The Enterprise Imaging Value Proposition

  • Cheryl A. PetersilgeEmail author


As resources in the healthcare environment continue to wane, leaders are seeking ways to continue to provide quality care bounded by the constraints of a reduced budget. This manuscript synthesizes the experience from a number of institutions to provide the healthcare leadership with an understanding of the value of an enterprise imaging program. The value of such a program extends across the entire health system. It leads to operational efficiencies through infrastructure and application consolidation and the creation of focused support capabilities with increased depth of skill. An enterprise imaging program provides a centralized foundation for all phases of image management from every image-producing specialty. Through centralization, standardized image exchange functions can be provided to all image producers. Telehealth services can be more tightly integrated into the electronic medical record. Mobile platforms can be utilized for image viewing and sharing by patients and providers. Mobile tools can also be utilized for image upload directly into the centralized image repository. Governance and data standards are more easily distributed, setting the stage for artificial intelligence and data analytics. Increased exposure to all image producers provides opportunities for cybersecurity optimization and increased awareness.


Enterprise imaging Strategy Digital transformation Value-based care Operational effectiveness Cybersecurity 



The author would like to thank Joe Turk and Emily Labes for their valuable feedback and editorial assistance.

Funding Sources


Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.


  1. 1.
    Top 4 Challenges Healthcare Executives Face in 2019. Managed Healthcare Executive. Available at: Accessed 10 Mar 2019.
  2. 2.
    The top 10 challenges healthcare executives anticipate for 2019. Becker’s ASC Review. Available at: Accessed 10 Mar 2019.
  3. 3.
    Bodenheimer T, Sinsky C: From Triple to Quadruple Aim: Care of the Patient Requires Care of the Provider. Ann Fam Med 12:573–576, 2014PubMedPubMedCentralCrossRefGoogle Scholar
  4. 4.
    Hartman DJ, Pantanowitz L, McHugh JS, Piccoli AL, OLeary MJ, Lauro GR: Enterprise Implementation of Digital Pathology: Feasibility, Challenges, and Opportunities. J Digit Imaging 30:555–560, 2016CrossRefGoogle Scholar
  5. 5.
    Baidoshvili A, Bucur A, van Leeuwen J, van der Laak J, Kluin P, van Diest PJ: Evaluating the benefits of digital pathology implementation: time savings in laboratory logistics. Histopathology 73:784–794, 2018PubMedCrossRefGoogle Scholar
  6. 6.
    Ho J, Ahlers SM, Stratman C, Aridor O, Pantanowitz L, Fine JL, Kuzmishin JA, Montalto MC, Parwani AV: Can Digital Pathology Result In Cost Savings? A Financial Projection For Digital Pathology Implementation At A Large Integrated Health Care Organization. J Pathol Inform 5:33, 2014PubMedPubMedCentralCrossRefGoogle Scholar
  7. 7.
    Roth CJ, Lannum LM, Persons KR: A Foundation for Enterprise Imaging: HIMSS-SIIM Collaborative White Paper. J Digit Imaging 29:530–538, 2016PubMedPubMedCentralCrossRefGoogle Scholar
  8. 8.
    Roth CJ, Lannum LM, Joseph CL: Enterprise Imaging Governance: HIMSS-SIIM Collaborative White Paper. J Digit Imaging 29:539–546, 2016PubMedPubMedCentralCrossRefGoogle Scholar
  9. 9.
    Digital Imaging Adoption Model HIMSS Analytics DIAM. Available at: Accessed 3 Apr 2019.
  10. 10.
    What is an electronic health record (EHR)? Available at: Accessed 10 Sept 2019.
  11. 11.
    Benefits of EHRs. Accessed 10 Sept 2019.
  12. 12.
    Jung HY, Gichoya JW, Vest JR: Providers’ Access of Imaging versus Only Reports: A System Log File Analysis. J Am Coll Radiol 14:217–223, 2017PubMedCrossRefGoogle Scholar
  13. 13.
    Lundstrom CF, Gilmore HL, Ros PR: Cross-disciplinary practices in radiology, pathology and genomics. Radiology 285:12–15, 2017PubMedCrossRefGoogle Scholar
