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Cited by 3Electronic Health Records SystemsArtificial Intelligence in Healthcare and EducationClinical Reasoning and Diagnostic Skills

Mixed methods evaluation of a clinical decision support system to reduce variation in healthcare

Open access
Pak, Anton, Robertson, Samantha T., Nguyen, Bao Hoang, Hodgson, Tobias, Sullivan, Clair, Burton-Jones, Andrew·2025·npj Digital Medicine
DOI 10.1038/s41746-025-02141-9PMC PMC12739111
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Abstract

Evidence that clinical decision support systems (CDSS) reduce unwarranted variation in care and improve patient outcomes remains limited, particularly for acute, high-risk conditions where deviations from clinical guidelines are costly. We evaluated utilisation of a CDSS-embedded care plan within an electronic medical record designed to support timely, protocol-driven management of a complex metabolic emergency. Among 345 episodes, the CDSS was used in just over half of cases; yet a 1% increase in utilisation was associated with a reduction in hospital length of stay by approximately 1.3%, and any use was linked to a 3.3% reduction in in-hospital mortality. Fourteen clinician interviews supported these findings but highlighted barriers such as limited awareness and lack of training that constrain meaningful use. Quantitative and qualitative evidence together suggest that well-designed CDSS care plans can reduce variation and improve outcomes, while emphasising the need for awareness, education, and workflow alignment to unlock greater value.

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  • AI systems have improved medication safety through decision-support tools.

    “improved medication safety through decision-support tools 6,13 .”

    From AI-induced never-skilling in medical education (2026) · cites this paper as [13]

    The Discussion reports that decision-support tools can improve DKA care, including more guideline-concordant dosing, and the Abstract links use of this paper’s CDSS to better clinical outcomes. But the paper does not establish that its system is AI or report a measured improvement in medication safety. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 30

