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Appl Clin Inform 2020; 11(01): 142-152
DOI: 10.1055/s-0040-1701256
Research Article
Georg Thieme Verlag KG Stuttgart · New York

Joint Design with Providers of Clinical Decision Support for Value-Based Advanced Shoulder Imaging

Michael C. Brunner
1   Department of Radiology, William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin, United States
2   Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, United States
,
Scott E. Sheehan
1   Department of Radiology, William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin, United States
2   Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, United States
,
Eric M. Yanke
3   Department of Medicine, William S. Middleton Memorial Veteran Hospital, Madison, Wisconsin, United States
,
Dean F. Sittig
4   Department of Biomedical Informatics, University of Texas Health Science Center, Houston, Texas, United States
,
Nasia Safdar
3   Department of Medicine, William S. Middleton Memorial Veteran Hospital, Madison, Wisconsin, United States
5   Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, United States
,
Barbara Hill
6   Population Health Sciences, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, United States
,
Kenneth S. Lee
2   Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, United States
,
John F. Orwin
7   Department of Orthopedics and Rehabilitation, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, United States
,
David J. Vanness
8   Department of Health Policy and Administration, Pennsylvania State University, University Park, Pennsylvania, United States
,
Christopher J. Hildebrand
3   Department of Medicine, William S. Middleton Memorial Veteran Hospital, Madison, Wisconsin, United States
5   Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, United States
,
Michael A. Bruno
9   Department of Radiology, The Penn State Milton S. Hershey Medical Center and Penn State College of Medicine, Hershey, Pennsylvania, United States
,
Timothy J. Erickson
10   Department of Physical Medicine and Rehabilitation, William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin, United States
,
Ryan Zea
11   Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, United States
,
D. Paul Moberg
6   Population Health Sciences, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, United States
› Author Affiliations Funding 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.
Further Information

Publication History

28 October 2019

23 December 2019

Publication Date:
19 February 2020 (online)

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Abstract

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.

Keywords

clinical decision support - medical informatics - diagnostics - informatics

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.


Supplementary Material

 

  • References

  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    PubMedGoogle Scholar
  • 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
    PubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 6 Tashjian RZ. Epidemiology, natural history, and indications for treatment of rotator cuff tears. Clin Sports Med 2012; 31 (04) 589-604
    CrossrefPubMedGoogle Scholar
  • 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
    PubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 10 Centers for Medicare & Medicaid Services. Medicare Physician Fee Schedule. Available at: https://www.cms.gov/apps/physician-fee-schedule/ . Accessed June 21, 2019
    PubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    PubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 27 Freeman R, Khanna S, Ricketts D. Inappropriate requests for magnetic resonance scans of the shoulder. Int Orthop 2013; 37 (11) 2181-2184
    CrossrefPubMedGoogle Scholar
  • 28 Creswell JW. Research Design: Qualitative, Quantitative and Mixed Methods Approaches. 4th ed. Thousand Oaks, CA: Sage Publications; 2014: 220-223
    Google Scholar
  • 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
    Google Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    PubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    Article in Thieme ConnectPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    Article in Thieme ConnectPubMedGoogle Scholar