Predictive Optimization of Patient No-Show Management in Primary Healthcare Using Machine Learning.
The "no-show" problem in healthcare refers to the prevalent phenomenon where patients schedule appointments with healthcare providers but fail to attend them without prior cancellation or rescheduling. In addressing this issue, our study delves into a multivariate analysis over a five-year period in...
| Published in: | Journal of Medical Systems Vol. 49; no. 1; pp. 1 - 27 |
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| Main Authors: | , , , |
| Format: | equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
1/14/2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=183076904&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183076904 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 1/14/2025 vid: 49 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 183076904 183076904 183076904 10.1007/s10916-025-02143-w 183076904 ppf: 1 ppct: 26 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Predictive Optimization of Patient No-Show Management in Primary Healthcare Using Machine Learning. aug: au: Leiva-Araos, Andrés Contreras, Cristián Kaushal, Hemani Prodanoff, Zornitza affil: https://ror.org/01j903a45 Department of Computing, University of North Florida, 1 UNF Dr., 32246, Jacksonville, FL, USA sug: subj: Primary Health Care Appointments and Schedules Patient Compliance Evaluation Appointment and Scheduling Information Systems Machine Learning Prediction Models Evaluation Human Multivariate Analysis Conceptual Framework Organizational Efficiency ROC Curve Sensitivity and Specificity Educational Status Marital Status Health Literacy Time Age Factors Pearson's Correlation Coefficient Chi Square Test Logistic Regression Kruskal-Wallis Test Analysis of Variance ab: The "no-show" problem in healthcare refers to the prevalent phenomenon where patients schedule appointments with healthcare providers but fail to attend them without prior cancellation or rescheduling. In addressing this issue, our study delves into a multivariate analysis over a five-year period involving 21,969 patients. Our study introduces a predictive model framework that offers a holistic approach to managing the no-show problem in healthcare, incorporating elements into the objective function that address not only the accurate prediction of no-shows but also the management of service capacity, overbooking, and idle resource allocation resulting from mispredictions. Our approach simplifies preprocessing and eliminates the need for expert judgment in variable selection, thereby enhancing the model's usability in routine healthcare operations. Our research revealed that key predictors of no-shows are consistent across various studies. We employed semi-automatic feature selection techniques, achieving results comparable to state-of-the-art approaches but with significantly reduced complexity in their selection. This method not only streamlines the feature selection process but also enhances the overall efficiency and scalability of our predictive models, making them more adaptable to diverse healthcare settings. This comprehensive strategy enables healthcare providers to optimize resource allocation and improve service delivery, making our findings relevant for healthcare systems globally facing similar challenges. Future work aims to expand the analysis by incorporating additional third-party data sources, such as weather and commuting activities, to explore the broader impacts of external factors on patient no-show behavior. To the best of our knowledge, this innovative approach is expected to provide deeper insights and further enhance the predictability and effectiveness of no-show mitigation strategies in healthcare systems. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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