Approaches to extracting patterns of service utilization for patients with complex conditions: graph community detection vs. natural language processing clustering
Date
2024
Authors
Bambi, Jonas
Sadri, Hanieh
Moselle, Ken
Chang, Ernie
Santoso, Yudi
Howie, Joseph
Rudnick, Abraham
Elliott, Lloyd T.
Kuo, Alex
Journal Title
Journal ISSN
Volume Title
Publisher
BioMedInformatics
Abstract
Background: As patients interact with a healthcare service system, patterns of service utilization (PSUs) emerge. These PSUs are embedded in the sparse high-dimensional space of longitudinal cross-continuum health service encounter data. Once extracted, PSUs can provide quality assurance/quality improvement (QA/QI) efforts with the information required to optimize service system structures and functions. This may improve outcomes for complex patients with chronic diseases. Method: Working with longitudinal cross-continuum encounter data from a regional health service system, various pattern detection analyses were conducted, employing (1) graph community detection algorithms, (2) natural language processing (NLP) clustering, and (3) a hybrid NLP–graph method. Result: These approaches produced similar PSUs, as determined from a clinical perspective by clinical subject matter experts and service system operations experts. Conclusions: The similarity in the results provides validation for the methodologies. Moreover, the results stress the need to engage with clinical or service system operations experts, both in providing the taxonomies and ontologies of the service system, the cohort definitions, and determining the level of granularity that produces the most clinically meaningful results. Finally, the uniqueness of each approach provides an opportunity to take advantage of the various analytical capabilities that each approach brings, which will be further explored in our future research.
Description
Keywords
clinical pathways, clinical practice guideline, clustering, decision support, electronic healthcare, graph community detection, health information management, health service system, machine learning algorithms, natural language processing
Citation
Bambi, J., Sadri, H., Moselle, K., Chang, E., Santoso, Y., Howie, J., Rudnick, A., Elliott, L. T., & Kuo, A. (2024). Approaches to extracting patterns of service utilization for patients with complex conditions: Graph community detection vs. natural language processing clustering. BioMedInformatics, 4(3), Article 3. https://doi.org/10.3390/biomedinformatics4030103