From transactional logs to clinical bundles: Applying Apriori to neurology outpatient orders

Damla Su Karadoğan; Vecdi Emre Levent

doi:10.35860/iarej.1878439

From transactional logs to clinical bundles: Applying Apriori to neurology outpatient orders

Abstract

Private hospitals face rising operating costs while being expected to provide timely, high-quality diagnostic services. In outpatient neurology, diagnostic workups frequently combine laboratory panels and neuroimaging, and incomplete completion of ordered tests can delay diagnosis and reduce operational efficiency. This retrospective single-center case study applied association rule mining to 14,000 diagnostic test-order line items spanning 145 distinct tests, extracted over five months in 2025 from a private hospital in Konya, Türkiye and aggregated into de-identified encounter-level baskets. Apriori was applied with a minimum support of 0.01 and a minimum confidence of 0.30. Post-processing retained one-to-one rules and removed symmetric duplicates, reducing 124,039 raw rules to 116 candidates for clinical review. A neurology specialist grouped clinically coherent items into a 20-test HIS order set. At baseline, 1,445 of 2,408 diagnostic orders were completed (60.01%; Wilson 95% CI, 58.04%–61.95%); during the seven-month follow-up, 1,996 of 2,683 orders were completed (74.39%; 95% CI, 72.71%–76.01%). This was an absolute increase of 14.39 percentage points (Newcombe-Wilson 95% CI, 11.82–16.93) and a 23.97% relative increase (RR, 1.240; 95% CI, 1.192–1.290). The baseline and follow-up daily series comprised 114 and 144 active clinic days. Diagnostic-order volume decreased from 21.12 ± 9.15 to 18.63 ± 9.14 orders/day (mean difference, -2.49; 95% CI, -4.75 to -0.23; p=.031), while completed tests/day calculated from the aggregate counts increased from 12.68 to 13.86. Fewer tests were ordered, but a larger proportion was completed; this pattern is compatible with more selective ordering, although the uncontrolled design is descriptive and non-causal. Association rule mining combined with clinical governance may support locally tailored order-set design and monitoring.

Keywords

References

1. Wright, A. and D.F. Sittig, Automated development of order sets and corollary orders by data mining in an ambulatory computerized physician order entry system. AMIA Annual Symposium Proceedings, 2006. p. 819–823.
2. Chan, A.J., et al., Order sets in health care: A systematic review of their effects. International Journal of Technology Assessment in Health Care, 2012. 28(3): p. 235–240.
3. Weemaes, M., et al., Implementation of a care pathway based computerized order entry system streamlines test ordering and offers tools for benchmarking clinical practice. Informatics in Medicine Unlocked, 2023. 41: p. 101308.
4. Koh, H.C. and G. Tan, Data mining applications in healthcare. Journal of Healthcare Information Management, 2005. 19(2): p. 64–72.
5. Islam, M.S., et al., A systematic review on healthcare analytics: Application and theoretical perspective of data mining. Healthcare, 2018. 6(2): p. 54.
6. Fayyad, U., G. Piatetsky-Shapiro, and P. Smyth, From data mining to knowledge discovery in databases. AI Magazine, 1996. 17(3): p. 37–54.
7. Agrawal, R., T. Imieliński, and A. Swami, Mining association rules between sets of items in large databases, in Proc. ACM SIGMOD International Conference on Management of Data. 1993. p. 207–216.
8. Agrawal, R. and R. Srikant, Fast algorithms for mining association rules, in Proc. 20th International Conference on Very Large Data Bases (VLDB). 1994. p. 487–499.

