Applying sequence analysis to uncover ‘real-world’ clinical pathways from routinely collected data: a systematic review

Mathew, Smitha; Peat, George; Parry, Emma; Singh Sokhal, Balamrit; Yu, Dahai

Abstract

Objective

This systematic review aims to elucidate the methodological practices and reporting standards associated with sequence analysis (SA) for the identification of clinical pathways in real-world scenarios, using routinely collected data.
Study design and setting

We conducted a methodological systematic review, searching five medical and health databases: MEDLINE, PsycINFO, CINAHL, EMBASE and Web of Science. The search encompassed articles from the inception of these databases up to February 28, 2023. The search strategy comprised two distinctive sets of search terms, specifically focused on sequence analysis and clinical pathways.
Results

19 studies met the eligibility criteria for this systematic review. Nearly 60% of the included studies were published in or after 2021, with a significant proportion originating from Canada (n=7) and France (n=5). 90% of the studies adhered to the fundamental SA steps. The optimal matching (OM) method emerged as the most frequently employed dissimilarity measure (63%), while agglomerative hierarchical clustering using Ward’s linkage was the preferred clustering algorithm (53%). However, it is imperative to underline that a majority of the studies inadequately reported key methodological decisions pertaining to SA.
Conclusion

This review underscores the necessity for enhanced transparency in reporting both data management procedures and key methodological choices within SA processes. The development of reporting guidelines and a robust appraisal tool tailored to assess the quality of SA would be invaluable for researchers in this field.

Citation

Mathew, S., Peat, G., Parry, E., Singh Sokhal, B., & Yu, D. (2024). Applying sequence analysis to uncover ‘real-world’ clinical pathways from routinely collected data: a systematic review. Journal of Clinical Epidemiology, 111226. https://doi.org/10.1016/j.jclinepi.2023.111226

• Journal Article Type: Article
• Acceptance Date: Nov 27, 2023
• Online Publication Date: Nov 28, 2023
• Publication Date: 2024-02
• Deposit Date: Dec 10, 2023
• Journal: Journal of Clinical Epidemiology
• Print ISSN: 0895-4356
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Article Number: 111226
• Pages: 111226
• DOI: https://doi.org/10.1016/j.jclinepi.2023.111226
• Keywords: care trajectories, optimal matching, sequence analysis, cost setting, clinical pathways, cluster analysis
• Additional Information: This article is maintained by: Elsevier; Article Title: Applying Sequence Analysis to Uncover 'Real-World' Clinical Pathways from Routinely Collected Data: A Systematic Review; Journal Title: Journal of Clinical Epidemiology; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.jclinepi.2023.111226; Content Type: article; Copyright: © 2023 The Author(s). Published by Elsevier Inc.