A methodological approach to extracting patterns of service utilization from a cross-continuum high dimensional healthcare dataset to support care delivery optimization for patients with complex problems

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dc.contributor.authorBambi, Jonas
dc.contributor.authorSantoso, Yudi
dc.contributor.authorSadri, Hanieh
dc.contributor.authorMoselle, Ken
dc.contributor.authorRudnick, Abraham
dc.contributor.authorRobertson, Stan
dc.contributor.authorChang, Ernie
dc.contributor.authorKuo, Alex
dc.contributor.authorHowie, Joseph
dc.contributor.authorDong, Gracia Yunruo
dc.contributor.authorOlobatuyi, Kehinde
dc.contributor.authorHajiabadi, Mahdi
dc.contributor.authorRichardson, Ashlin
dc.date.accessioned2024-10-10T17:23:10Z
dc.date.available2024-10-10T17:23:10Z
dc.date.issued2024
dc.description.abstractBackground: Optimizing care for patients with complex problems entails the integration of clinically appropriate problem-specific clinical protocols, and the optimization of service-system-encompassing clinical pathways. However, alignment of service system operations with Clinical Practice Guidelines (CPGs) is far more challenging than the time-bounded alignment of procedures with protocols. This is due to the challenge of identifying longitudinal patterns of service utilization in the cross-continuum data to assess adherence to the CPGs. Method: This paper proposes a new methodology for identifying patients’ patterns of service utilization (PSUs) within sparse high-dimensional cross-continuum health datasets using graph community detection. Result: The result has shown that by using iterative graph community detections, and graph metrics combined with input from clinical and operational subject matter experts, it is possible to extract meaningful functionally integrated PSUs. Conclusions: This introduces the possibility of influencing the reorganization of some services to provide better care for patients with complex problems. Additionally, this introduces a novel analytical framework relying on patients’ service pathways as a foundation to generate the basic entities required to evaluate conformance of interventions to cohort-specific clinical practice guidelines, which will be further explored in our future research.
dc.description.reviewstatusReviewed
dc.description.scholarlevelFaculty
dc.identifier.citationBambi, J., Santoso, Y., Sadri, H., Moselle, K., Rudnick, A., Robertson, S., Chang, E., Kuo, A., Howie, J., Dong, G. Y., Olobatuyi, K., Hajiabadi, M., & Richardson, A. (2024). A methodological approach to extracting patterns of service utilization from a cross-continuum high dimensional healthcare dataset to support care delivery optimization for patients with complex problems. BioMedInformatics, 4(2), Article 2. https://doi.org/10.3390/biomedinformatics4020053
dc.identifier.urihttps://doi.org/10.3390/biomedinformatics4020053
dc.identifier.urihttps://hdl.handle.net/1828/20540
dc.language.isoen
dc.publisherBioMedInformatics
dc.rightsAttribution CC BY
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectclinical pathways
dc.subjectclinical practice guidelines
dc.subjectdecision support
dc.subjectgraph community detection
dc.subjecthealth information management
dc.subjecthealth service system
dc.subjectLouvain algorithm
dc.subjectmachine learning algorithms
dc.subject.departmentDepartment of Computer Science
dc.subject.departmentSchool of Health Information Science
dc.subject.departmentDepartment of Psychology
dc.subject.departmentDepartment of Mathematics and Statistics
dc.titleA methodological approach to extracting patterns of service utilization from a cross-continuum high dimensional healthcare dataset to support care delivery optimization for patients with complex problems
dc.typeArticle

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