A Pilot of Data-Driven Modeling to Assess Potential for Improved Efficiency in an Academic Breast-Imaging Center

Abstract

Patient satisfaction and department efficiency are central pillars in defining quality in medicine. Patient satisfaction is often linked to wait times. We describe a novel method to study workflow and simulate solutions to improve efficiency, thereby decreasing wait times and adding value. We implemented a real-time location system (RTLS) in our academic breast-imaging department to study workflow, including measuring patient wait time, quantifying equipment utilization, and identifying bottlenecks. Then, using discrete event simulation (DES), we modeled solutions with changes in staffing and equipment. Nine hundred and ninety-nine patient encounters were tracked over a 10-week period. The RTLS system recorded 551,512 raw staff and patient time stamps, which were analyzed to produce 17,042 staff and/or patient encounter time stamps. Mean patient wait time was 27 min. The digital breast tomosynthesis (DBT) unit had the highest utilization rate and was identified as a bottleneck. DES predicts a 19.2% reduction in patient length of stay with replacement of a full field digital mammogram (FFDM) unit by a DBT unit and the addition of technologists. Through integration of RTLS with discrete event simulation testing, we created a model based on real-time data to accurately assess patient wait times and patient progress through an appointment, evaluate patient staff-interaction, identify system bottlenecks, and quantitate potential solutions. This quality improvement initiative has important implications, potentially allowing data-driven decisions for staff hiring, equipment purchases, and department layout.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. 1.

    Salazar G, Quencer K, Aran S, Abujudeh H: Patient satisfaction in radiology: qualitative analysis of written complaints generated over a 10-year period in an academic medical center. J Am Coll Radiol 10(7):513–517, 2013

    Article  PubMed  Google Scholar 

  2. 2.

    Bleustein C, Rothschild DB, Valen A, Valatis E, Schweitzer L, Jones R: Wait times, patient satisfaction scores, and the perception of care. Am J Manag Care 20(5):393–400, 2014

    PubMed  Google Scholar 

  3. 3.

    Porter ME: What is value in health care? N Engl J Med 363(26):2477–2481, 2010

    Article  CAS  Google Scholar 

  4. 4.

    Kamel Boulos MN, Berry G: Real-time locating systems (RTLS) in healthcare: a condensed primer. International journal of health geographics 11:25, 2012

    Article  PubMed  PubMed Central  Google Scholar 

  5. 5.

    [No authors listed]: Real-time tracking data drive process improvements, even while ED volumes continue to climb. ED Manag. 24 (6): 67–70, 2012

  6. 6.

    Cobbley B: Easing patient flow. How an RTLS solution can help ES efficiency. Health Facil Manag 24(12):43–44 7, 201

  7. 7.

    Kehoe B: Tracking IV pumps in real time. Mater Manag Health Care 16(7):20–24, 2007

    Google Scholar 

  8. 8.

    Johnson JE, Smith AL, Mastro KA: From Toyota to the bedside: nurses can lead the lean way in health care reform. Nurs Adm Q 36(3):234–242, 2012

    Article  PubMed  Google Scholar 

  9. 9.

    Melanson SE, Goonan EM, Lobo MM, Baum JM, Paredes JD, Santos KS, Gustafson ML, Tanasijevic MJ: Applying Lean/Toyota production system principles to improve phlebotomy patient satisfaction and workflow. Am J Clin Pathol 132(6):914–919, 2009

    Article  PubMed  Google Scholar 

  10. 10.

    Cai H, Jia J: Using discrete event simulation (DES) to support performance-driven healthcare design. HERD, 2018. https://doi.org/10.1177/1937586718801910

  11. 11.

    F. Zafari, A. Gkelias, K. Leung A survey of indoor localization systems and technologies, [online] Available: https://arxiv.org/abs/1709.01015. Accessed 4 Sept 2017

  12. 12.

    M.C. Albrecht PE. Introduction to discrete event simulation [Available from: http://www.albrechts.com/mike/DES/Introduction to DES.pdf. Accessed Jan 2010

Download references

Acknowledgements

We would like to thank Charlene Tomaselli and the Johns Hopkins (Radiology) Information Technology team.

Sources of Support

This research project was sponsored in part by St. Onge Company, a supply chain engineering company. The RTLS hardware was a donation provided by CenTrak for research purposes only.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Susan C. Harvey.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Amir, T., Lee, B., Woods, R.W. et al. A Pilot of Data-Driven Modeling to Assess Potential for Improved Efficiency in an Academic Breast-Imaging Center. J Digit Imaging 32, 221–227 (2019). https://doi.org/10.1007/s10278-018-0159-7

Download citation

Keywords

  • Patient experience
  • Breast imaging
  • Value
  • Efficiency
  • Real-time location system