Skip to main content

Advertisement

SpringerLink
Log in

Design and initial user experience of a computer-based decision-support tool to improve safety of chemotherapy delivery

  • Short Communication
  • Published:
Health and Technology Aims and scope Submit manuscript

Abstract

Purpose

Treatments for pediatric cancer require complex management schemes. Providers must ensure that dosages integrate on-going patient parameters into a concerted treatment plan. To assist with this challenging task, detailed flowcharts are used as a scaffold, but these can be difficult to interpret, risking treatment errors due to a lack of standardized care.

Methods

To improve patient outcomes, we developed Methotracks, a computer-based decision support tool for the delivery of high-dose methotrexate. Our hypothesis was that Methotracks would improve patient outcomes through concise and specific treatment instructions. To test the efficacy of Methotracks, we developed four virtual patient scenarios. We then compared the number of errors users made in managing each case using the Methotracks to the number of errors made using the standard-of-care flowchart.

Results

Methotracks users made significantly fewer errors than flowchart users, notably at key decision point areas such as hydration management: 80.6% of the Methotracks users treated virtual patients correctly compared to 7.4% of the standard-of-care users. Moreover, users reported that Methotracks was easy to use and that they felt confident when making their treatment decisions.

Conclusions

Our preliminary study indicates that Methotracks is a useful decision-support tool for the management of complex chemotherapy regimens.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Availability of data and materials

De-identified participant data (results from the assessment questions and surveys) are available through the Open Science Framework repository at https://osf.io/2p75m/?view_only=10387b042b1e416f94d44fe2b6db268a.

Design and initial user experience of a computer-based decision-support tool to improve safety of chemotherapy delivery.

References

  1. May J, Carson KR, Butler S, Liu W, Bartlett NL, Wagner-Johnston ND. High incidence of methotrexate associated renal toxicity in patients with lymphoma: a retrospective analysis. Leuk Lymphoma. 2014;55(6):1345–9. https://doi.org/10.3109/10428194.2013.840780.

    Article  Google Scholar 

  2. Foster J. Individualized high dose methotrexate for the treatment of malignancies in children and adolescents with a significant risk for methotrexate toxicities. ClinicalTrials.gov identifier: NCT02076997. Accessed August 20, 2022. https://clinicaltrials.gov/ct2/show/NCT02076997.

  3. Margolis CZ. Uses of clinical algorithms. JAMA. 1983;249(5):627–32. https://doi.org/10.1001/jama.249.5.627.

    Article  Google Scholar 

  4. Morris AH. Developing and implementing computerized protocols for standardization of clinical decisions. Ann Intern Med. 2000;132(5):373–83. https://doi.org/10.7326/0003-4819-132-5-200003070-00007.

    Article  Google Scholar 

  5. Loftus TJ, Tighe PJ, Ozrazgat-Baslanti T, Davis JP, Ruppert MM, Ren Y, Shickel B, Kamaleswaran R, Hogan WR, Moorman JR, Upchurch GR Jr. Ideal algorithms in healthcare: explainable, dynamic, precise, autonomous, fair, and reproducible. PLOS Digit Health. 2022;1(1):e0000006. https://doi.org/10.1371/journal.pdig.0000006.

    Article  Google Scholar 

  6. Morris AH, Stagg B, Lanspa M, Orme J, Clemmer TP, Weaver LK, Thomas F, Grissom CK, Hirshberg E, East TD, Wallace CJ. Enabling a learning healthcare system with automated computer protocols that produce replicable and personalized clinician actions. J Am Med Inform Assoc. 2021;28(6):1330–44. https://doi.org/10.1093/jamia/ocaa294.

    Article  Google Scholar 

  7. Joint Commission. Medical device alarm safety in hospitals. Sentin Event Alert. 2013;50:1–3. PMID: 23767076.

    Google Scholar 

  8. Ehrmann DE, Gallant SN, Nagaraj S, Goodfellow SD, Eytan D, Goldenberg A, Mazwi ML. Evaluating and reducing cognitive load should be a priority for machine learning in healthcare. Nat Med. 2022;28(7):1331. https://doi.org/10.1038/s41591-022-01833-z.

    Article  Google Scholar 

  9. Weir CR, Taber P, Taft T, Reese TJ, Jones B, Del Fiol G. Feeling and thinking: can theories of human motivation explain how EHR design impacts clinician burnout? J Am Med Inform Assoc. 2021;28(5):1042–6. https://doi.org/10.1093/jamia/ocaa270.

