Advertisement

Clinical Decision Support Tools for Order Entry

  • Laila Cochon
  • Ramin Khorasani
Chapter
Part of the Medical Radiology book series (MEDRAD)

Abstract

Medical imaging has helped to transform healthcare and will continue to advance the understanding and treatment of disease. Despite the substantial benefits of medical imaging, there is wide variation in the use of imaging (especially high-cost imaging) and concern about it’s inappropriate use persists. Inappropriate use may result in suboptimal quality of care and wasteand may harm patients by exposure to unnecessary ionizing radiation, the risks of over-diagnosis and over-treatment, including unnecessary additional tests and treatments provided in follow-up of incidental or ambiguous imaging findings.

Clinical decision support tools for order entry provide an opportunity to embed evidence/ clinical best practices in the workflow of providers requesting imaging examinations to reduce inappropriate use of imaging. In this chapter, we define clinical decision support for order entry, review trends in imaging use and describe general features of effective clinical decision support including experience from large-scale implementations. We conclude by reviewing some of the emerging challenges and opportunities for imaging clinical decision support and future directions.

Abbreviations

AUC

Appropriate use criteria

CDS

Clinical decision support

CPOE

Computerized physician order entry system

EHR

Electronic health record

IT

Information technology

References

  1. Alper EA, Ip IK, Silveira PC, Piazza G, Goldhaber SZ, Benson CB, Lacson R, Khorasani R (2017) Risk stratification model: lower extremity ultrasonography for hospitalized patients suspected of deep vein thrombosis. J Gen Intern Med 1–5Google Scholar
  2. Bates DW, Kuperman GJ, Wang S, Gandhi T, Kittler A, Volk L et al (2003) Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J Am Med Inform Assoc 10(6):523–530CrossRefPubMedGoogle Scholar
  3. Black WC (1998) Advances in radiology and the real versus apparent effects of early diagnosis. Eur J Radiol 27(2):116–122CrossRefPubMedGoogle Scholar
  4. Blackmore CC, Mecklenburg RS, Kaplan GS (2011) Effectiveness of clinical decision support in controlling inappropriate imaging. J Am Coll Radiol 8(1):19–25CrossRefPubMedGoogle Scholar
  5. Blumenthal D, Tavenner M (2010) The “meaningful use” regulation for electronic health records. N Engl J Med 363(6):501–504CrossRefPubMedGoogle Scholar
  6. CEBM (2009) Oxford Centre for Evidence-based Medicine - Levels of Evidence (March 2009) [Internet]. [cited 2017 Aug 29]. http://www.cebm.net/oxford-centre-evidence-based-medicine-levels-evidence-march-2009/
  7. Dunne RM, Ip IK, Abbett S, Gershanik EF, Raja AS, Hunsaker A et al (2015) Effect of evidence-based clinical decision support on the use and yield of CT pulmonary angiographic imaging in hospitalized patients. Radiology 276(1):167–174CrossRefPubMedGoogle Scholar
  8. Grade Definitions—US Preventive Services Task Force [Internet] (2012) [cited 2017 Jun 19]. https://www.uspreventiveservicestaskforce.org/Page/Name/grade-definitions
  9. Gupta A, Ip IK, Raja AS, Andruchow JE, Sodickson A, Khorasani R (2014) Effect of clinical decision support on documented guideline adherence for head CT in emergency department patients with mild traumatic brain injury. J Am Med Inform Assoc 21(e2):e347–e351CrossRefPubMedGoogle Scholar
