Development and validation of a machine learning model to improve precision prediction for irrational prescriptions in orthopedic perioperative patients

ABSTRACT

Objective

Our objective was to develop a machine learning model capable of predicting irrational medical prescriptions precisely within orthopedic perioperative patients.

Methods

A dataset comprising 3047 instances of suspected irrational medication prescriptions was collected from a sample of 1318 orthopedic perioperative patients from April 2019 to March 2022. Four machine learning models were employed to forecast irrational prescriptions, following which, the performance of each model was meticulously assessed. Subsequently, a thorough variable importance analysis was conducted on the model that performed the best predictive capabilities. Thereafter, the efficacy of integrating this optimal model into the existing audit prescription process was rigorously evaluated.

Results

Of the models utilized in this study, the RF model yielded the highest AUC of 92%, whereas the NB model presented the lowest AUC of 68%. Also, the RF model boasted the most robust performance in terms of PPV, reaching 82.4%, and NPV, reaching 86.6%. The ANN and the XGBoost model were neck and neck, with the ANN slightly edging out with a higher PPV of 95.9%, while the XGBoost model boasted an impressive NPV of 98.2%. The RF model singled out the following five factors as the most influential in predicting irrational prescriptions: the type of drug, the type of surgery, the number of comorbidities, the date of surgery after hospitalization, as well as the associated hospital and drug costs.

Conclusion

The RF model showcased significantly high level of proficiency in predicting irrational prescriptions among orthopedic perioperative patients, outperforming other models by a considerable margin. It effectively enhanced the efficiency of pharmacist interventions, displaying outstanding performance in assisting pharmacists to intervene with irrational prescriptions.

GRAPHICAL ABSTRACT

KEYWORDS:

• Orthopedics
• perioperative period
• machine learning
• random forest
• irrational prescription

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Author contributions

W Li: Conceptualization, Methodology, Writing – original draft, Software, Data curation, Visualization, N Shang: Funding acquisition, Project administration, Z Zhang: Resources, Y Li: Validation, X Li: Data curation, X Zheng: Conceptualization, Resources.

All authors have approved the final manuscript.

Supplementary materials

Supplemental data for this article can be accessed online at https://doi.org/10.1080/14740338.2024.2348569

Additional information

Funding

This research was funded by the Clinical Research Special Fund Project of Wu Jieping Foundation [Grant No. 320.6750.2021-8-13].