  1. [1]Colicchio, T. K., Cimino, J. J. & Del Fiol, G. Unintended consequences of nationwide electronic health record adoption: challenges and opportunities in the post-meaningful use era. J. Med. Internet Res. 21, e13313 (2019). 31162125 PMC6682280 · DOI
  2. [2]Lin, Y.-K., Lin, M. & Chen, H. Do electronic health records affect quality of care? Evidence from the HITECH Act. Inf. Syst. Res. 30, 306–318 (2019). · DOI · Paper page
  3. [3]Wani, D. & Malhotra, M. Does the meaningful use of electronic health records improve patient outcomes?. J. Oper. Manag. 60, 1–18 (2018). · DOI
  4. [4]Brailer, D. J. Guiding the health information technology agenda. Health Aff. 29, 586–595 (2010). 20368586 · DOI
  5. [5]Hodgson, T., Burton-Jones, A., Donovan, R. & Sullivan, C. The role of electronic medical records in reducing unwarranted clinical variation in acute health care: systematic review. JMIR Med. Inform. 9, e30432 (2021). 34787585 PMC8663492 · DOI
  6. [6]Hodgson, T., Burton-Jones, A. & Sullivan, C. Addressing unwarranted clinical variation in healthcare as a quality improvement process. In Proc. 55th Hawaii International Conference on System Sciences 3885–3891 (IEEE, 2022).
  7. [7]Naicker, S. et al. Patterns and perceptions of standard order set use among physicians working within a multihospital system: mixed methods study. JMIR Form. Res. 8, e54022 (2024). 39514274 PMC11584534 · DOI
  8. [8]Sutherland, K. & Levesque, J. F. Unwarranted clinical variation in health care: definitions and proposal of an analytic framework. J. Eval. Clin. Pract. 26, 687–696 (2020). 31136047 PMC7317701 · DOI
  9. [9]Ayabakan, S., Bardhan, I., Zheng, Z. & Kirksey, K. The impact of health information sharing on duplicate testing. MIS Q. 41, 1083–1104 (2017). · DOI
  10. [10]Harrison, R. et al. Addressing unwarranted clinical variation: a rapid review of current evidence. J. Eval. Clin. Pract. 25, 53–65 (2019). 29766616 · DOI
  11. [11]Wennberg, J. E. Unwarranted variations in healthcare delivery: implications for academic medical centres. BMJ 325, 961–964 (2002). 12399352 PMC1124450 · DOI
  12. [12]Runnacles, J., Roueché, A. & Lachman, P. The right care, every time: improving adherence to evidence-based guidelines. Arch. Dis. Child. Educ. Pract. 103, 27–33 (2018). 28536137 · DOI
  13. [13]Park, Y., Bang, Y. & Kwon, J. Clinical decision support system and hospital readmission reduction: evidence from US panel data. Decis. Support Syst. 159, 113816 (2022). · DOI
  14. [14]Eden, R., Burton-Jones, A., Scott, I., Staib, A. & Sullivan, C. Effects of eHealth on hospital practice: synthesis of the current literature. Aust. Health Rev. 42, 568–578 (2018). 29986809 · DOI
  15. [15]Sahota, N. et al. Computerized clinical decision support systems for acute care management: a decision-maker-researcher partnership systematic review of effects on process of care and patient outcomes. Implement. Sci. 6, 1–14 (2011). 21824385 PMC3169487 · DOI
  16. [16]Eden, R. et al. Unpacking the complexity of consistency: insights from a grounded theory study of the effective use of electronic medical records. In Proc. 51st Hawaii International Conference on System Sciences (IEEE, 2018).
  17. [17]Liberati, E. G. et al. What hinders the uptake of computerized decision support systems in hospitals? A qualitative study and framework for implementation. Implement. Sci. 12, 1–13 (2017). 28915822 PMC5602839 · DOI · Paper page
  18. [18]Sutton, R. T. et al. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit. Med. 3, 17 (2020). 32047862 PMC7005290 · DOI · Paper page
  19. [19]Echouffo-Tcheugui, J. B. & Garg, R. Management of hyperglycemia and diabetes in the emergency department. Curr. Diab. Rep. 17, 1–8 (2017). 28646357 · DOI
  20. [20]Nyenwe, E. A. & Kitabchi, A. E. Evidence-based management of hyperglycemic emergencies in diabetes mellitus. Diab. Res. Clin. Pract. 94, 340–351 (2011). 21978840 · DOI
  21. [21]Sly, B., Russell, A. W. & Sullivan, C. Digital interventions to improve safety and quality of inpatient diabetes management: a systematic review. Int. J. Med. Inform. 157, 104596 (2022). 34785487 · DOI · Paper page
  22. [22]Pichardo-Lowden, A. R. et al. Clinical decision support for glycemic management reduces hospital length of stay. Diab. Care 45, 2526–2534 (2022). PMC9679255 36084251 · DOI
  23. [23]Blair, A. M. J., Hamilton, B. K. & Spurlock, A. Evaluating an order set for improvement of quality outcomes in diabetic ketoacidosis. Adv. Emerg. Nurs. J. 40, 59–72 (2018). 29384776 · DOI
  24. [24]Flood, K. et al. Implementation and evaluation of a diabetic ketoacidosis order set in pediatric type 1 diabetes at a tertiary care hospital: a quality-improvement initiative. Can. J. Diab. 43, 297–303 (2019). 30777707
  25. [25]Singh, R., Perros, P. & Frier, B. Hospital management of diabetic ketoacidosis: are clinical guidelines implemented effectively?. Diabet. Med. 14, 482–486 (1997). 9212315 · DOI
  26. [26]Sola, E. et al. Management of diabetic ketoacidosis in a teaching hospital. Acta Diabetol. 43, 127–130 (2006). 17211563 · DOI
  27. [28]Prgomet, M., Li, L., Niazkhani, Z., Georgiou, A. & Westbrook, J. I. Impact of commercial computerized provider order entry (CPOE) and clinical decision support systems (CDSSs) on medication errors, length of stay, and mortality in intensive care units: a systematic review and meta-analysis. J. Am. Med. Inform. Assoc. 24, 413–422 (2017). 28395016 PMC7651905 · DOI
  28. [29]Devaraj, S., Sharma, S. K., Fausto, D. J., Viernes, S. & Kharrazi, H. Barriers and facilitators to clinical decision support systems adoption: a systematic review. J. Bus. Adm. Res. 3, 36 (2014).
  29. [30]Westerbeek, L. et al. Barriers and facilitators influencing medication-related CDSS acceptance according to clinicians: a systematic review. Int. J. Med. Inform. 152, 104506 (2021). 34091146 · DOI
  30. [32]Braun, V., Clarke, V., Hayfield, N. & Terry, G. Thematic analysis. In Handbook of Research Methods in Health Social Sciences (ed. Liamputtong, P.) 843–860 (Springer Nature, 2019).

Cited by

Sorted by citations

Authors
Pak, A., Robertson, S. T., Nguyen, B. H., Hodgson, T., Sullivan, C., & Burton-Jones, A.
Affiliations
The University of Queensland (AU)
Corresponding author
Pak, Anton
ORCID
Venue
npj Digital Medicine
Publisher
Springer Science and Business Media LLC
Published
2025-12-06
Volume
8
Issue
1
Pages
781-781
ISSN
2398-6352 (ISSN-L)
DOI
10.1038/s41746-025-02141-9
PMC
PMC12739111
OpenAlex
W4417115298
Cited by
3
References
2
Citations by year
2026: 3
FWCI
2.36
Type
journal article
Language
en
Open access
Licence
Locations
  • npj Digital Medicine (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Australian Research Council
Topics
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
Keywords
clinical decision support systems, metabolic emergencies, electronic health records, care planning, length of hospital stay, in-hospital mortality, guideline adherence, acute care

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