Using Natural Language Processing in the LACE Index Scoring Tool to Predict Unplanned Trauma and Surgical Readmissions in South Africa.

IF 2.3 3区医学 Q2 SURGERY

World Journal of Surgery Pub Date : 2025-03-09 DOI:10.1002/wjs.12523

Umit Tokac, Jennifer Chipps, Petra Brysiewicz, John Bruce, Damian Clarke

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引用次数: 0

Abstract

Background: Unplanned and potentially avoidable readmission within 30 days post discharge is a major financial burden.

Aim: To use text-based electronic patient records to calculate the Charlson Comorbidity Index (CCI) score using a natural language processing technique to establish the feasibility and usefulness of the text-based electronic patient records in identifying patients at risk for unplanned readmission.

Methods: A retrospective review of electronic patient records for general and trauma surgery in a hospital in South Africa (2012-2022) was conducted using the LACE score. Validated sentiment analysis analyzed free text components of electronic patient records to compute the CCI score and to establish the feasibility and usefulness of the LACE score in identifying patients at risk for unplanned readmission.

Results: Trauma surgery patients had a mean LACE score of 5.91 (SD = 2.41), with 8.44% scoring 10 or higher and a specificity and sensitivity of 91.63% and 13.81%, respectively. The general surgery patients had a mean LACE score of 7.75 (SD = 3.04), with 10.63% scoring 10 or higher and a specificity of 71.47% and a sensitivity of 44.80%, respectively. Logistic regression analysis revealed that LACE scores significantly predicted unplanned readmissions in both trauma (β = 0.11, p < 0.001; OR = 1.112, 95% CI [1.082, 1.143]) and general surgery (β = 0.15, p < 0.001; OR = 1.162, 95% CI [1.130, 1.162]) patients.

Conclusion: The LACE score demonstrated the predictive value for readmission in trauma and general surgery patients. The LACE score was relatively effective in identifying patients who were less likely to be readmitted but showed limitations in identifying patients at higher risk of readmission. However, the successful use of natural language processing for data extraction of comorbidities shows promise on addressing the challenges around text-based medical records.

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来源期刊

World Journal of Surgery 医学-外科

CiteScore

5.10

自引率

3.80%

发文量

460

审稿时长

3 months

期刊介绍： World Journal of Surgery is the official publication of the International Society of Surgery/Societe Internationale de Chirurgie (iss-sic.com). Under the editorship of Dr. Julie Ann Sosa, World Journal of Surgery provides an in-depth, international forum for the most authoritative information on major clinical problems in the fields of clinical and experimental surgery, surgical education, and socioeconomic aspects of surgical care. Contributions are reviewed and selected by a group of distinguished surgeons from across the world who make up the Editorial Board.