  14. 14.
    Roth CJ, Lannum LM, Dennison DK, Towbin AJ: The Current State and Path Forward for Enterprise Image Viewing: HIMSS-SIIM Collaborative White Paper. J Digit Imaging 29:567–573, 2016PubMedPubMedCentralCrossRefGoogle Scholar
  15. 15.
    Rajbhandari SM, Harris ND, Sutton M, Lockett C, Eaton S, Gadour M, Tesfaye S, Ward JD: Digital Imaging: An Accurate and Easy Method of Measuring Foot Ulcers. Diabet Med 16:339–342, 1999PubMedCrossRefGoogle Scholar
  16. 16.
    Wang SC, Anderson JA, Jones DV, Evans R: Patient perception of wound photography. Int Wound J 13:326–330, 2016PubMedCrossRefGoogle Scholar
  17. 17.
    Coulter A: Patient Engagement – What Works? J Ambul Care Manage 35:80–89, 2012PubMedCrossRefGoogle Scholar
  18. 18.
    The 3 Building Blocks Supporting Patient Engagement Strategies. Patient Engagement HIT. Available at: Accessed 7 Sept 2019.
  19. 19.
    Carlin LE, Smith HE, Henwood F: To see or not to see: a qualitative interview study of patients’ views on their own diagnostic images. BMJ Open 4:e004999, 2014PubMedPubMedCentralCrossRefGoogle Scholar
  20. 20.
    Hiremath A, Awan O, Mendelson D, Siegel EL: Patient perceptions of participating in the RSNA image share project: a preliminary study. J Digit Imaging 29:189–194, 2016PubMedCrossRefGoogle Scholar
  21. 21.
    Building Efficient IT Organizations: Insights from Our Benchmarks. McKinsey Digitial. Available at: Accessed 7 Sept 2019.
  22. 22.
    How to Improve Healthcare Operational Efficiency through Lean Principles and Predictive Analytics. Health IT Outcomes. Available at: Accessed 7 Sept 2019.
  23. 23.
    How to Increase Operational Efficiency in IT. The Wall Street Journal. Available at: Accessed 7 Sept 2019.
  24. 24.
    5 Best Practices for Reducing IT Complexity. Enterprise Systems Journal. Available at:, Accessed 7 Sept 2019.
  25. 25.
    Sirota-Cohen C, Rosipko B, Forsberg D, Sunshine JL: Implementation and Benefits of a Vendor-Neutral Archive and Enterprise-Imaging Management System in an Integrated Delivery Network. J Digit Imaging 32:211–220, 2019PubMedCrossRefGoogle Scholar
  26. 26.
    Kayhart D: Using an Existing DICOM Infrastructure to Enhance the Availability, Quality, and Efficiency of Imaging Throughout the Healthcare Enterprise. J Digit Imaging 32:75–80, 2019PubMedCrossRefGoogle Scholar
  27. 27.
    Missed Opportunities? The Labor Market in Health Informatics, 2014. Available at: Accessed 10 Sept 2019.
  28. 28.
    Future of Work The Global Talent Crunch. Available at: Accessed 10 Sept 2019.
  29. 29.
    The Digital Talent Gap Are Companies Doing Enough? Available at: Accessed 10 Sept 2019.
  30. 30.
    IT Architecture Cutting Costs and Complexity. McKinsey Digital. Available at: Accessed 10 Sept 2019.
  31. 31.
    Towbin AJ, Roth CJ, Bronkalla M, Cram D: Workflow Challenges of Enterprise Imaging: HIMSS-SIIM Collaborative White Paper. J Digit Imaging 29:574–582, 2016PubMedPubMedCentralCrossRefGoogle Scholar
  32. 32.
    Clunie DA, Dennison DK, Cram D, Persons KR, Bronkalla MD, Primo HR: Technical Challenges of Enterprise Imaging: HIMSS-SIIM Collaborative White Paper. J Digit Imaging 29:583–614, 2016PubMedPubMedCentralCrossRefGoogle Scholar
  33. 33.
    Cram D, Roth CJ, Towbin AJ: Orders- Versus Encounters- Based Image Capture: Implications Pre- and Post-Procedure Workflow, Technical and Build Capabilities, Resulting, Analytics and Revenue Capture: HIMSS-SIIM Collaborative White Paper. J Digit Imaging 29:559–566, 2016PubMedPubMedCentralCrossRefGoogle Scholar
  34. 34.
    Scheduled Workflow. Available at: Accessed 12 Sept 2019.
  35. 35.
    Encounter-Based Imaging Workflow. Available at: Accessed 12 Sept 2019.
  36. 36.
    The State of Digital Transformation in Healthcare in 2019. Digital Authority Partners. Available at: .
  37. 37.