9. Ahmad, P., S. Qamar, and S.Q.A. Rizvi, Techniques of data mining in healthcare: A review. International Journal of Computer Applications, 2015. 120(15): p. 38–50.
10. Ordonez, C., Association rule discovery with the train and test approach for heart disease prediction. IEEE Transactions on Information Technology in Biomedicine, 2006. 10(2): p. 334–343.
11. Wright, A., et al., Order sets in computerized physician order entry systems: An analysis of seven sites. AMIA Annual Symposium Proceedings, 2010. p. 892–896.
12. Wright, A., et al., Use of order sets in inpatient computerized provider order entry systems: A comparative analysis of usage patterns at seven sites. International Journal of Medical Informatics, 2012. 81(11): p. 733–745.
13. Wright, A., E.S. Chen, and F.L. Maloney, An automated technique for identifying associations between medications, laboratory results and problems. Journal of Biomedical Informatics, 2010. 43(6): p. 891–901.
14. Bates, D.W., et al., The impact of computerized physician order entry on medication error prevention. Journal of the American Medical Informatics Association, 1999. 6(4): p. 313–321.
15. Koppel, R., et al., Role of computerized physician order entry systems in facilitating medication errors. JAMA, 2005. 293(10): p. 1197–1203.
16. Schiff, G.D., et al., Computerised physician order entry-related medication errors: Analysis of reported errors and vulnerability testing of current systems. BMJ Quality & Safety, 2015. 24(4): p. 264–271.
17. Claeys, K.C., K. Coffey, and D.J. Morgan, What is diagnostic stewardship? The Journal of Applied Laboratory Medicine, 2025. 10(1): p. 130–139.
18. Schinas, G., G. Dimopoulos, and K. Akinosoglou, Understanding and implementing diagnostic stewardship: A guide for resident physicians in the era of antimicrobial resistance. Microorganisms, 2023. 11(9): p. 2214.
19. Fabre, V., et al., Principles of diagnostic stewardship: A practical guide from the Society for Healthcare Epidemiology of America Diagnostic Stewardship Task Force. Infection Control & Hospital Epidemiology, 2023. 44(2): p. 178–185.
20. Müskens, J.L.J.M., et al., Overuse of diagnostic testing in healthcare: A systematic review. BMJ Quality & Safety, 2022. 31(1): p. 54–63.
21. Vrijsen, B.E.L., et al., Inappropriate laboratory testing in internal medicine inpatients: Prevalence, causes and interventions. Annals of Medicine and Surgery, 2020. 51: p. 48–53.
22. White, T.E., et al., Strategies for laboratory professionals to drive laboratory stewardship. Practical Laboratory Medicine, 2021. 26: p. e00249.
23. Feldman, L.S., et al., Impact of providing fee data on laboratory test ordering: A controlled clinical trial. JAMA Internal Medicine, 2013. 173(10): p. 903–908.
24. Silvestri, M.T., et al., Impact of cost display on ordering patterns for hospital laboratory and imaging services. Journal of General Internal Medicine, 2018. 33(8): p. 1268–1275.
25. Muris, D.M.J., et al., Effect of a price display intervention on laboratory test ordering behavior of general practitioners. BMC Family Practice, 2021. 22(1): Article 242.
26. Sarıyer, G. and C. Öcal Taşar, Highlighting the rules between diagnosis types and laboratory diagnostic tests for patients of an emergency department: Use of association rule mining. Health Informatics Journal, 2020. 26(2): p. 1177–1193.
27. Dassiou, X., D. Glycopantis, and C. Stavropoulou, Bundling in general markets and in health care systems. Journal of Economic Asymmetries, 2009. 6(2): p. 47–68.
28. Shih, T., L.M. Chen, and B.K. Nallamothu, Will bundled payments change health care? Examining the evidence thus far in cardiovascular care. Circulation, 2015. 131(24): p. 2151–2158.
29. Sirur, A.J.N. and R. Pillai K, Pricing of hospital services: Evidence from a thematic review. Health Economics, Policy and Law, 2024. 19(2): p. 234–252.
30. Benchimol, E.I., et al., The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. PLoS Medicine, 2015. 12(10): p. e1001885.
31. von Elm, E., et al., The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies. PLoS Medicine, 2007. 4(10): p. e296.
32. McKinney, W., Data structures for statistical computing in Python, in Proc. 9th Python in Science Conference (SciPy). 2010. p. 56–61.
33. Harris, C.R., et al., Array programming with NumPy. Nature, 2020. 585(7825): p. 357–362.
34. Raschka, S., MLxtend: Providing machine learning and data science utilities and extensions to Python's scientific computing stack. Journal of Open Source Software, 2018. 3(24): p. 638.
35. Newcombe, R.G., Interval estimation for the difference between independent proportions: Comparison of eleven methods. Statistics in Medicine, 1998. 17(8): p. 873–890.
36. Bernal, J.L., S. Cummins, and A. Gasparrini, Interrupted time series regression for the evaluation of public health interventions: A tutorial. International Journal of Epidemiology, 2017. 46(1): p. 348–355.