  10. Harpole LH, Khorasani R, Fiskio J, Kuperman GJ, Bates DW (1997) Automated evidence-based critiquing of orders for abdominal radiographs: impact on utilization and appropriateness. J Am Med Inform Assoc 4(6):511–521CrossRefPubMedGoogle Scholar
  11. Harvey L (2012) Medical Imaging: Is the Growth Boom Over? [Internet]. Neiman Health Policy Institute; [cited 2017 Jun 19]. (Neiman Report). Report No.: 1. https://www.acr.org/~/media/ACR/Documents/PDF/Research/Brief-01/PolicyBriefHPI092012.pdf
  12. Health Affairs (2017) Physician Burnout Is A Public Health Crisis: A Message To Our Fellow Health Care CEOs [Internet]. [cited 2017 May 14]. http://healthaffairs.org/blog/2017/03/28/physician-burnout-is-a-public-health-crisis-a-message-to-our-fellow-health-care-ceos/
  13. Health Information Technology for Economic and Clinical Health (HITECH) Act (2009) Public Law 111–5 Feb, 2009Google Scholar
  14. Hendee WR, Becker GJ, Borgstede JP, Bosma J, Casarella WJ, Erickson BA et al (2010) Addressing overutilization in medical imaging. Radiology 257(1):240–245CrossRefPubMedGoogle Scholar
  15. Hentel K, Menard A, Khorasani R (2017) New CMS clinical decision support regulations: a potential opportunity with major challenges. Radiology 283(1):10–13CrossRefPubMedGoogle Scholar
  16. Hussey PS, Timbie JW, Burgette LF, Wenger NS, Nyweide DJ, Kahn KL (2015) Appropriateness of advanced diagnostic imaging ordering before and after implementation of clinical decision support systems. JAMA 313(21):2181–2182CrossRefPubMedGoogle Scholar
  17. Institute of Medicine (US) Committee on Standards for Developing Trustworthy Clinical Practice Guidelines (2011) Graham R, Mancher M, Miller Wolman D, Greenfield S, Steinberg E (eds) Clinical Practice Guidelines We Can Trust [Internet]. Washington, DC: National Academies Press (US); [cited 2017 Jun 19]. http://www.ncbi.nlm.nih.gov/books/NBK209539/
  18. Ip IK, Schneider LI, Hanson R, Marchello D, Hultman P, Viera M et al (2012) Adoption and meaningful use of computerized physician order entry with an integrated clinical decision support system for radiology: ten-year analysis in an urban teaching hospital. J Am Coll Radiol 9(2):129–136CrossRefPubMedGoogle Scholar
  19. Ip IK, Schneider L, Seltzer S, Smith A, Dudley J, Menard A et al (2013) Impact of provider-led, technology-enabled radiology management program on imaging. Am J Med 126(8):687–692CrossRefPubMedGoogle Scholar
  20. Ip IK, Gershanik EF, Schneider LI, Raja AS, Mar W, Seltzer S et al (2014) Impact of IT-enabled intervention on MRI use for back pain. Am J Med 127(6):512–518.e1CrossRefPubMedGoogle Scholar
  21. Ip IK, Raja AS, Seltzer SE, Gawande AA, Joynt KE, Khorasani R (2015a) Use of public data to target variation in providers’ use of CT and MR imaging among Medicare beneficiaries. Radiology 275(3):718–724CrossRefPubMedGoogle Scholar
  22. Ip IK, Raja AS, Gupta A, Andruchow J, Sodickson A, Khorasani R (2015b) Impact of clinical decision support on head computed tomography use in patients with mild traumatic brain injury in the ED. Am J Emerg Med 33(3):320–325CrossRefPubMedGoogle Scholar
  23. Ip IK, Lacson R, Hentel K, Malhotra S, Darer J, Langlotz C et al (2017) Predictors of provider response to clinical decision support: lessons learned from the Medicare imaging demonstration. AJR Am J Roentgenol 208(2):351–357CrossRefPubMedGoogle Scholar
  24. Jha AK (2010) Meaningful use of electronic health records: the road ahead. JAMA 304(15):1709–1710CrossRefPubMedGoogle Scholar