    Liew C: The future of radiology augmented with artificial intelligence: a strategy for success. Eur J Radiol 102:152–156, 2018PubMedCrossRefGoogle Scholar
  38. 38.
    Brink JA: Providing Higher Value Care Through Population Health Management: What Is the Radiologist's Role? J Am Coll Radiol 13:759–760, 2016PubMedCrossRefGoogle Scholar
  39. 39.
    Mazzanti M, Shirka E, Gjergo H, Hasimi E: Imaging, Health Record, and Artificial Intelligence: Hype or Hope? Curr Cardiol Rep 20:48, 2018PubMedCrossRefGoogle Scholar
  40. 40.
    Top 6 Digital Transformation Trends In Healthcare For 2019. Forbes. Available at: Accessed 3 Apr 2019.
  41. 41.
    The Role of Healthcare Data Governance in Big Data Analytics. Health IT Analytics. Available at: Accessed 10 Sept 2019.
  42. 42.
    Langlotz CP, Allen B, Erickson BJ, Kalpathy-Cramer J, Bigelow K, Cook TS, Flanders AE, Lungren MP, Mendelson DS, Rudie JD, Wang G, Kandarps K: A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop. Radiology 291:781–791, 2019PubMedPubMedCentralCrossRefGoogle Scholar
  43. 43.
    Brinker TJ, Hekler A, Enk AH, Klode J, Hauschild A, Berking C, Schilling B, Haferkamp S, Schadendorf D, Fröhling S, Utikal JS, von Kalle C, Collaborators: A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task. Eur J Cancer:148–154, 2019Google Scholar
  44. 44.
    Lu W, Tong Y, Yu Y, Xing Y, Chen C, Shen Y: Applications of Artificial Intelligence in Ophthalmology: General Overview. J Opt 19(2018):5278196, 2018Google Scholar
  45. 45.
    Olsen TG, Jackson HB, Feeser TA, Kent MN, Moad JC, Krishnamurthy S, Lunsford DD, Soans RE: Diagnostic Performance of Deep Learning Algorithms Applied to Three Common Diagnoses in Dermatopathology. J Pathol Inform 9:32, 2018PubMedPubMedCentralCrossRefGoogle Scholar
  46. 46.
    Liu Z, Wang S, Dong D, Wei J, Fang C, Zhou X, Sun K, Li L, Li B, Wang M, Tian J: The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges. Theranostics 9:1303–1322, 2019PubMedPubMedCentralCrossRefGoogle Scholar
  47. 47.
    Artificial Intelligence at RSNA18 Shows Signs of Maturity, But a Long Way to Go. Available at: Accessed 12 Sept 2019
  48. 48.
    Growth Opportunities in Precision Medical Imaging, Forecast to 2022. Available at: Accessed 12 Sept 2019
  49. 49.
    Rodriguez-Palmoares JF, Fernandez MAG, Cosials JB: Integrating Multimodal Imaging in Clinical Practice: The Importance of a Multidisciplinary Approach. Rev Esp Cardiol 69:477–479, 2016CrossRefGoogle Scholar
  50. 50.
    Jones S, Cournane S, Sheehy N, Hederman L: A business analytics software tool for monitoring and predicting radiology throughput performance. J Digit Imaging 29:645–653, 2016PubMedPubMedCentralCrossRefGoogle Scholar
  51. 51.
    Rubin DL: Informatics in Radiology: Measuring and Improving Quality in Radiology: Meeting the Challenge with Informatics. Radiographics 31:1511–1527, 2011PubMedCrossRefGoogle Scholar
  52. 52.
    Zwank MD, Gordon BD, Truman SM: Refining the Wild Wild West of Point-of-Care Ultrasound at an Academic Community Hospital. J Am Coll Radiol 14:1574–1577, 2017PubMedCrossRefGoogle Scholar
  53. 53.
    Choudhri AF, Chatterjee AR, Javan R, Radvany MG, Shih G: Security Issues for Mobile Medical Imaging: A Primer. Radiographics 35:1814–1824, 2015PubMedCrossRefGoogle Scholar
  54. 54.
    CMS Code Gives Docs a Chance to Use Store-and-Forward Telehealth. mHealthIntelligence. Available at: Accessed 18 Apr 2019.
  55. 55.
    HCPCS Code G2010. Available at: Accessed 12 Sept 2019.
  56. 56.
    Will Telehealth and mHealth Fulfill Their Potential in 2019? MHealth Intelligence. Available at: Accessed 10 Sept 2019.
  57. 57.
    Kim J, Lee Y, Lim S, Kim JH, Lee B, Lee J-H: What Clinical Information is Value to Doctors using mobile electronic records and when? J Med Internet Res 19:e340, 2017PubMedPubMedCentralCrossRefGoogle Scholar
  58. 58.