    Article  Google Scholar 

  10. Children’s Oncology Group, AALL1732. A Phase 3 Randomized Trial of Inotuzumab Ozogamicin for Newly Diagnosed High-Risk B-ALL; Risk-Adapted Post-Induction Therapy for High-Risk B-ALL, Mixed Phenotype Acute Leukemia, and Disseminated B-LLy. May 2019; Identifier NCT03959085. https://clinicaltrials.gov/ct2/show/NCT03959085.

  11. Morris AH. The importance of protocol-directed patient management for research on lung-protective ventilation. In: Dreyfuss D, Saumon G, Hubgayr R, editors. Ventilator-induced lung injury. Boca Raton, FL: CRC Press; 2006. pp. 571–644.

    Google Scholar 

  12. Berner ES, Houston TK, Ray MN, Allison JJ, Heudebert GR, Chatham WW, Kennedy JI Jr, Glandon GL, Norton PA, Crawford MA, Maisiak RS. Improving ambulatory prescribing safety with a handheld decision support system: a randomized controlled trial. J Am Med Inform Assoc. 2006;13(2):171–9. https://doi.org/10.1197/jamia.M1961.

    Article  Google Scholar 

  13. Morris AH, Orme J Jr, Truwit JD, Steingrub J, Grissom C, Lee KH, Li GL, Thompson BT, Brower R, Tidswell M, Bernard GR. A replicable method for blood glucose control in critically ill patients. Crit Care Med. 2008;36(6):1787–95. https://doi.org/10.1097/CCM.0b013e3181743a5a.

    Article  Google Scholar 

  14. Sintchenko V, Iredell JR, Gilbert GL, Coiera E. Handheld computer-based decision support reduces patient length of stay and antibiotic prescribing in critical care. J Am Med Inform Assoc. 2005;12(4):398–402. https://doi.org/10.1197/jamia.M1798.

    Article  Google Scholar 

  15. Tierney WM, Overhage JM, McDonald CJ. 1996. Computerizing guidelines: factors for success. In Proceedings of the AMIA Annual Fall Symposium American Medical Informatics Association: p. 459.

  16. East TD, Heermann LK, Bradshaw RL, Lugo A, Sailors RM, Ershler L, Wallace CJ, Morris AH, McKinley B, Marquez A, Tonnesen A. 1999. Efficacy of computerized decision support for mechanical ventilation: results of a prospective multi-center randomized trial. In Proceedings of the AMIA Symposium: p. 251.

  17. Thompson BT, Orme JF, Zheng H, Luckett PM, Truwit JD, Willson DF, Hite RD, Brower RG, Bernard GR, Curley MA, Steingrub JS. Multicenter validation of a computer-based clinical decision support tool for glucose control in adult and pediatric intensive care units. J Diabetes Sci Technol. 2008;2(3):357. https://doi.org/10.1177/193229680800200304.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Johns Hopkins University School of Medicine Institute for Excellence in Medical Education 2018 Shark Tank Funding.

Funding

This work was supported by the Institutional Funding.

Author information

Authors and Affiliations

  1. Department of Biology, Quinnipiac University, 275 Mt Carmel Ave, Hamden, CT, 06518, USA

    Caitlin Hanlon

  2. Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA

    Harry Goldberg

  3. Department of Gynecology & Obstetrics, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA

    Angela Liang

  4. Seasons Software, Ashland, Oregon, USA

    Aaron Spjut

  5. Department of Oncology, Johns Hopkins School of Medicine, Baltimore, MD, USA

    Stacy L. Cooper

Authors
  1. Caitlin Hanlon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Harry Goldberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Angela Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Aaron Spjut
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Stacy L. Cooper
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. The design, execution, analysis, and drafting/revision of the manuscript was performed by Caitlin Hanlon. The study design and manuscript revisions were performed by Harry Goldberg. The design, execution, and analysis of the retrospective chart study was performed by Angela Liang. The design, coding, and execution of the Methotracks application was performed by Aaron Spjut. The design, execution, analysis, and revision of the manuscript was performed by Stacy Cooper. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Caitlin Hanlon.

Ethics declarations

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

Ethics approval and consent to participate

The authors have no conflicts of interest. No work was conducted on patients for this study. This study was approved by the Johns Hopkins University Institutional Review Board, IRB00203619 and was performed in line with the principles of the Declaration of Helsinki. All data was de-identified prior to analysis. Participants signed informed consent documents prior to participation in the study. This document indicated that participants consented to their de-identified data being included in a publication.

Consent for publication

Participants signed informed consent documents prior to participation in the study. This document indicated that participants consented to their de-identified data being included in a publication.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hanlon, C., Goldberg, H., Liang, A. et al. Design and initial user experience of a computer-based decision-support tool to improve safety of chemotherapy delivery. Health Technol. 13, 659–663 (2023). https://doi.org/10.1007/s12553-023-00758-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12553-023-00758-y

Keywords

Search

Navigation