Details

Primary Language

English

Subjects

Computer Software

Journal Section

Research Article

Authors

Damla Su Karadoğan
0000-0001-7325-1626
Türkiye

Vecdi Emre Levent ^*
0000-0001-6886-8875
Türkiye

Publication Date

August 25, 2026

Submission Date

February 6, 2026

Acceptance Date

August 19, 2026

Published in Issue

Year 2026 Volume: 10 Number: 2

DOI

https://doi.org/10.35860/iarej.1878439

IZ

https://izlik.org/JA27YH22HS

Cite

RIS / Bibtex

APA

Karadoğan, D. S., & Levent, V. E. (2026). From transactional logs to clinical bundles: Applying Apriori to neurology outpatient orders. International Advanced Researches and Engineering Journal, 10(2), 158-165. https://doi.org/10.35860/iarej.1878439

AMA

1.Karadoğan DS, Levent VE. From transactional logs to clinical bundles: Applying Apriori to neurology outpatient orders. Int. Adv. Res. Eng. J. 2026;10(2):158-165. doi:10.35860/iarej.1878439

Chicago

Karadoğan, Damla Su, and Vecdi Emre Levent. 2026. “From Transactional Logs to Clinical Bundles: Applying Apriori to Neurology Outpatient Orders”. International Advanced Researches and Engineering Journal 10 (2): 158-65. https://doi.org/10.35860/iarej.1878439.

EndNote

Karadoğan DS, Levent VE (August 1, 2026) From transactional logs to clinical bundles: Applying Apriori to neurology outpatient orders. International Advanced Researches and Engineering Journal 10 2 158–165.

IEEE

[1]D. S. Karadoğan and V. E. Levent, “From transactional logs to clinical bundles: Applying Apriori to neurology outpatient orders”, Int. Adv. Res. Eng. J., vol. 10, no. 2, pp. 158–165, Aug. 2026, doi: 10.35860/iarej.1878439.

ISNAD

Karadoğan, Damla Su - Levent, Vecdi Emre. “From Transactional Logs to Clinical Bundles: Applying Apriori to Neurology Outpatient Orders”. International Advanced Researches and Engineering Journal 10/2 (August 1, 2026): 158-165. https://doi.org/10.35860/iarej.1878439.

JAMA

1.Karadoğan DS, Levent VE. From transactional logs to clinical bundles: Applying Apriori to neurology outpatient orders. Int. Adv. Res. Eng. J. 2026;10:158–165.

MLA

Karadoğan, Damla Su, and Vecdi Emre Levent. “From Transactional Logs to Clinical Bundles: Applying Apriori to Neurology Outpatient Orders”. International Advanced Researches and Engineering Journal, vol. 10, no. 2, Aug. 2026, pp. 158-65, doi:10.35860/iarej.1878439.

Vancouver

1.Damla Su Karadoğan, Vecdi Emre Levent. From transactional logs to clinical bundles: Applying Apriori to neurology outpatient orders. Int. Adv. Res. Eng. J. 2026 Aug. 1;10(2):158-65. doi:10.35860/iarej.1878439