    Vetter SY, Schuler S, Hackbusch M, Muller M, Swartman B, Schnetzke M, Grutzner PA, Franke J: Tablets for image review and communication in daily routine of orthopedic surgeons – an evaluation study. J Digit Imaging 31:74–83, 2018PubMedCrossRefGoogle Scholar
  59. 59.
    Vreeland A, Persons KR, Primo HR, Bishop M, Garriott KM, Doyle MK, Silver E, Brown DM, Bashall C: Considerations for Exchanging and Sharing Medical Images for Improved Collaboration and Patient Care: HIMSS-SIIM Collaborative White Paper. J Digit Imaging 29:547–558, 2016PubMedPubMedCentralCrossRefGoogle Scholar
  60. 60.
    Greco G, Patel AS, Lewis SC, Shi W, Rasul R, Torosyan M, Erickson BJ, Hiremath A, Moskowitz AJ, Wm T, Siegel EL, Arenson RL, Mendelson DS: Patient-directed internet-based medical image exchange: experience from an initial multicenter implementation. Acad Radiol 23:237–244, 2016PubMedCrossRefGoogle Scholar
  61. 61.
    Carr CM, Gilman CS, Krywko DM, Moore HE, Walker BJ, Saef SH: Observational study and estimate of cost savings from use of a health information exchange in an academic emergency department. J Emerg Med 46:250–256, 2014PubMedCrossRefGoogle Scholar
  62. 62.
    Vest JR, Jung HY, Ostrovsky A, Das LT, McGinty GB: Image Sharing Technologies and Reduction of Imaging Utilization: A Systematic Review and Meta-analysis. J Am Coll Radiol 12(12 Pt B):1371–79.e3, 2015PubMedPubMedCentralCrossRefGoogle Scholar
  63. 63.
    21st Century Cures Act: Interoperability, Information Blocking, and the ONC Health IT Certification Program. Federal Register. Available at: Accessed 18 Apr 2019.
  64. 64.
    Mendelson DS, Erickson BJ, Choy G: Image sharing: evolving solutions in the age of interoperability. J Am Coll Radiol 11(12 Pt B):1260–1269, 2014PubMedPubMedCentralCrossRefGoogle Scholar
  65. 65.
    Patient medical records sell for $1K on dark web. Becker’s Health IT & CIO report. Available at: Accessed 10 Sept 2019.
  66. 66.
    Williams PA, Woodward AJ: Cybersecurity vulnerabilities in medical devices: a complex environment and multifaceted problem. Med Devices (Auckl) 20(8):305–316, 2015Google Scholar
  67. 67.
    Healthcare’s number one financial issue is cybersecurity. Healthcare Finance. Available at: Accessed 10 Sept 2019.
  68. 68.
    Kruse CS, Frederick B, Jacobson T, Monticone DK: Cybersecurity in healthcare: A systematic review of modern threats and trends. Technol Health Care 25:1–10, 2017PubMedCrossRefGoogle Scholar
  69. 69.
    Moses V, Korah I: Lack of security of networked medical equipment in radiology. AJR Am J Roentgenol 204:343–353, 2015PubMedCrossRefGoogle Scholar
  70. 70.
    5 More Healthcare Providers Fall Victin to Ransomware Attackes. Health IT security. Available at: Accessed 10 Sept 2019.
  71. 71.
    Hospital viruses: Fake cancerous nodes in CT scans, created by malware, trick radiologists. The Washington Post . Available at:[. Accessed 10 Apr 2019.
  72. 72.
    Healthcare Again Tops Industries for Cybersecurity Attacks, Data Breaches. Healthcare Dive. Available at: Accessed 10 Sept 2019.
  73. 73.
    Argaw ST, Bempong NE, Eshaya-Chauvin B, Flahault A: The state of research on cyberattacks against hospitals and available best practice recommendations: a scoping review. BMC Med Decis Inform Mak 19:10, 2019CrossRefGoogle Scholar
  74. 74.
    Top 10 Tips for Cybersecurity in Health Care. Available at: Accessed 10 Sept 2019.
  75. 75.
    Campbell N: Managing to Succeed: Radiology’s Cybersecurity. Landscape 19:8, 2018Google Scholar
  76. 76.
    Yegulalp S. Device loss, not hacking, poses greatest risk to health care data. InfoWorld. Available at Accessed 18 Apr 2019.
  77. 77.
    MD Anderson appeals $4.3M HIPAA penalty. Becker’s Health IT & CIO Report. Available at: .Accessed 19 Apr 2019.

Copyright information

© Society for Imaging Informatics in Medicine 2019

Authors and Affiliations

  1. 1.Vidagos AdvisorsNoveltyUSA

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