Approaches to extracting patterns of service utilization for patients with complex conditions: graph community detection vs. natural language processing clustering

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dc.contributor.authorBambi, Jonas
dc.contributor.authorSadri, Hanieh
dc.contributor.authorMoselle, Ken
dc.contributor.authorChang, Ernie
dc.contributor.authorSantoso, Yudi
dc.contributor.authorHowie, Joseph
dc.contributor.authorRudnick, Abraham
dc.contributor.authorElliott, Lloyd T.
dc.contributor.authorKuo, Alex
dc.date.accessioned2024-10-10T17:23:08Z
dc.date.available2024-10-10T17:23:08Z
dc.date.issued2024
dc.description.abstractBackground: 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.
dc.description.reviewstatusReviewed
dc.description.scholarlevelFaculty
dc.identifier.citationBambi, 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
dc.identifier.urihttps://doi.org/10.3390/biomedinformatics4030103
dc.identifier.urihttps://hdl.handle.net/1828/20511
dc.language.isoen
dc.publisherBioMedInformatics
dc.rightsAttribution CC BY
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectclinical pathways
dc.subjectclinical practice guideline
dc.subjectclustering
dc.subjectdecision support
dc.subjectelectronic healthcare
dc.subjectgraph community detection
dc.subjecthealth information management
dc.subjecthealth service system
dc.subjectmachine learning algorithms
dc.subjectnatural language processing
dc.subject.departmentDepartment of Computer Science
dc.subject.departmentDepartment of Psychology
dc.subject.departmentSchool of Health Information Science
dc.titleApproaches to extracting patterns of service utilization for patients with complex conditions: graph community detection vs. natural language processing clustering
dc.typeArticle

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