  25. Jolesz FA, Blumenfeld SM (1994) Interventional use of magnetic resonance imaging. Magn Reson Q 10(2):85–96PubMedGoogle Scholar
  26. Khorasani R (2001) Computerized physician order entry and decision support: improving the quality of care. Radiographics 21(4):1015–1018CrossRefPubMedGoogle Scholar
  27. Khorasani R, Hentel K, Darer J, Langlotz C, Ip IK, Manaker S et al (2014) Ten commandments for effective clinical decision support for imaging: enabling evidence-based practice to improve quality and reduce waste. Am J Roentgenol 203(5):945–951CrossRefGoogle Scholar
  28. Lacson R, Raja AS, Osterbur D, Ip I, Schneider L, Bain P et al (2016) Assessing strength of evidence of appropriate use criteria for diagnostic imaging examinations. J Am Med Inform Assoc 23(3):649–653CrossRefPubMedGoogle Scholar
  29. Lin E (2010) Radiation risk from medical imaging. Mayo Clin Proc 85(12):1142–1146CrossRefPubMedGoogle Scholar
  30. Medicare C for, Baltimore MS 7500 SB, Usa M (2013) 2008–10-30(2) [Internet]. [cited 2017 Aug 28]. https://www.cms.gov/Newsroom/MediaReleaseDatabase/Fact-sheets/2008-Fact-sheets-items/2008-10-302.html
  31. Medicare Imaging Demonstration Evaluation Report to Congress [Internet] (2014) [cited 2017 Jun 19]. https://innovation.cms.gov/Files/reports/MedicareImagingDemoRTC.pdf
  32. Medicare Payment Advisory Commission (2016) Report to the Congress: Medicare Payment Policy [Internet]. [cited 2017 Jun 19]. march-2016-report-to-the-congress-medicare-payment-policy.pdf
  33. Medicare the USC for, Boulevard MS 7500 S, Baltimore, Baltimore M 21244 7500 SB, Usa M 21244 (2017) Medicare Imaging Demonstration | Center for Medicare & Medicaid Innovation [Internet]. [cited 2017 Jun 19]. https://innovation.cms.gov/initiatives/Medicare-Imaging/
  34. O’Connor SD, Sodickson AD, Ip IK, Raja AS, Healey MJ, Schneider LI et al (2014) Journal club: requiring clinical justification to override repeat imaging decision support: impact on CT use. AJR Am J Roentgenol 203(5):W482–W490CrossRefPubMedGoogle Scholar
  35. OCEBM Levels of Evidence—CEBM [Internet] (2017) [cited 2017 Jun 19]. http://www.cebm.net/ocebm-levels-of-evidence/
  36. Protecting Access to Medicare Act of 2014 (2014) Public Law 113-93 Apr 1, 2014 p. Congressional Record Vol 160Google Scholar
  37. Raja AS, Wright C, Sodickson AD, Zane RD, Schiff GD, Hanson R et al (2010) Negative appendectomy rate in the era of CT: an 18-year perspective. Radiology 256(2):460–465CrossRefPubMedGoogle Scholar
  38. Raja AS, Ip IK, Prevedello LM, Sodickson AD, Farkas C, Zane RD et al (2012) Effect of computerized clinical decision support on the use and yield of CT pulmonary angiography in the emergency department. Radiology 262(2):468–474CrossRefPubMedGoogle Scholar
  39. Raja AS, Ip IK, Sodickson AD, Walls RM, Seltzer SE, Kosowsky JM et al (2014a) Radiology utilization in the emergency department: trends of the past 2 decades. AJR Am J Roentgenol 203(2):355–360CrossRefPubMedGoogle Scholar
  40. Raja AS, Gupta A, Ip IK, Mills AM, Khorasani R (2014b) The use of decision support to measure documented adherence to a national imaging quality measure. Acad Radiol 21(3):378–383CrossRefPubMedGoogle Scholar
  41. Raja AS, Ip IK, Dunne RM, Schuur JD, Mills AM, Khorasani R (2015) Effects of performance feedback reports on adherence to evidence-based guidelines in use of CT for evaluation of pulmonary embolism in the emergency department: a randomized trial. AJR Am J Roentgenol 205(5):936–940CrossRefPubMedGoogle Scholar
