NC, Faloppa F. Magnetic resonance imaging, magnetic resonance arthrography and ultrasonography for assessing rotator cuff tears in people with shoulder pain for whom surgery is being considered. Cochrane Database Syst Rev 2013; 24 (09) CD009020
- 8 Teefey SA, Rubin DA, Middleton WD, Hildebolt CF, Leibold RA, Yamaguchi K. Detection and quantification of rotator cuff tears. Comparison of ultrasonographic, magnetic resonance imaging, and arthroscopic findings in seventy-one consecutive cases. J Bone Joint Surg Am 2004; 86 (04) 708-716
- 9 Rutten MJ, Spaargaren GJ, van Loon T, de Waal Malefijt MC, Kiemeney LA, Jager GJ. Detection of rotator cuff tears: the value of MRI following ultrasound. Eur Radiol 2010; 20 (02) 450-457
- 10 Centers for Medicare & Medicaid Services. Medicare Physician Fee Schedule. Available at: https://www.cms.gov/apps/physician-fee-schedule/ . Accessed June 21, 2019
- 11 Roshanov PS, You JJ, Dhaliwal J. , et al; CCDSS Systematic Review Team. Can computerized clinical decision support systems improve practitioners' diagnostic test ordering behavior? A decision-maker-researcher partnership systematic review. Implement Sci 2011; 6: 88
- 12 Centers for Medicare & Medicaid Services. Appropriate Use Criteria Program. Available at: https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Appropriate-Use-Criteria-Program/index.html . Accessed July 7, 2019
- 13 Georgiou A, Prgomet M, Markewycz A, Adams E, Westbrook JI. The impact of computerized provider order entry systems on medical-imaging services: a systematic review. J Am Med Inform Assoc 2011; 18 (03) 335-340
- 14 Ip IK, Lacson R, Hentel K. , et al. JOURNAL CLUB: predictors of provider response to clinical decision support: lessons learned from the medicare imaging demonstration. Am J Roentgenol 2017; 208 (02) 351-357
- 15 Huber TC, Krishnaraj A, Patrie J, Gaskin CM. Impact of a commercially available clinical decision support program on provider ordering habits. J Am Coll Radiol 2018; 15 (07) 951-957
- 16 Saleem JJ, Patterson ES, Militello L, Render ML, Orshansky G, Asch SM. Exploring barriers and facilitators to the use of computerized clinical reminders. J Am Med Inform Assoc 2005; 12 (04) 438-447
- 17 Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ 2005; 330 (7494): 765
- 18 Goehler A, Moore C, Manne-Goehler JM. , et al. Clinical decision support for ordering CTA-PE studies in the emergency department-a pilot on feasibility and clinical impact in a tertiary medical center. Acad Radiol 2019; 26 (08) 1077-1083
- 19 Sim I, Gorman P, Greenes RA. , et al. Clinical decision support systems for the practice of evidence-based medicine. J Am Med Inform Assoc 2001; 8 (06) 527-534
- 20 Miller K, Mosby D, Capan M. , et al. Interface, information, interaction: a narrative review of design and functional requirements for clinical decision support. J Am Med Inform Assoc 2018; 25 (05) 585-592
- 21 Bettmann MA, Oikarinen H, Rehani M. , et al. Clinical imaging guidelines part 4: challenges in identifying, engaging and collaborating with stakeholders. J Am Coll Radiol 2015; 12 (04) 370-375
- 22 Geeting GK, Beck M, Bruno MA. , et al. Mandatory assignment of modified wells score before CT angiography for pulmonary embolism fails to improve utilization or percentage of positive cases. AJR Am J Roentgenol 2016; 207 (02) 442-449
- 23 Bates DW, Kuperman GJ, Wang S. , et al. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J Am Med Inform Assoc 2003; 10 (06) 523-530
- 24 Kilsdonk E, Peute LW, Jaspers MWM. Factors influencing implementation success of guideline-based clinical decision support systems: a systematic review and gaps analysis. Int J Med Inform 2017; 98: 56-64
- 25 Sittig DF, Ash JS. On the importance of using a multidimensional sociotechnical model to study health information technology. Ann Fam Med 2011; 9 (05) 390-391
- 26 Sittig DF, Singh H. A new sociotechnical model for studying health information technology in complex adaptive healthcare systems. Qual Saf Health Care 2010; 19 (Suppl. 03) i68-i74
- 27 Freeman R, Khanna S, Ricketts D. Inappropriate requests for magnetic resonance scans of the shoulder. Int Orthop 2013; 37 (11) 2181-2184
- 28 Creswell JW. Research Design: Qualitative, Quantitative and Mixed Methods Approaches. 4th ed. Thousand Oaks, CA: Sage Publications; 2014: 220-223
- 29 Delbecq A, Van de Ven A, Gustafson D. Group Techniques for Program Planning: A Guide to Nominal Group and Delphi Processes. 1st ed. Glenview, IL: Scott, Foresman and Company; 1975: 83-107
- 30 Brunner J, Chuang E, Goldzweig C, Cain CL, Sugar C, Yano EM. User-centered design to improve clinical decision support in primary care. Int J Med Inform 2017; 104: 56-64
- 31 Palin TE, Sharpe RE, Shetterly SM, Steiner JF. Randomized clinical trial of a clinical decision support tool for improving the appropriateness scores for ordering imaging studies in primary and specialty care ambulatory Clinics. Am J Roentgonol 2019; 213: 1-6
- 32 Lumsden G, Lucas-Garner K, Sutherland S, Dodenhoff R. Physiotherapists utilizing diagnostic ultrasound in shoulder clinics. How useful do patients find immediate feedback from the scan as part of the management of their problem?. Musculoskelet Care 2018; 16 (01) 209-213
- 33 Ojha HA, Wyrsta NJ, Davenport TE, Egan WE, Gellhorn AC. Timing of physical therapy initiation for nonsurgical management of musculoskeletal disorders and effects on patient outcomes: a systematic review. J Orthop Sports Phys Ther 2016; 46 (02) 56-70
- 34 Yoshida E, Fei S, Bavuso K, Lagor C, Maviglia S. The value of monitoring clinical decision support interventions. Appl Clin Inform 2018; 9 (01) 163-173
- 35 Wright A, Ai A, Ash J. , et al. Clinical decision support alert malfunctions: analysis and empirically derived taxonomy. J Am Med Inform Assoc 2018; 25 (05) 496-506
- 36 Jones W, Drake C, Mack D, Reeder B, Trautner B, Wald H. Developing mobile clinical decision support for nursing home staff assessment of urinary tract infection using goal-directed design. Appl Clin Inform 2017; 8 (02) 632-650