  42. Ransohoff DF, Pignone M, Sox HC (2013) How to decide whether a clinical practice guideline is trustworthy. JAMA 309(2):139–140CrossRefPubMedGoogle Scholar
  43. Shinagare AB, Ip IK, Abbett SK, Hanson R, Seltzer SE, Khorasani R (2014) Inpatient imaging utilization: trends of the past decade. AJR Am J Roentgenol 202(3):W277–W283CrossRefPubMedGoogle Scholar
  44. Sistrom CL, Dang PA, Weilburg JB, Dreyer KJ, Rosenthal DI, Thrall JH (2009) Effect of computerized order entry with integrated decision support on the growth of outpatient procedure volumes: seven-year time series analysis. Radiology 251(1):147–155CrossRefPubMedGoogle Scholar
  45. Smith-Bindman R, Lipson J, Marcus R, Kim K-P, Mahesh M, Gould R et al (2009) Radiation dose associated with common computed tomography examinations and the associated lifetime attributable risk of cancer. Arch Intern Med 169(22):2078–2086CrossRefPubMedGoogle Scholar
  46. Smith-Bindman R, Miglioretti DL, Johnson E, Lee C, Feigelson HS, Flynn M et al (2012) Use of diagnostic imaging studies and associated radiation exposure for patients enrolled in large integrated health care systems, 1996-2010. JAMA 307(22):2400–2409CrossRefPubMedGoogle Scholar
  47. Sodickson A, Baeyens PF, Andriole KP, Prevedello LM, Nawfel RD, Hanson R et al (2009) Recurrent CT, cumulative radiation exposure, and associated radiation-induced cancer risks from CT of adults. Radiology 251(1):175–184CrossRefPubMedGoogle Scholar
  48. Stiell IG, Greenberg GH, McKnight RD, Nair RC, McDowell I, Worthington JR (1992) A study to develop clinical decision rules for the use of radiography in acute ankle injuries. Ann Emerg Med 21(4):384–390CrossRefPubMedGoogle Scholar
  49. Tempany CMC (2001) Advances in biomedical imaging. JAMA 285(5):562–567CrossRefPubMedGoogle Scholar
  50. Vartanians VM, Sistrom CL, Weilburg JB, Rosenthal DI, Thrall JH (2010) Increasing the appropriateness of outpatient imaging: effects of a barrier to ordering low-yield examinations. Radiology 255(3):842–849CrossRefPubMedGoogle Scholar
  51. Wasser EJ, Prevedello LM, Sodickson A, Mar W, Khorasani R (2013) Impact of a real-time computerized duplicate alert system on the utilization of computed tomography. JAMA Intern Med 173(11):1024–1026CrossRefPubMedGoogle Scholar
  52. Weilburg JB, Sistrom CL, Rosenthal DI, Stout MB, Dreyer KJ, Rockett HR et al (2017) Utilization management of HIGH-COST IMAGING in an outpatient setting in a large stable patient and provider cohort over 7 years. Radiology 284(3):766–776CrossRefPubMedGoogle Scholar
  53. Weissleder R (1999) Molecular imaging: exploring the next frontier. Radiology 212(3):609–614CrossRefPubMedGoogle Scholar
  54. Welch HG, Schwartz L, Woloshin S (2011) Overdiagnosed: making people sick in the pursuit of health. Beacon Press, Boston, MA, p 228Google Scholar
  55. Wells PS, Anderson DR, Rodger M, Stiell I, Dreyer JF, Barnes D et al (2001) Excluding pulmonary embolism at the bedside without diagnostic imaging: management of patients with suspected pulmonary embolism presenting to the emergency department by using a simple clinical model and d-dimer. Ann Intern Med 135(2):98–107CrossRefGoogle Scholar
  56. Yan Z, Lacson R, Ip I, Valtchinov V, Raja A, Osterbur D et al (2016) Evaluating terminologies to enable imaging-related decision rule sharing. AMIA Annu Symp Proc 2016:2082–2089PubMedGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of RadiologyCenter for Evidence-Based Imaging, Brigham and Women’s Hospital, Harvard Medical SchoolBostonUSA

Personalised recommendations